esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Microstructure, Element Composition, and Phase  
After the deposition, a thin piece of the cross-section area of the single wall was sectioned by wire electric discharge machining (EDM). The build direction (BD) is from the bottom to the top. In order to analyze the material properties at certain locations, five specific heights were marked as H1, H2, H3, H4, and H5 along the BD. The detailed information of the specific locations of the single wall cross-section is illustrated in Figure 3. H1 is a near-bottom location that is 2 mm above the bottom of the TiNiCu single wall, while H5 is a near-top location with a distance of 16 mm from the bottom. H2, H3, and H4 equally divide the cross-section from H1 to H5 into four sections. At the region of each specific height, the chemical composition and phase were studied to reveal the differences at various locations.  
The cross-section was mounted, ground, and polished using a Buehler EcoMet 250 grinder/polisher. The well-polished cross-section of the single wall was analyzed using an FEI Helios NanoLab 600 Dual-Beam SEM equipped with Oxford Energy Dispersive Spectrometer. Both large area and small area energy dispersive spectroscopy (EDS) analyses were carried out within the cross-section area of the single wall. The large area EDS analysis was conducted at all five height locations with a rough area of 1 mm\({}^{2}\). Specific small areas were selected within various phases for the EDS spectrum analysis to determine  
Figure 2: SEM images of elemental powders used in this work: (**a**) Ti; (**b**) Ni; (**c**) Cu.  
the composition of Ti, Ni, and Cu. The X-ray diffraction (XRD) technique was used to characterize the crystal structure. All five locations were analyzed by an X'Pert X-ray diffractometer with a step size of 0.03\({}^{\circ}\).
# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
## 3 Multi-Stage Probabilistic Model
### Coal-Fired Unit Multi-State Probabilistic Model
#### 3.2.2 Calculation of Unit Steady-State Probabilities  
For the three multi-state unit models described above, the steady-state probabilities of each state are determined using the Markov process state transition equations:  
\[\left\{\begin{array}{l}(p_{1},p_{2},\ldots,p_{n})\cdot A=0\\ \sum_{k=1}^{n}p_{k}=1\end{array}\right. \tag{15}\]  
where \(p_{k}\) represents the steady-state probability of the \(k\)-th state of the unit, and \(A\) is the state transition matrix of the coal-fired unit.  
Taking Model 2 as an example, suppose it is the \(i\)-th operational unit, with the corresponding load at time \(t\) being L(\(t\)). The steady-state probabilities for the states in the state space to be solved are \(p_{k}\). According to Figure 3, the state transition matrix A for unit \(i\) is  
Figure 4: Start–stop load-following state-space model.  
\[\begin{array}{l}A=\\ \left(\begin{array}{cccc}-\frac{k}{n=2}\frac{\rho_{i}^{+}(1,\mathrm{n})}{TR_{1 \sim n}^{i}}-u_{1}&\frac{\rho_{i}^{+}(1,2)}{TR_{1\sim 2}^{i}}&\frac{\rho_{i}^{+}(1,3)}{TR_{1\sim 3 }^{i}}&\cdots&\frac{\rho_{i}^{+}(1,\mathrm{k})}{TR_{1\sim k}^{i}}&u_{1}\\ \frac{\rho_{i}^{-}(2,1)}{TR_{2\sim 1}^{i}}&-\frac{\rho_{i}^{-}(2,1)}{TR_{2\sim 1}^{i}} -\frac{k}{n=3}\frac{\rho_{i}^{+}(2,\mathrm{n})}{TR_{1\sim n}^{i}}-u_{2}&\frac{ \rho_{i}^{+}(2,3)}{TR_{2\sim 3}^{i}}&\cdots&\frac{\rho_{i}^{+}(2,\mathrm{k})}{TR_{2\sim k }^{i}}&u_{2}\\ \frac{\rho_{i}^{-}(3,1)}{TR_{3\sim 1}^{i}}&\frac{\rho_{i}^{-}(3,2)}{TR_{3\sim 2 }^{i}}&-\sum\limits_{j=1}^{2}\frac{\rho_{i}^{-}(3,i)}{TR_{3\sim j}^{i}}-\sum \limits_{n=4}^{k}\frac{\rho_{i}^{+}(3,\mathrm{n})}{TR_{1\sim n}^{i}}-u_{3}& \cdots&\frac{\rho_{i}^{+}(3,\mathrm{k})}{TR_{3\sim k}^{i}}&u_{3}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ \frac{\rho_{i}^{-}(\mathrm{k},1)}{TR_{k\sim 1}^{i}}&\frac{\rho_{i}^{-}( \mathrm{k},2)}{TR_{k\sim 2}^{i}}&\frac{\rho_{i}^{-}(\mathrm{k},\mathrm{k}-1)}{TR_{k \sim(k-1)}^{i}}&\cdots&-\sum\limits_{j=1}^{k-1}\frac{\rho_{i}^{-}(\mathrm{k}, \mathrm{j})}{TR_{k\sim j}^{i}}&u_{k}\\ \lambda&\lambda&\lambda&\cdots&\lambda&-k\lambda\end{array}\right) \tag{16}\]  
In this equation, the off-diagonal elements \(a_{ij}\) of matrix A represent the transition rate from state \(i\) to state \(j\), while the diagonal elements \(a_{ii}\) represent the negative of the exit rate from state \(i\). Eventually, the steady-state probability of the unit state can be obtained after repeated amendments.
# Enhancement of Wheat Flour and Dough Properties by Non-Thermal Plasma Treatment of Wheat Flour
###### Abstract  
Demand to improve food quality attributes without the use of chemicals has risen exponentially in the past few years. Non-thermal plasma (NTP) (also called 'cold plasma') is becoming increasingly popular for this purpose due to its unique low-temperature and non-chemical nature. In the present research, the concept of in situ dielectric barrier discharge (DBD) plasma treatment inside a rotational reactor for the direct treatment of wheat flour was experimentally analyzed. The primary research goal was to determine the effects of short-period NTP treatment of DBD type on flour and dough properties. For this purpose, the influence of different operating parameters was tested, i.e., treatment time, the amount of flour placed in the reactor and the environmental (air) temperature. Changes in the structural attributes of the most commonly used flours (type 550 and 1050) and their respective doughs were studied using a set of analytical techniques. Rheological analysis demonstrated the ability of NTP to significantly intensify the visco-elastic properties of dough produced from wheat flour type 550 that was treated for less than 180 s. This indicated that plasma treatment enhanced intermolecular disulphide bonds in gluten proteins, which resulted in stronger protein-starch network formations. However, longer treatment times did not result in a significant increase in the visco-elastic properties of wheat dough. The obtained results showed a 6-7% increase in flour hydration due to NTP treatment, which also makes a contribution to hydrogen bonding due to changes in the bonded and free water phase. Experimental findings further confirmed the dependence of NTP treatment efficiency on environmental air temperature.  
wheat flour; dough; rheology; non-thermal plasma; dielectric barrier discharge; wheat functionality; hydration properties; microstructure 28 June 2022  
Accepted: 8 August 2022  
Submitted: 10 August 2022
## 4 Discussion
Further studies are required to evaluate the trueness using a various number of mill burs and a five-axial milling unit. In order to measure the trueness, the reference data must exist and the method of acquisition is very important. In previous studies [33], the trueness was analyzed by calculating the distance from the coordinates of the reference data using a contact type scanner. In other studies [1], the trueness was measured by acquiring the reference data using a precision industrial scanner. Contact scanners were more accurate and stable than optical scanners, and more efficient in reproducing the abutment margins than laser scanners [34]. In previous studies [35], the abutment was scanned with both an optical scanner and a contact scanner, and excellent reproducibility was obtained with a contact scanner than an optical scanner. The trueness of the outer surface of the crown was not evaluated in this study since the narrow, deep, and undercut regions were not contact-scannable. In addition, when separating the crown from the block, the external connections may affect trueness. However, further research is needed to analyze the trueness of the outer surface of the crown. In this study, the correlation between the trueness of the milling unit and the fitness showed that trueness and internal fit were correlated only in the in-lab (open type) group (\(p\) = 0.013) (Figure 9), while there was no correlation between the trueness and internal fit in the chairside (closed type) group (\(p\) > 0.05). The reason for this is that the trueness of the in-lab (open type) group was significantly poorer than the chairside (closed type) group (\(p\) < 0.001). Therefore, it can be assumed that the poor trueness of the milling unit adversely affects the internal fit.
# Assessment of Two Streamline Curvature Correction Methods for an Elliptic Blending Turbulence Model
## 2 Turbulence Modeling  
The set of transport equations for the proposed models is as follows:  
\[\frac{\partial\rho k}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i} k)=F_{1}G_{k}-\rho f_{k}\beta^{*}k\omega+\frac{\partial}{\partial x_{j}}\Bigg{[} \frac{\partial k}{\partial x_{j}}\Bigg{]}, \tag{1}\]  
\[\frac{\partial\rho\omega}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_ {i}\omega)=f_{\omega}\gamma\frac{\omega}{k}F_{1}G_{k}-F_{2}\rho\beta\omega^{2 }+C_{D}+\frac{\partial}{\partial x_{j}}\Bigg{[}\Big{(}\frac{\mu}{2}+\sigma_{ \omega}\mu_{t}\Big{)}\frac{\partial\omega}{\partial x_{j}}\Bigg{]}, \tag{2}\]  
\[\frac{\partial\rho\varphi}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u _{i}\varphi)=(1-\alpha^{p})\rho f_{wall}+\alpha^{p}\rho f_{hom}-\frac{\varphi }{k}F_{1}G_{k}+\frac{2}{k}\mu_{t}\sigma_{k}\frac{\partial\varphi}{\partial x_ {j}}+\frac{\partial}{\partial x_{j}}\Bigg{[}\Big{(}\frac{\mu}{2}+\sigma_{ \varphi}\mu_{t}\Big{)}\frac{\partial\varphi}{\partial x_{j}}\Bigg{]}, \tag{3}\]  
\[0=\frac{1-\alpha}{L^{2}}+\frac{\partial}{\partial x_{j}}\Bigg{(}\frac{\partial \alpha}{\partial x_{j}}\Bigg{)}. \tag{4}\]Compared with the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model, the curvature sensitization functions \(F_{1}\) and \(F_{2}\) are introduced in the proposed models. For more information about the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model, please refer to [PERSON] et al. [16]. Two methods for sensitization of the original SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model to streamline the curvature effect are provided in this paper. The first one (hereafter denoted as the SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC1 Model) is based on the work of [PERSON] [12]. In this method, \(F_{1}=1\) and the other sensitization function, \(F_{2}\), is introduced to modify the dissipation term of the \(\omega\)-equation. It is described as follows:  
\[F_{2}=\frac{1}{1+C_{rc}R_{i}}, \tag{5}\]  
where \(C_{rc}=2.8\) and \(R_{i}\) is the Richardson number, defined as  
\[R_{i}=\frac{\Omega}{S}\left(\frac{\Omega}{S}-1\right), \tag{6}\]  
where \(\Omega\) and \(S\) are the magnitudes of the vorticity tensor and shear strain tensor, respectively. It can be seen that, for canonical flows such as the channel flow and the flat plate turbulent boundary layer flow, where \(\Omega\ \approx\ S\) and \(F_{2}\approx 1\), this model has scant impact. The second method (hereafter denoted as the SST \(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-CC2 Model) starts from the explicit algebraic stress model of [PERSON] [8].
## 2 Materials and Methods
### Infrastructure and Functional Components  
The platform for which this architecture was developed is rather complex, with several levels and components. Some of those components will be briefly exposed to contextualize the reader with the challenges.
## 3 Methodology
### Elimination of Multicollinearity  
Collinearity among pairs of explanatory variables or multicollinearity among more than two variables in regression analysis is known to cause problems such as parameter estimate instability, unintuitive parameter signs, high coefficient of determination (R\({}^{2}\)) diagnostics despite few or no significant parameters, and others [28]. Due to the huge and complex landslide data, the complicated relationship between geological and topographical factors should be considered. Therefore, the multicollinearity elimination in landslide susceptibility assessment is very important. Correlations in GWR parameters, both within a set of local parameter estimates for all locations (global multicollinearity) and among different parameter estimates at each location (local multicollinearity), are the symptom of multicollinearity among explanatory variables [28].  
Global multicollinearity of the entire area can be easily distinguished by the variance inflation factor (VIF) method [29], which measures the effect of collinearity on the estimated variance of a regression coefficient. Local multicollinearity is the linear dependencies in the design matrix of local regression model. Principal component analysis (PCA) [30] is adopted to eliminate local multicollinearity since the diagnosis of local multicollinearity is very complicated. Through data transformation and processing, the influencing factors of landslide susceptibility can be grouped into less integrated factors, which not only maintains the main information of original factors, but also weakens the correlation among them. First obtained initial factor loading matrix. There are two main principles for selecting the principal component: (1) the principal component eigenvalue is greater than 1; (2) the cumulative contribution rate of principal components reaches 80%. Then, the factor rotation is performed so that the obtained factors have clear professional interpretation significance. Quartimax method [39], a common factor rotation method, was used for factor rotation.
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 3 Design
### Motivation  
However, due to the compatibility with the existing IP infrastructure, ICN packets are still transmitted in a best-effort approach employed by IP routing rules. With the increase  
Figure 1: The protocol stack of ICN.  
in the number of access devices, the congestion probability of the default path gradually increases. Once congestion or failure occurs, it often takes seconds or even minutes for routing protocols to converge to a normal state [39]. The end-to-end connections may experience outages for seconds or minutes during this process. Therefore, rigid IP single path routing has been unable to meet the demand of massive data distribution. A reasonable data transmission scheme is necessary to improve the transmission reliability and efficiency of such IP-compatible ICN architectures.  
With a complete protocol stack, the ICN routers can resolve an ID to network address(es) by initiating a query operation to the NRS. In addition, the ICN routers can also use the IDP to process the network address(es) of packets, which may include adding, deleting, and modifying the network address. Therefore, it is a feasible solution to utilize the ICN routers to act as relay nodes to enhance transmission. In addition, researchers have proven through experiments that the single-hop indirect path formed by one relay node could achieve significant gains in terms of round-trip delay, packet loss rate, and throughput [13]. Based on this finding, an ICN router can form a relay path between the sender and the receiver, so multiple ICN routers can be used for multipath transmission. In the next subsection, we will introduce the design of our proposed multipath transmission scheme in detail.
## 3 Results
### Results Acquired Using GPSR with RHS and 5-CV
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Case Number**} & \multirow{2}{*}{**Evaluation Metric**} & \multicolumn{4}{c}{**Symbolic Expressions**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{\(\sigma\)} \\ \cline{3-3} \cline{6-9}  & & **1** & **2** & **3** & **4** & **5** & \\ \hline \multirow{4}{*}{1} & \(R^{2}\) & 0.9827 & 0.9892 & 0.9811 & 0.9934 & 0.9905 & 0.9876 & 0.0048 \\ \cline{2-9}  & \(MAE\) & 1.1180 & 0.9919 & 1.1645 & 0.8225 & 0.9619 & 1.0096 & 0.1215 \\ \cline{2-9}  & \(RMSE\) & 1.8649 & 1.4646 & 1.9488 & 1.1537 & 1.3810 & 1.5506 & 0.3050 \\ \hline \multirow{4}{*}{2} & \(R^{2}\) & 0.9878 & 0.9896 & 0.9887 & 0.9836 & 0.9837 & 0.9867 & 0.0025 \\ \cline{2-9}  & \(MAE\) & 1.0615 & 0.9393 & 0.9894 & 1.1815 & 1.1450 & 1.0634 & 0.0911 \\ \cline{2-9}  & \(RMSE\) & 1.5664 & 1.4490 & 1.5057 & 1.8173 & 1.8116 & 1.6300 & 0.1551 \\ \hline \multirow{4}{*}{3} & \(R^{2}\) & 0.9909 & 0.9865 & 0.9889 & 0.9828 & 0.9873 & 0.9873 & 0.0027 \\ \cline{2-9}  & \(MAE\) & 0.8032 & 1.0518 & 1.0513 & 1.3162 & 1.0666 & 1.0578 & 0.1623 \\ \cline{2-9}  & \(RMSE\) & 1.3529 & 1.6464 & 1.4958 & 1.8588 & 1.5978 & 1.5903 & 0.1677 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance metric values obtained on the test dataset. Figure 7: The mean values of the performance metric obtained on the testing dataset. The \(\sigma\) values are presented as error bars.
# Accelerated Tensor Robust Principal Component Analysis via Factorized Tensor Norm Minimization
## 4 Proposed Algorithm
### Optimization Model  
Assume that we have a tensor \(\mathcal{X}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\), which can be expressed as the sum of a low-rank tensor and a sparse tensor such that \(\mathcal{X}=\mathcal{L}+\mathcal{S}\). If the low-rank component \(\mathcal{L}\) can be factorized as \(\mathcal{L}=\mathcal{A}*\mathcal{B}^{T}\), then solving the TRPCA problem defined in (3) is equivalent to finding the solution of the following optimization problem:  
\[\arg\min_{\mathcal{A}\mathcal{B}}\frac{1}{2n_{3}}\Big{(}\|\mathcal{A}\|_{F}^{2 }+\|\mathcal{B}\|_{F}^{2}\Big{)}+\lambda||\mathcal{S}||_{1},\text{ s.t. }\mathcal{X}=\mathcal{L}+\mathcal{S}, \tag{13}\]  
where \(\mathcal{A}\in\mathbb{R}^{n_{1}\times r\times n_{3}}\) and \(\mathcal{B}\in\mathbb{R}^{n_{2}\times r\times n_{3}}\). Due to the low-rank property of \(\mathcal{L}\), it satisfies that \(r\ll\min(n_{1},n_{2})\).  
**Theorem 2**: _Let \(\mathcal{L}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) be factorized as \(\mathcal{L}=\mathcal{A}*\mathcal{B}^{T}\). Then, the nuclear norm minimization problem of \(\mathcal{L}\) can be relaxed to the following optimization problem such that_  
\[\arg\min||L||_{*}\stackrel{{\text{relav}}}{{ \Longrightarrow}}\arg\min_{\mathcal{A}\mathcal{B}}\frac{1}{2n_{3}}(\| \mathcal{A}\|_{F}^{2}+\|\mathcal{B}\|_{F}^{2}). \tag{14}\]  
By Definition 6, the tensor nuclear norm is defined as  
\[||\mathcal{L}||_{*}=\frac{1}{n_{3}}\sum_{i=1}^{n_{3}}||\bar{L}(i)||_{*}. \tag{15}\]  
From [22], if we assume \(\bar{L}(i),1\leq i\leq n_{3}\) can be factorized as \(A(i)\) and \(B(i)\) such that \(\bar{L}(i)=A(i)B(i)^{T}\), then the following relaxation holds:  
\[\frac{1}{n_{3}}\sum_{i=1}^{n_{3}}||\bar{L}(i)||_{*} =\frac{1}{n_{3}}\sum_{i=1}^{n_{3}}||\bar{A}(i)\bar{B}(i)^{T}||_{*}\] \[\stackrel{{\text{relav}}}{{\Longrightarrow}}\frac{1} {2n_{3}}\sum_{i=1}^{n_{3}}(\|\bar{A}(i)\|_{F}^{2}+\|\bar{B}(i)\|_{F}) \tag{16}\]  
Since \(\|\mathcal{A}\|_{F}=\|\bar{A}\|_{F}\) and \(\|\mathcal{B}\|_{F}=\|\bar{B}\|_{F}\), this completes the proof of (14).  
Note that if the rank of the solution obtained from (3) is equal to that of the solution obtained from (13), then the solution of (13) is also a solution to (3) [23]. The augmented Lagrange function corresponding to the optimization problem in (13) is given by  
\[L(\mathcal{A},\mathcal{B},\mathcal{P},\lambda) =\frac{1}{2n_{3}}(\|\mathcal{A}\|_{F}^{2}+\|\mathcal{B}\|_{F})+ \lambda||\mathcal{S}||_{1}+<\mathcal{P},\mathcal{A}*\mathcal{B}^{T}+\mathcal{ S}-\mathcal{X}>\] \[+\frac{\mu}{2}\|\mathcal{A}*\mathcal{B}^{T}+\mathcal{S}-\mathcal{ X}\|_{F}^{2}, \tag{17}\]where \(\mathcal{P}\) denotes the augmented Lagrange multiplier and \(\mu\) denotes a penalty parameter. Note that the inner product between tensors \(<\mathcal{A},\mathcal{B}>\) is  
\[<\mathcal{A},\mathcal{B}>=\frac{1}{n_{3}}<\bar{A},\bar{B}>. \tag{18}\]  
The low-rank tensor \(\mathcal{L}=\mathcal{A}*\mathcal{B}^{T}\) is then recovered by solving the optimization problem of (17).
# Multiresponsive Hybrid Microparticles for Stimuli-Responsive Delivery of Bioactive Compounds
###### Abstract  
Hybrid microparticles based on an iron core and an amphiphilic polymeric shell have been prepared to respond simultaneously to magnetic and ultrasonic fields and variation in the surrounding pH to trigger and modulate the delivery of doxorubicin. The microparticles have been developed in four steps: (i) synthesis of the iron core; (ii) surface modification of the core; (iii) conjugation with the amphiphilic poly(lactic acid)-grafted chitosan; and (iv) doxorubicin loading. The particles demonstrate spherical shape, a size in the range of 1-3 \(\upmu\)m and surface charge that is tuneable by changing the pH of the environment. The microparticles demonstrate good stability in simulated physiological solutions and are able to hold up to 400 ug of doxorubicin per mg of dried particles. The response to ultrasound and the changes in the shell structure during exposure to different pH levels allows the control of the burst intensity and release rate of the payload. Additionally, the magnetic response of the iron core is preserved despite the polymer coat. In vitro cytotoxicity tests performed on fibroblast NIH/3T3 demonstrate a reduction in the cell viability after administration of doxorubicin-loaded microparticles compared to the administration of free doxorubicin. The application of ultrasound causes a burst in the release of the doxorubicin from the carrier, causing a decrease in cell viability. The microparticles demonstrate in vitro cytocompatibility and hemocompatibility at concentrations of up to 50 and 60 ug/mL, respectively.  
core-shell microparticles; ultrasound; amphiphilic polymers; magnetic microparticles; doxorubicin
## 3 Results
### Establishment of a Detection Method  
During MIP preparation, several imprinted sites remained on the surface of the MIPs containing QDs. As a result, the template bacteria were easily bound to the polymer beads and caused a significant change in the fluorescence intensity. To confirm the utility of these materials, the MIPs and NIPs were first incubated with \(10^{3}\) and \(10^{5}\) CFU mL\({}^{-1}\) concentrations of bacteria and the fluorescence intensity of the resulting polymer beads was directly observed using a fluorescence microscope under an ultraviolet lamp. Images are presented in Figure 7. The beads differed in brightness at different concentrations of _L. monocytogenes_, and the original MIPs and NIPs exhibited the brightest blue luminescence. With increasing bacteria concentrations ranging from \(10^{3}\) to \(10^{5}\) CFU mL\({}^{-1}\), the color of the polymer beads became increasingly darker with a more pronounced change in the MIPs. Considering that microbial imprinting has not reached the level of precision that can be achieved by imprinting small molecules, the fluorescence MIPs sensor was applied for fast and qualitative detection of _L. monocytogenes_ with a limit of detection (LOD) of \(10^{3}\) CFU mL\({}^{-1}\).  
Figure 6: The optical images of MIPs absorbing (**a**) _L. monocytogenes_ and (**b**) _S. aureus_.