esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The utilization of CoFeB thin films in spintronic devices has attracted significant attention due to their exceptional magnetic properties, which include high saturation magnetization and spin polarization. However, the effect of ambient temperature on the magnetic properties of CoFeB/MgO frames, particularly those with different buffer and capping layers, remains unexplored. Therefore, in this study, the magnetostatic and dynamic properties of CoFeB/MgO frames were investigated at various temperatures. Using vibrating sample magnetometry and ferromagnetic resonance spectroscopy, changes in key parameters such as saturation magnetization, the Gilbert damping constant, magnetic anisotropy field, in-plane uniaxial magnetic anisotropy energy, and thermal stability factor were investigated. Furthermore, the thermal stabilities of CoFeB/MgO frames with Ta buffer and capping layers were compared with those of CoFeB/MgO frames with W buffer and capping layers by examining the changes in the key parameters at various temperatures. These results reveal that the thermal stability of the latter surpassed that of the former. This study provides significant insights for the development of thermally robust spintronic devices capable of operating above room temperature.  
high-temperature measurements; vibrating sample magnetometry; ferromagnetic resonance; spintronics; magnetic thin film; CoFeB thin film +
Footnote †: journal: Physics Letters
## 1 Introduction
While many structurally diverse secondary metabolites, especially terpenoid compounds, have been identified in _P. minus_, its biosynthesis of major constituents remains unclear because of the limited genomic information that is available for this plant. Therefore, this plant is investigated for the isolation and characterisation of novel sesquiterpene synthase genes. Additionally, there is not much work on sesquiterpene synthase from _P. minus_, especially at the genetic level, as it is still very scarce. In previous studies, several works on sesquiterpene synthases were identified. The first _P. minus_ putative sesquiterpene synthase gene was cloned and expressed in _E. coli_ systems [35]. [PERSON] et al. [36] successfully overexpressed a sesquiterpene synthase, _Pm_STS, in metabolically engineered gram-positive bacteria, _Lactococcus lactis_, with the MVA pathway. Then, a structural study was performed that demonstrated an active catalytic site with the same gene sequence [37]. The encoded enzyme was named \(\beta\)-sesquiphellandrene synthase, based on the principal product that was formed. In a subsequent study, the influence of jasmonic acid treatment on the expression level of the _Persicaria minor_ sesquiterpene synthase (_Pm_SS) gene was reported [38]. Until very recently, the purification and overexpression of _P. minor_ sesquiterpene synthase encoded as _Pm_STS recombinant protein in pET28b vector, using the _E. coli_ BL21 (DE3) strain, were reported [39]. All of the studies on _P. minus_ that have been mentioned above were based on the same sesquiterpene synthase. In order to understand the terpenoid metabolism of _P. minus_, the potential gene for terpene synthases must be isolated and studied. Therefore, in this study, we described the cDNAs isolation and characterisation of two new sesquiterpene synthases (STPS) that were responsible for the formation of two key aromatic compounds, which made substantial contributions to the flavour and fragrance of _P. minus_ essential oil. The results could have provided a foundation for the further exploration of gene function in _P. minus_, and helped to reveal the regulation of terpenoid biosynthesis.
# Experimental Study on the Workability and Stability of Steel Slag Self-Compacting Concrete
## 1 Introduction
Moreover, it is not enough to encourage the use of a steel slag in the construction industry without misgivings or concerns if the technical opinion is not supported by sufficient characterization and performance testing [28]. Analyzing the aforementioned research on the use of steel slag in SCC, it found that the steel slag was mainly used to replaces the coarse aggregate [22; 26] or both coarse and fine aggregate [20; 21; 23]. Meanwhile, the substitution rate is relatively high, for instance, all coarse aggregate was replaced [21; 22; 23; 26], the substitution rate is of 0%, 50%, 100% for both coarse and fine aggregates [20]. The steel slag was also used as powder replacing the cement (fixed substitution rate of 30%) [25], as a fine aggregate (substitution rate is 0, 40%, 60%, 80%) together with the glass powder [24]. The research work that has been carried out mainly focuses on workability and mechanical properties of steel slag self-compaction concrete (SSCC). However, the expansion potential of steel slag has always been an important factor restricting its large-scale application in building materials [28], and it is easier to cause inhomogeneous expansion stress for large-size coarse aggregates. Therefore, this paper focuses on the use of steel slag as fine aggregate, and the aged steel slag used in incorporating fly ash in SSCC, which is of benefit to reduce the expansion effect of steel slag and inhomogeneous expansion stress. The present work is to investigate the filling ability, passing ability, dynamic stability and static stability of a fresh mixture of SSCC with steel slag as the fine aggregate, and is expected to systematically investigate SSCC workability to promote the wide application of steel slag in SCC for traditional material saving and sustainable development.
## 2 Preliminaries and Problem Formulation
### Vessel Kinematics and Dynamics  
The schematic diagrams for a vessel sailing in restricted waters are presented in Figures 1 and 2. Here restricted waters with sloped bottoms are considered since the term \"slope bottom\" is a simple but quite realistic description of the near-shore area. Two right-handed reference frames, the inertial reference frame \(o_{s}-x_{s}y_{s}z_{s}\), and the body-fixed reference frame \(o_{b}-x_{b}y_{b}z_{b}\), are adopted. The plane \(o_{s}-x_{s}y_{s}\) is fixed at the undisturbed free surface, with \(o_{s}\) being the vessel's initial position, \(y_{s}\)-axis being perpendicular to the bank with the positive direction pointing to the bank, and the positive direction of \(x_{s}\)-axis pointing toward the vessel's course; the plane \(o_{b}-x_{b}y_{b}\) is located at the undisturbed free surface, with the origin \(o_{b}\) at the midship section, \(x_{b}\)-axis directing from aft to fore, and \(y_{b}\)-axis directing to starboard. \(D\) presents the initial distance of the vessel away from the bank, and \(D\) = 44.5 m.  
The kinematic equations for the vessel's 3-DOF motion in the horizontal plane are:  
\[\begin{array}{l}\dot{x}=u\cos\psi-v\sin\psi,\\ \dot{y}=u\sin\psi+v\cos\psi,\\ \dot{\psi}=r,\end{array} \tag{12}\]  
Figure 1: Coordinate systems for a vessel sailing in restricted waters.  
Figure 2: Sketch maps of a vessel sailing in restricted waters with varying water depths. (**a**) Side view; (**b**) rear view.  
where \([x,\,y,\,\psi]^{T}\) denotes the position and orientation of the vessel in the inertial reference frame, \([u,\,v,\,r]^{T}\) is the velocity vector including surge and sway velocities and yaw rate in the body-fixed reference frame.  
It is assumed that (i) the vessel is regarded to be rigid with its mass uniformly distributed; (ii) the vessel is symmetrical about the longitudinal center plane; (iii) the vessel's speed \(U\) is constant, the yaw angle \(\psi\) is small, therefore \(u=U\); and only the sway and yaw motions are considered; (iv) the bank is straight.  
According to the above assumptions, the dynamics of a vessel sailing in restricted waters is denoted:  
\[\begin{array}{c}(m+m_{22})\dot{v}+(x_{G}m+m_{26})\dot{r}+(m+m_{11})r=Y(v,r,y, h,\delta),\\ (I_{z}+x_{G}^{2}m+m_{66})\dot{r}+(x_{G}m+m_{26})\dot{v}+x_{G}mr=N(v,r,y,h,\delta), \end{array} \tag{13}\]  
where \(x_{G}\) is the longitudinal position of the vessel's center of gravity. \(m\) is the vessel's mass and \(I_{z}\) is the inertia moment about \(o_{z}z_{b}\); \(m_{ij}\) is the added mass. \(Y(\cdot)\) and \(N(\cdot)\) are the sway force and yaw moment acting on the vessel excepting the hydrodynamic inertia terms, the item \(h\) in \(Y(\cdot)\) and \(N(\cdot)\) represents the influence of water depth on the vessel motion. The detailed expressions for \(Y(\cdot)\) and \(N(\cdot)\) refer to [PERSON] et al. [7]. According to Equation (13), the only available control is the rudder angle \(\delta\). To solve the path following problem of an underactuated vessel, a sideslip observer based path following control scheme was designed, to make the vessel converge to the desired path.
## 3 Results and Discussion
### Kinetic Studies  
Zn adsorption towards biomass-based carbon at room temperature is quite fast, as can be seen in Figure 6. Almost 80% removal can be achieved within the first 60 min. The adsorption equilibrium was attained at 240 min, and it remained constant thereafter. Maximum Zn removal and adsorption capacity were 95% and 14.4 mg/g, respectively. The fast removal of impurities is typical for carbonaceous adsorbents [34; 44].  
Pseudo first-order, pseudo second-order, and Elovich kinetic models were applied to the experimental data. The obtained kinetic parameters are listed in Table 4, and fits are shown in  
Figure 5: Langmuir, Freundlich, and Sips models applied to the experimental results.  
Figure 6: Zinc (Zn) removal [%] as a function of time (minutes). Experiments were done duplicated and the range of results were at maximum 3 percentage points.  
Figure 7. The best fitting model was the Elovich model, with a correlation coefficient of 0.97 and an RMSE of 0.68. However, the correlation of the pseudo second-order model was also rarely good as it typically is for carbonaceous adsorbents [34; 44]. The R\({}^{2}\) value was 0.94, and the calculated equilibrium removal (13.01 mg/g) was quite similar to the experimental one (14.36 mg/g). It can be seen in Figure 7 that the sorption process of the Zn removal did not take place immediately; rather, equilibrium was reached after a few hundred minutes. This is typical of processes following the Elovich model.
## 2 System Model
### K-Means Clustering Algorithm  
At the foundation of this framework lies the implementation of the K-means clustering algorithm, an extensively utilized unsupervised learning methodology for the categorization of data points predicated on their similarity. Within the framework of VANETs, K-means clustering is employed to categorize vehicles into distinct clusters according to their spatial proximity, Signal-to-Noise Ratio (SNR) metrics, and prevailing traffic patterns. This clustering mechanism facilitates the efficient exchange of data within the clusters and enhances inter-cluster communication by designating cluster heads tasked with the responsibility of transmitting information.  
Figure 1: An illustration of the multi-layered unsupervised learning-based and VANET-enabled ITS framework.  
Each vehicle is assumed to periodically calculate its SNR values with respect to nearby vehicles. Based on these values, the K-means algorithm groups vehicles into clusters, optimizing communication paths and improving data transmission reliability.  
The clustering process is mathematically modeled as follows:  
Let \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) represent the set of vehicles in the network, and let \(P=\{p_{1},p_{2},\ldots,p_{k}\}\) represent the set of cluster centroids, where \(k\) is the number of clusters. The objective of the K-means algorithm is to minimize the intra-cluster variance, defined as:  
\[J=\sum_{i=1}^{k}\sum_{v_{j}\in C_{i}}\|v_{j}-p_{i}\|^{2}\]  
where \(C_{i}\) represents the set of vehicles assigned to cluster \(i\), and \(\|v_{j}-p_{i}\|\) denotes the Euclidean distance between vehicle \(v_{j}\) and the centroid \(p_{i}\).  
The centroids are updated iteratively as follows:  
\[p_{i}=\frac{1}{|C_{i}|}\sum_{v_{j}\in C_{i}}v_{j}\]  
The clustering process continues until the centroids converge, i.e., there is no significant change in their positions across iterations.
## 3 Theoretical Analysis
### Atomization Rate  
In a previous study [42], it was found that when the piezoelectric vibrator moves from the equilibrium position to the extreme position, the volume of the liquid chamber changes is follows:  
\[\Delta V_{VS}=2\pi\int_{0}^{R}w_{0}(1-\frac{r^{2}}{R^{2}})rdr=\frac{\pi w_{0} R^{2}}{2} \tag{3}\]  
where \(\Delta V_{VS}\) is the volume of the liquid chamber changes.  
The volume of the cone apertures changes is follows [42]:  
\[\Delta V_{VD}=\iint\limits_{\Omega}[2z(f_{yy}+f_{xx}+f_{y}^{2}f_{xx}-2f_{x}f_{ y}f_{xy}+f_{x}^{2}f_{yy})m^{-1}]dV \tag{4}\]  
where \(\Delta V_{VD}\) is the volume of the cone apertures changes.  
Figure 4: Empirical curves of the cone angle, the diffuser loss coefficient \(\xi_{Sd}\), and the nozzle loss coefficient \(\xi_{Sn}\). (\(d\), \(L\), \(L_{1}\) is defined in figure 4 in references [44].)  
[PERSON] et al. provided an equation for the net volume flow rate when the cone hole is in a steady state [44]:  
\[\mathrm{q}_{V}\approx\Delta Vf\frac{\xi_{Nd}-\xi_{Nn}}{2+\xi_{Nd}+\xi_{Nn}} \tag{5}\]  
where \(\Delta V\) is the volume variation of the liquid chamber, \(f\) is the vibrational frequency, \(\xi_{Nd}\) is the diffuser element loss coefficient, and \(\xi_{Nn}\) is the nozzle element loss coefficient.  
Therefore, the flow rate caused by the change of the volume of the liquid cavity is:  
\[q_{VS}\approx\Delta V_{S}f\frac{\overline{\xi}_{Dd}-\overline{\xi}_{Dn}}{2+ \overline{\xi}_{Dd}+\overline{\xi}_{Dn}} \tag{6}\]  
where \(\Delta V_{S}\) is the volume variation caused by the piezoelectric vibrator.  
Similarly, the flow rate caused by the change of the volume of the micro-cone hole is:  
\[q_{VD}\approx\Delta V_{D}f\frac{\overline{\xi}_{Dd}-\overline{\xi}_{Dn}}{2+ \overline{\xi}_{Dd}+\overline{\xi}_{Dn}} \tag{7}\]  
where \(\Delta V_{D}\) is the volume variation caused by the micro-cone hole.  
With the tapered side exposed to the air and the flared side immersed in the liquid, the overall volume flow of the dispenser is:  
\[\begin{array}{c}q\\ =\Delta V_{S}f\frac{\overline{\xi}_{Dd}-\overline{\xi}_{Dn}}{2+\overline{\xi} _{Dd}+\overline{\xi}_{Dn}}+\Delta V_{D}f\frac{\overline{\xi}_{Dd}-\overline{ \xi}_{Dn}}{2+\overline{\xi}_{Dd}+\overline{\xi}_{Dn}}\\ \end{array} \tag{8}\] \[=(\Delta V_{S}+\Delta V_{D})f\frac{\overline{\xi}_{Dd}-\overline{\xi} _{Dn}}{2+\overline{\xi}_{Dd}+\overline{\xi}_{Dn}}\]  
When the atomizer shown in Figure 1a is reversed by \(180^{\circ}\), the flared side is exposed to the air, and the tapered side immersed in the liquid. The pumping effect of the micro-cone hole and liquid chamber can also cause atomization. However, in such a case, the flow resistance is higher in the diffuser element than in the nozzle element, and the atomization rate is much less than that it is in normal conditions.  
Due to the difference of the diffuser flow resistance and the nozzle flow resistance, and the volume changes in the micro-tapered aperture and the liquid chamber, a pumping effect is produced, and the one-way flow of the liquid is caused by the pumping effect.
## 5 Simulation Analysis
### Simulation Results Using FTC Strategies
The simulation results indicate that the system under moderate fault and with the proposed CO-FTC control method can track the command input better then when using the GCC control method: To track the square-wave reference signal, the rising time using the proposed CO-FTC control method is about 1 s while the rising time using the GCC control method is about 2 s (see Figure 11(a1)). Meanwhile, the tracking error former control method converges faster than the error using the latter control method (see Figure 11(a2)). To track a compound sine-wave reference signal, using the former control method, the tracking performance has less time lag and smaller peak error compared with it using the later control method
_Article_  
Effects of Added Mass and Structural Damping on Dynamic Responses of a 3D Wedge Impacting  
on Water  
[PERSON] 1\({}^{\copyright}\), [PERSON] 2\({}^{,}\)*\({}^{\copyright}\) and [PERSON] 3  
1 College of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian 116026, China; [EMAIL_ADDRESS]  
2 Department of Mechanical and Structural Engineering and Materials Science, University of Stavanger, Stavanger 4036, Norway  
3 College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +47-51-831-112  
Received: 13 March 2018; Accepted: 22 April 2018; Published: 16 May 2018  
**Abstract:** The impact between the wave and the bottom of a high-speed vessel is often simplified as water-entry problems of wedges. Most investigations focus on the water entry of two dimensional (2D) wedges. The effects of added mass and structural damping are still not fully investigated. By combining the normal mode method, the hydrodynamic impact model of rigid wedges and the potential flow theory, a dynamic model for predicting the response of a three dimensional (3D) wedge impacting on water with a constant velocity is established in this paper. The present model can selectively consider the effects of the added mass and the structural damping. The present method has been validated through comparisons with results of published literatures and commercial software. It is found that the added mass can increase the stress response before the flow separation, and reduce the vibration frequency after the flow separation. Due to the effect of the added mass, the stress response of some positions after the flow separation is even higher than that before the flow separation. The structural damping has a negligible effect on the stress before the flow separation, but it can reduce vibration stress after the flow separation.  
**Keywords:** water impact; dynamic response; added mass; structural damping
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement  
### Scenario Development  
The Arctic NSR is characterized by a special nautical environment and unique location compared to other national and international waterways. Traditionally, the NSR depicted in Figure 5 is made up of five legs: the Barents Sea, the Kara Sea, the Laptev Sea, the East Silerian Sea, and the Chukchi Sea. The presence of sea ice, including drifting icebergs, has made this route almost inaccessible for marine transportation [56]. The area of ice-covered waters within the NSR is shown in Table 2. It is widely accepted that the navigating period for the NSR can be continuous from September to March of the following year.  
With the difficulties associated with communication and data acquisition in the NSR region, the safety issues and environmental risks related to NSR transport are traditionally managed based on empirically determined rules and regulations, which can be useful for shipping activities characterized by low traffic density with small ships [58]. However, the ships sailing along the NSR are becoming increasingly large in size, and there is a lack of relevant empirical data on which mitigation of the hazards with respect to the NSR can be based [59]. Therefore, safety issues concerning the NSR are an urgent problem that must be solved.
_Applied_  
_sciences_  
_Article_  
**Distinguishing Different Cancerous Human Cells**  
**by Raman Spectroscopy Based on Discriminant**  
**Analysis Methods**  
**[PERSON] 1, [PERSON] 1,2,*, [PERSON] 1 (c), [PERSON] 1, [PERSON] 1,* and [PERSON] 1,3**  
1 Chongqing Key Laboratory of Multi-Scale Manufacturing Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; [EMAIL_ADDRESS] (M.T.); [EMAIL_ADDRESS] (D.W.); [EMAIL_ADDRESS] (S.Y.); [EMAIL_ADDRESS] (H.-L.C.)  
2 School of Electronic Information Engineering, Yangtze Normal University, Chongqing 408100, China  
3 College of Instrumentation Science and Electrical Engineering, Jilin University, Changchun 130061, China  
* Correspondence: [EMAIL_ADDRESS] (L.X.); [EMAIL_ADDRESS] (C.D.); Tel.: +86-023-6593-5646 (L.X.); +86-023-6593-5600 (C.D.)  
Received: 20 July 2017; Accepted: 30 August 2017; Published: 1 September 2017  
**Abstract:** An approach to distinguish eight kinds of different human cells by Raman spectroscopy was proposed and demonstrated in this paper. Original spectra of suspension cells in the frequency range of 623-1783 cm\({}^{-1}\) were acquired and pre-processed by baseline calibration, and principal component analysis (PCA) was employed to extract the useful spectral information. To develop a robust discrimination model, a linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were attempted comparatively in the work. The results showed that the QDA model is better than the LDA model. The optimal QDA model was generated with 12 principal components. The classification rates are 100% in the calibration and prediction set, respectively. From the experimental results, it is concluded that Raman spectroscopy combined with appropriate discriminant analysis methods has significant potential in human cell detection.  
**Keywords:** Raman spectra; discriminant analysis; distinguish; human cells
## 4 Influence of Structural Parameters on Crossbeam Mode
### Structural Parameters Analytical Solutions
Compared with the bending stiffness, the linear density in the middle part of the crossbeam has a lower impact on the natural frequency, and, however, the effect on the modal node position has the opposite result. With regard to the mode shape in the right figure, the modal node span decreases with the increase in the linear density in the middle part of the crossbeam. The increase in the bending stiffness in the middle part will lead to the increase in the modal node span, but this change is much weaker than that of the linear density. The trend of crossbeam mode with the structural parameters in the end part of the crossbeam is shown in Figure 8. Something noticeable can be observed from Figure 8. Compared with the results of the bending stiffness in the middle part of the crossbeam, it appears that the coupling between the natural frequency and the bending stiffness in the end part is weak. In addition to the above differences, the other influence degree of the structural parameters in the end parts is basically consistent with the structural parameters in the middle part, but the change direction of the crossbeam mode shape is reverse. It can be concluded from the results that it is easier to adjust the crossbeam mode shape by modifying the linear density in both the end and middle part of the crossbeam. This is good information because modulating weights in crossbeam design is usually  
Figure 7: The crossbeam mode versus the structural parameters in the middle part: (**a**) mode versus the bending stiffness; (**b**) mode shape versus the bending stiffness; (**c**) mode versus the linear density; (**d**) mode shape versus the linear density. more achievable. Another piece of information obtained from the comparison is that each structural parameter has a different influence degree on the crossbeam mode. We elaborate more the sensitivity of the crossbeam mode to various structural parameters below.