esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Black Jade on Osteogenic Differentiation of Adipose Derived Stem Cells under Benzopyrene
###### Abstract  
Jade, a popular gemstone symbolizing beauty, grace, and longevity, is known to improve blood circulation; however, scientific research evidence is still lacking. The effect of black jade extract on the expression levels of apoptotic and osteogenic genes was validated using qPCR and flow cytometry. In combination with the use of a fluorescence microscope, osteogenic differentiation and the stained osteocytes count were analyzed. Under the pressure of benzo(a)pyrene, dermal cell apoptosis was accelerated and the osteogenic differentiation of adipose-derived stem cells (ASCs) was suppressed; but black jade extract counteracted the effects. Through an anti-apoptotic mechanism, the extract suppressed the expression of apoptotic proteins Bax and cytochrome C to 9 and 4.8 times, respectively, compared to that in dermal cells exposed to benzo(a)pyrene. During osteogenic differentiation of ASCs, the extract enhanced their differentiation despite being exposed to benzo(a)pyrene, and the relative levels of the osteoblast differentiation markers osteeponin, osteeocalcin, and sclerostin were 1.87, 2.54, and 3.9 times higher, respectively, than those in the conditioned medium by benzo(a)pyrene. These effects of the extract indicate that black jade extract is very useful when applied as a functional biomaterial.  
osteogenesis; black jade; adipose-derived stem cell; fine particles; osteocyte +
Footnote †: journal:  
(BMSCs) are very useful cells in osteogenic application [7], from the viewpoint of functional application in cosmetics, osteogenic cells derived from adipocyte-derived stem cells (ASCs) are more suitable. ASCs are differentiated from various lineages including adipocyte, chondrocyte, neuron, myocyte, and osteocyte [7; 8; 9; 10]. In the skin, ASCs are localized to subcutaneous fat tissue and connected to other dermal cells [11]. Unlike the inhibition of osteogenic differentiation, fibroblast growth factor-2 (FGF-2) activates adipogenic and chondrogenic differentiations in ASCs [12]. In addition, matrix metalloproteinase (MMP) release is increased during damage to the connective tissue [13] and osteogenic differentiation is inhibited by MMPs in ASCs [14].  
During the osteogenesis of mesenchymal stem cells, the levels of expression of various genes change with development stages. In pre-osteoblasts, levels of RUNX2 and DLX5 are increased, osteoponin and osteoponin are expressed in osteoblasts and SOST-sclerostin is expressed in osteocytes [15; 16]. In addition, p53 down-regulates osterix and RUNX2 in ASCs [15].  
This study aimed to investigate the protective effect of black jade extract on dermal cells under benzo(a)pyrene stress and the altered osteogenic differentiation in ASCs using activated fibroblast cells. In addition, this research documented the interaction between ASCs and dermal cells exposed to benzo(a)pyrene.
## 1 Introduction
The long periodicity of low-frequency signals leads to fewer complete cycles in fixed sampling periods, worsening resolution problems [27]. Additionally, segmentation-based PSD estimation further reduces resolution at low frequencies. Although [PERSON]'s multi-taper and adaptive sine multi-taper algorithms theoretically offer higher precision [28,29], their application in this field remains unexplored. Noise assessment should not only focus on algorithms; optimizing the physical systems is also important. For instance, numerical optimization studies of catalytic diesel particulate filters have demonstrated that enhancing system design can effectively reduce noise interference, offering valuable insights for noise suppression in gravitational-wave-detection hardware [30]. This study proposes a taper number-optimized adaptive sine multi-taper algorithm to improve the resolution in low-frequency bands of gravitational-wave-detection systems. Improving resolution depends on balancing bias and variance, which can be accomplished through taper number optimization. The original algorithm employs equal weighting across all frequency bands and utilizes high taper numbers at low frequencies. However, this approach can lead to increased variance and biased estimates, resulting in poor resolution in low-frequency regions. To overcome this, our proposed algorithm introduces a full-frequency taper number optimization strategy. It begins with a uniform initialization of taper numbers across all frequencies. For low-frequency bands, a gradient-based refinement is applied to adjust the taper numbers iteratively. This iterative process continues until the optimal PSD estimate is achieved. Performance evaluation using mean squared error (MSE) demonstrates the superiority of the proposed algorithm. By comparing it with existing methods and validating its effectiveness through practical system performance experiments, this study establishes the algorithm as a robust tool for assessing gravitational wave detection systems under complex noise conditions. The paper is organized as follows: Section 2 describes the taper number optimization process; Section 3 compares the proposed algorithm with existing methods in simulated scenarios; Section 4 validates and discuss the algorithm through real-world system performance experiments; and Section 5 concludes with key findings. This research contributes a novel PSD estimation method with significant theoretical and practical implications for gravitational wave detection.
## 1 Introduction  
This article is the second in a series of two dealing with the potential release of nanomaterials after intense mechanical-, thermal-, and chemically-induced stress conditions. Nanomaterials are used in consumer products like food packaging articles which require profound knowledge on the potential release of nanomaterials into foods for proper risk assessment. In our previous works, based on comprehensive migration testing using plastics nanocomposites with incorporated silver nanoparticles (Ag-NPs) [1], nanotitanium nitride (TiN) [2], and nanolaponite [3], we could not find any detectable migration and together with theoretical considerations we concluded that migration of nanoparticles from plastic food packaging materials in general is not possible according to Fickian Law of diffusion as long as the particles are larger than 3-4 nm (in case of polyolefins) or 1-2 nm (for PET and similar polymers) in diameter [4] and when the nanomaterial is fully covered with polymer (i.e., does not stick out of the polymer). Since such small nanomaterial sizes are not used in food contact nanocomposites exposure of the consumer due to diffusion-controlled migration of nanomaterials can be excluded.  
In a number of publications positive results for nanoparticle migration were reported. A closer view into these publications revealed that the migration tests were done using cut strips or pieces of polymer films or articles or using very aggressive food simulants which destroyed the polymer [5]. From these findings and from the generally raised question whether under stress conditions in use nanomaterials could be released from the food contact surface of nanocomposites, the motivation for this project was taken to explore whether such mechanical release of nanomaterials may occur and if yes, how it could be tested. In Part A (reference to: Appl. Sci. 2018, 8, [PERSON], [PERSON], \"Investigations into the potential abrasive release of nanomaterials due to material stress conditions- Part A: carbon black nanoparticles in plastic and rubber composites\") of this series a possible experimental design to test release of nanomaterials after mechanical-, thermal-, and chemical-induced stress was proposed and already tested on nanocomposites containing carbon black. It was demonstrated that carbon black will not be released as long it is fully incorporated into the polymer. In Part B of this series, the same experimental design and abrasion test will now be used on Ag, TiN, and laponite nanocomposites.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 3 Perturbation Observer-Based Nonlinear Adaptive Controller Design
### NAC Design of PMSG
#### 3.2.3 Design of Perturbation Observer  
When the system outputs \(y_{2,3}\) are available, two second-order POs are designed for the estimations of states and perturbation for the subsystems  
\[S2:\left\{\begin{array}{rcl}\hat{\mathbf{\varepsilon}}_{21}&=&\hat{\mathbf{\varepsilon} }_{21}+l_{21}(z_{21}-\hat{\mathbf{\varepsilon}}_{21})+B_{2}(0)u_{2}\\ \hat{\mathbf{\varepsilon}}_{22}&=&l_{22}(z_{21}-\hat{\mathbf{\varepsilon}}_{21}), \end{array}\right. \tag{38}\]  
\[S3:\left\{\begin{array}{rcl}\hat{\mathbf{\varepsilon}}_{31}&=&\hat{\mathbf{ \varepsilon}}_{31}+l_{31}(z_{31}-\hat{\mathbf{\varepsilon}}_{31})+B_{3}(0)u_{3}\\ \hat{\mathbf{\varepsilon}}_{32}&=&l_{32}(z_{31}-\hat{\mathbf{\varepsilon}}_{31}), \end{array}\right. \tag{39}\]  
where \(\hat{\mathbf{\varepsilon}}_{21}\), \(\hat{\mathbf{\varepsilon}}_{22}\), \(\hat{\mathbf{\varepsilon}}_{31}\), and \(\hat{\mathbf{\varepsilon}}_{32}\) are the estimations of \(z_{21}\), \(z_{22}\), \(z_{31}\) and \(z_{32}\), respectively, and \(l_{21}\), \(l_{22}\), \(l_{31}\) and \(l_{32}\) are gains of the observers. They are designed similarly to Equation (20).  
**Remark 1**.: _It should be mentioned that during the design procedure, \(\epsilon_{i}\) used in POs Equations (38) and (39) are required to be some relatively small positive constants only, and the performance of POs is not very sensitive to the observer gains, which are determined based on the upper bound of the derivative of perturbation._
## 4 Results and Discussions
### Comparisons  
Another assessment measure for slope stability, the stability number \(N_{s}\) (\(N_{s}=\gamma H_{cr}/\zeta_{t}\)), is utilized to analyze the stability of the slopes governed by the linear M-C yield criterion in the 3D horn-shaped rotating failure mechanism [9], in which \(H_{cr}\) is the critical height of the slope in the case of instability and the corresponding safety factor FS = 1. Meanwhile, the safety number \(N_{s}\) can be represented by the reciprocal of \(\lambda_{ct}\) (\(N_{s}=1/\lambda_{ct}\)). In Equation (1), if the parameter n = 1, the nonlinear H-B criterion will be transformed into a linear M-C criterion. Hence, to verify the correctness of the rock slope failure mechanism, the results from [PERSON] and [PERSON] can be converted into \(\lambda_{ct}\) and brought into Equation (1) to calculate FS, corresponding to \(T_{ui}\) = 0.0 (\(i=1,\ 2,\ \dots\,\) n), \(T_{lj}\) = 0.0 (\(j=1,\ 2,\ \dots\,\) m), \(k_{h}\) = 0.0, and \(\lambda_{a}\) = 0.0 (\(\lambda_{a}=a/H\), the step width coefficient). The comparison results are displayed in Table 1, which shows that the safety factor is close to 1, indicating that the failure model proposed in this paper is effective.  
In order to examine the accuracy of the modified pseudo-dynamic approach used in the article, it is possible to compare the results with those obtained from the pseudo-static method, whose horizontal acceleration can be expressed as: \(a_{h}=k_{h}\cdot g\). After eliminating the effect of the remaining parameters, the comparison outcomes employing the two methods are listed in Table 2, corresponding to \(f=1.0\) and \(D=0.0\). It can be observed that the FS derived from the two pseudo-dynamic methods are remarkably analogous. The rest of the input parameter values are: \(H=15\) m, \(\beta_{1}=\beta_{2}=45^{\circ}\), \(\lambda_{a}=0.0\), \(\sigma_{c}=10\) Mpa, \(\gamma=25\) kPa, GSI = 20, \(m_{i}=10\), \(D=0.0\), \(T=0.2\) s, \(V_{s}=300\) m/s, \(T_{ui}=0.0\) (\(i=1,\,2,\,\ldots,\,\) n), and \(T_{ij}=0.0\) (\(j=1,\,2,\,\ldots,\,\) m).  
On the basis of converting the nonlinear H-B criterion to the linear M-C criterion, set \(\lambda_{a}=0.0\), \(B/H=\infty\), and the number of anchor rows \(\mathbf{n}=1\), \(\mathbf{m}=0\) for the upper and lower step of the slope, respectively, and the 3D two-stage slope reinforced by multiple rows of prestressed anchors is reduced to the 2D single-stage slope reinforced by a single row of anchors, which will facilitate a comparison with the data in the previous literature [36; 49]. From the comparison results in Table 3, it is clear that the data derived in this paper employing the LA in the 3D horn-shaped rotating failure mechanism are similar to the results obtained using other methods, which correspond to the rest of the geometric parameters: \(H=8\) m, \(h_{ui}=4\) m, \(\delta_{ui}=15^{\circ}\).
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 3 The Virtual Musculoskeletal Methodology
### The Closed-Loop Control Structure  
The control algorithm is designed to control the robotic fish compliantly and efficiently. When swimming, the biological fish changes the feature of muscles to control the body's posture and the swing speed to fit the water environment. In the control process, the fish perceives the flow field of the water. Then, the nervous system mixes this information with the posture and the swimming speed to decide how to adjust its swimming parameters. When the neural signals reach the muscles, the muscles contract for the swing of the fish. Consequently, the fish efficiently swims at an expected speed. In this way, the control process designed to mimic the fish in nature is shown in Figure 3.  
The robotic fish is controlled in a closed-loop procedure. At first, the task planning module sets the swimming velocity and the stiffness of both joints. The lookup table is a fitting curve function, shown in Figure 4, which outputs the frequency \(f\) and the amplitude \(A\) of the wave of the robotic fish (All subsequent curve figures in the paper are drawn using MATLAB software.). We regulate the input of this table according to the error between the set velocity and the actual swimming velocity. This negative feedback helps the robotic fish follow the set velocity from the task planning. Second, Equation (2) transforms the wave to angles in the joint space. Third, Equation (24) is decomposed into two equations of two joints.  
\[\left\{\begin{array}{l}M_{1}=d(s)[K_{v1}(r^{*}-v)+H_{1}e_{1}(\theta_{10}, \theta_{10}^{{}^{\prime}})+K_{1}](\theta_{10}^{*}-\theta_{10})\\ M_{2}=d(s)[K_{v2}(r^{*}-v)+H_{2}e_{2}(\theta_{21},\theta_{21})+K_{2}](\theta_ {21}^{*}-\theta_{21})\end{array}\right. \tag{26}\]  
where \(M_{1}\) and \(M_{2}\) are the torques to drive the joints, \(K_{v1}\) and \(K_{v2}\) are the gains for the velocity deviation, \(H_{1}\) and \(H_{2}\) are the intensive coefficients of the feedback in the joints, \(e_{1}\) and \(e_{2}\) are the virtual spindle functions, \(K_{1}\) and \(K_{2}\) are the stiffness set by the task planning module, and \"\({}^{*}\)\" indicates that the value is the given value.  
Figure 3: The closed-loop control structure.  
The last part of the procedure is the movement of the robotic fish under the excitation of \(M_{1}\) and \(M_{2}\). Two models restrain the robotic fish: one is the dynamic model, which is analyzed by the Lagrange method, the other is the hydrodynamic model, which describes how the flow generates the force on the parts of the robotic fish. While the fish is swimming, the swimming velocity and the angles of the joints are fed back to the control algorithm in real-time.
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 3 Methodology
### Rotation Branch for Adaptive Angle Estimation
By applying the 2D IFT to \(\widetilde{C}(\alpha,\ \beta)\), the phase correlation function \(\varphi\) of the spatial domain can be obtained by  
\[\varphi(u-u_{0},v-v_{0})=\mathcal{F}^{-1}\Big{(}e^{j2\pi(\alpha u_{0}+\beta v _{0})}\Big{)}. \tag{13}\]  
In the \(\varphi\), the location \((u_{0},\ v_{0})\) corresponds to the offset between the two images and can be computed for  
\[(u_{0},\ v_{0})=argmax(\varphi(u-u_{0},v-v_{0})). \tag{14}\]  
Through phase correlation, we can finally obtain the offset \(\theta_{0}=v_{0}\), which is the angle difference between \(I_{2}(u,v)\) and \(I_{1}(u,v)\). In tracking, the rotation template \(temp(1)\) for the first frame is obtained by extracting the HOG feature \(z_{hog}(1)\) of the log-polar patch representation. In the subsequent frames, the extracted result \(z_{hog}(t)\) at frame \(t\) is utilized to compute the angle difference \(\theta(t)\) between the \(temp(t-1)\) and \(z_{hog}(t)\), as shown in \[\theta=\arctan\biggl{(}\frac{v-v_{0}}{u-u_{0}}\biggr{)}, \tag{7}\]0 Moreover, to adapt to object changes (e.g., illumination and deformation), the rotation template \(temp(t)\) at frame \(t\) is updated by the learning rate \(\eta_{\theta}\). The pseudocode of rotation branch procedure is shown in Algorithm 1. Compared with the methods [37; 40] that use slope and angle pool to estimate the rotation angle of the object, the proposed method may be suitable for achieving accurate angle estimation and orienting the bounding boxes to a real state in SVs. Stable response maps can also be obtained under object rotation, thereby enhancing the positioning of the translation branch.
# Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data
## 2 River Flow Velocity Inversion Model  
The GNSS-R carrier phase river flow velocity model includes two essential parts: the open-loop tracking method and carrier phase river flow velocity inversion method. The former is used to obtain the interferometric carrier phase observation between direct signals and reflected signals and to extract the Doppler frequency, and the latter is used to conduct river flow velocity inversion.
## 3 System Modeling with Petri Nets
### PN Model for the Whole System  
The PN in Figure 5 models the whole system with two storage tanks, two charging tanks, and two distillers, where the discrete places, enforcing places, and their associated arcs, and the inhibitor arcs are omitted for concision. Crude oil is transported from storage tanks to charging tanks through a pipeline. Thus, \(y\) models both the discharging of storage tanks and the charging of charging tanks at the same time. In the model, \(\{t_{11},\,t_{12},\,y,\,p_{1s},\,p_{1c},\,p_{13}\}\) and \(\{t_{21},\,t_{22},\,y,\,p_{2s},\,p_{2c},\,p_{23}\}\) model the two storage tanks, while \(\{y,\,t_{32},\,t_{33},\,t_{34},\,t_{35},\,p_{3s},\,p_{3s}\}\) and \(\{y,\,t_{42},\,t_{43},\,t_{44},\,t_{45},\,p_{4s},\,p_{4c},\,p_{43}\}\) model the two charging tanks.  
Figure 4: The PN model for the pipeline.  
Figure 5: PN model of the whole system.  
Place \(p_{0}\) with \(F\) tokens in it models a tanker carrying \(F\) types of crude oil when it arrives. Transition \(t_{1}\) can fire to move one token from \(p_{0}\) to \(p_{1}\) only if \(p_{1}\) is empty, and models that only one type of oil can be unloaded at a time. At the right part, places \(p_{3}\) and \(p_{4}\) model two distillers, and \(t_{5}\) and \(t_{6}\) represent distillation. However, it should be noted that the PN model just describes the structure of the system, not the detailed dynamics of the crude oil flow. From the definition of a short-term schedule, the firing of a continuous transition is triggered by an \(OD\). Thus, with the flow rate given by \(OD\)s and the transition enabling and firing rules, the dynamics of the crude oil flow in the PN model can be defined.  
The set of input places of transition \(t\) (input transitions of place \(p\)) is denoted as \({}^{\bullet}t\) (\({}^{\bullet}p\)). Similarly, the set of output places of \(t\) (output transitions of \(p\)) is denoted as \(t^{\bullet}\) (\(p^{\bullet}\)). Furthermore, colors are introduced into the PN model to distinguish different types of crude oil and we let \(\phi_{i}\in\Phi\) denote the crude oil type \(i\), i.e., the color of a token in a place. At marking \(M\), the number of tokens with color \(\phi_{i}\) in \(p\) is denoted as \(M(p,\phi_{i})\) and the associated volume is denoted as \(V(M(p,\phi_{i}))\). It should be pointed out that \(V(M(p,\phi_{i}))=0\) implies \(M(p,\phi_{i})=0\). With the above symbols, readers are referred to [21; 22] for the transition enabling and firing rules. Then, we can discuss the scheduling problem by using the developed model.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
###### Abstract  
Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond [PERSON]'s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system's efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.  
autoencoder; error-correcting codes; M2M communication; polar codes; semantic coding; semantic communication; successive cancellation decoding