esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
We established that chondral cells support and remodel the tissue matrix of cartilage with processes that involve the expression of genes such as SOX9 and Cfba/RUNX2, whose main objective is matrix metalloproteinase (MMPs) and tissue metalloproteinases inhibitors (TIMPs) [30; 31; 32]. For this reason, the presence of the SOX9 gene is critical, allowing us to evaluate the articular cartilage response in relation to the charges. As mentioned, the expression of SOX9 helps with cartilage protection, maintaining the balance between chondrogenesis/osteogenesis [33]. Chondrocytes exhibit unique behavior: they secrete a matrix of plenty of collagen II and aggrecan proteoglycans, expressing a programmed genetic feature driven by SOX9; thus, the levels of SOX9 must be controlled so that normal chondrogenesis occurs [34]. SOX9 regulates the expressions of collagen II, IX, X, and aggrecan; based on this, we are able to determine that, when this gene regulates these proteins, they behave in a certain way. In the present study, we observed cartilage regeneration induced by SOX9 as a result of over-expressions of SOX9, 0.80%\(\uparrow\), Aggrecan, 0.80%\(\uparrow\), and the consequent over-expression of COL2A, 0.47%\(\uparrow\), in the implant inside joint with MSCs that were pre-differentiated to chondrocytes. This may have been caused by the articular charge zone [35], because SOX9 regulates the expression and formation of proteins, as well as the cartilage phenotype and growth [36]. For the control implants without pre-differentiated MSCs, we observed similar expressions of SOX9 and Aggrecan tissue control, but the expression of COL2A1 always remained lower than that of the tissue control. This may have been the result of the migration of cells from the surrounding tissue to the implants. In relation to bone tissue, the obtained results provided null expressions of the specific markers in almost all the implants, except for the one used inside the joint with MSCs that were pre-differentiated to osteoblasts for 6 months, which presented positive significant differences of 0.52%\(\uparrow\) for OPN and 0.63%\(\uparrow\) for RUNX2.
## 1 Introduction
By adopting a two-branch architecture: the MobileNet-v3-small pre-training model extracts deep features from remote sensing images and generates semantic feature vectors representing the image content, while the heterogeneous feature processing channel integrates the MLP branch (which encodes attribute features, such as the content generation time, geographic location range, and payload type, as well as the satellite cache state) and the LSTM branch (which simulates the temporal evolution of the user-cached content). Based on this, a dynamically weighted attention mechanism adaptively fuses the outputs of the dual branches with the image feature vectors to build a multi-modal correlation model that connects the image content, attribute data, and user requirements. * Finally, simulation experiments demonstrate that the proposed multi-modal-MAPPO framework significantly enhances data access efficiency and delivery timeliness. This is achieved through the joint optimization of multi-modal features and agent collaboration mechanisms, which exhibit robust adaptive capabilities and scalability in dynamic topological environments. Consequently, our work establishes a novel paradigm for satellite-terrestrial collaborative services. The rest of this article is structured as follows. Section 1 discusses current research on existing remote sensing data push technology in ISTNs. Section 2 models the network scenario and formulates the optimization problem. Section 3 formulates the proposed multi-modal-MAPPO intelligent push strategy. Section 4 shows the experimental simulation results. Finally, we conclude this article in Section 5.
## 2 System Structure
### Active Structure  
An active structure [13; 17; 18] contains converters in its power network. The fuel cell is coupled to the battery by a converter. Additionally, the battery may be connected to the load by a converter. Different types of DC/DC converters are placed at different locations in the power network to configure different active structures.  
[PERSON] et al. [13] introduced four hybrid structures, but only two were considered to be active (see Figure 2). One is referred to as a semi-active (SA) structure, and the other is referred to as a fully active (FA) structure. In the SA structure, the battery is connected to the fuel cell by a converter, and the battery is directly coupled with the load. In this structure, the battery voltage is determined by the load, similar to the passive structure. In contrast, in the FA structure, the battery is connected to the load by a converter. Therefore, the battery voltage can be controlled to some extent and is not fully dependent on the load. In this paper, they numerically assessed these hybrid structures using computer simulations. Consequently, the SA structure was selected on the basis of performance metrics such as hydrogen consumption and component degradation. However, for an actual implementation, they concluded that it is also necessary to account for constructive metrics such as component availability in the market.  
[PERSON] et al. [18] proposed three active hybrid structures, called the one-stage series, two-stage series, and two-stage parallel structure (see Figure 2). Here, two-stage indicates two converters in a power network. In terms of [PERSON] et al.'s work [13], the one-stage series is equivalent to the SA, and the two-stage series corresponds to the FA. The novel configuration presented in this paper is the two-stage parallel structure, where the battery is connected to the load in parallel. They theoretically assessed these three hybrid structures. As a basis for comparison, a number of performance metrics were considered, including efficiency, complexity, output voltage regulation, design flexibility, peak load capability, controllability, and responsiveness to load changes. As a result, the two-stage parallel structure was selected because it can regulate the load voltage efficiently. They claimed that efficiency and controllability are the most crucial performance metrics in the field of power conversion.
## 2 Network Model
### System Model  
The network architecture of the proposed SROA is shown in Figure 1. Sensor nodes are randomly deployed underwater and divided into a number of clusters. Each cluster elects the cluster head and potential cluster head for data transmission between clusters. In order to transmit the sensed data through acoustic channels, sink nodes are deployed on the seabed that are integrated with the underwater data center, making it convenient for aggregating or processing sensed data, then forward the sensed data to the terrestrial base station for further data analysis. The deployed sensor nodes collect data from the surrounding environment and the sensed data will be sent to the sink node through multihop forwarding. In a three-dimensional system, the distance between the two sensor nodes [9] is calculated as follows,  
\[d=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}+\left(z_{2}-z_{ 1}\right)^{2}} \tag{1}\]  
where \((x_{i},y_{i},z_{i})\) are the location coordinates for the \(i\) node.  
Some assumptions:  
1. The sink node and sensor nodes can obtain its location information. Sensor nodes can obtain the location of the sink using the existing services [19; 20].
2. The initial energy of underwater sensor nodes is same; however, the sink node is not restricted by energy. Each node has the ability to keep its recent communication records in local storage [21].
3. Nodes in a particular layer send packets to that layer's cluster head, who then transmits packets to the cluster head. Sensor nodes have uniform transmission radius and are not impacted by water flow in a short period of time [22; 23].
###### Abstract  
With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient's body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.  
wireless body area networks; application-specific integrated circuit; security and privacy; energy efficiency; health monitoring +
Footnote †: journal: Sensors
# Deformations of FRP-Concrete Composite Beam: Experiment and Numerical Analysis
## 3 Deformation Analysis
### Numerical Simulations  
Deformation behaviour of the GFRP-concrete specimen is considered in a plane stress state with nonlinear constitutive law for concrete. The analysis is carried out using the finite element (FE) software Atena. Half of the beam is modelled following the symmetry conditions. The concrete prism and GFRP profile are simulated as separate macroelements. The GFRP is assumed as an elastic material. All simulations assume perfect bond between the macroelements (i.e., no slip was enabled). The validity of this assumption was verified experimentally--the LVDT device shown in Figure 4 indicated no relative displacement of the GFRP profile. Different sizes of FE meshes are used to represent the concrete (= 5 mm), the GFRP profile (= 2 mm) and the epoxy adhesive layer (= 0.7 mm). Figure 8 shows the FE model and boundary conditions of the analysis.  
Three alternative numerical models are considered. The perfect bond and tension-stiffening model (Figure 7b) describe the deformation behaviour of the GFRP-concrete specimen in the first simulation. An elastic behaviour of the compressive concrete is assumed as well. An additional macroelement is used in the other two simulations to represent the adhesive that is modelled as a perfectly elastic material. Table 1 describes the properties of the materials.  
Material model of the concrete determines the difference between the numerical simulations. Table 2 defines the parameters of the FE models. The 3D Nonlinear Cementtitious 2 User material model, based on the concept of smeared cracks and damage [20], is assumed in the first simulation. Cracking of the concrete is modelled using the tension-stiffening diagram shown in Figure 7b. The fixed crack approach is used for that purpose. The necessary input parameters of the concrete, i.e., the tensile strength (\(f_{cl}\)), the modulus of elasticity (\(E_{c}\)) and the Poisson's ratio (\(\
u\)) are assessed with _fib_ Model Code 2010 [26] using respective values of the compressive strength. The adhesive is not modelled, assuming the perfect bond between the concrete and GFRP profile.  
The Sbeta material model is used in the second and third FE simulations. In contrast to the nonlinear constitutive law (Figure 7b) used in Model #1, a fracture mechanics approach, based on the crack band model [27], is used to define the softening mechanism of the tensile concrete. It is based on the softening law proposed by [PERSON] [28]; the fracture energy (\(G_{f}\)) describes this mechanism. In a similar manner as in Model #1, _fib_ Model Code 2010 [26] defines the strength, \(f_{cl}\); the modulus, \(E_{c}\); the ratio, \(\
u\); and the energy, \(G_{f}\). The second model assumes a perfectly elastic behaviour of the compressive concrete, whereas the default nonlinear model of the compressive concrete is used in the third simulation. The authors of [20] provide a detail description of the material models.  
Figure 7: Tension-stiffening relations: (**a**) derived using the test data and (**b**) the corresponding model.
## 3 LBV of Conventional Aviation Fuel
### The Effect of Additives on Aviation Fuel LBV
Firstly, under conditions of an initial temperature of 420 K and initial pressure of 0.1 MPa, the equivalence ratio corresponding to the maximum LBV differs. Secondly, at \(\Phi\) = 0.8, the LBV of fuel without added methane is minimal, while at \(\Phi\) = 1.2/1.3, the blend fuel with a methane mixture ratio of 0.8 exhibits the lowest LBV. Thirdly, the influence of methane addition on LBV is significant when \(\Phi\) < 0.9 or \(\Phi\) > 1.1, while it is less pronounced when 0.9 < \(\Phi\) < 1.0. The impact of methane addition on RP-3 LBV may arise from changes in density, flame equilibrium temperature, heat capacity, and heat release rate. Similarly, renewable ethanol biofuels can be blended with aviation fuel to mitigate energy crises and facilitate the achievement of the aviation industry's fly net-zero objectives. However, their lower energy density and higher water solubility limit their potential to fully replace aviation rerosene. Therefore, blending ethanol into aviation rerosene is a more practically effective approach [52]. [PERSON] et al. obtained the LBV of ethanol/RP-3 premixed flames in a constant-volume chamber [16]. The results revealed that LBV_max occurred at \(\Phi\) = 1.1, gradually decreasing on both sides. Increasing the initial temperature, decreasing the initial pressure, and increasing the ethanol blending ratio all enhanced the LBV of the premixed flame, with ethanol exhibiting the most significant promoting effect at \(\Phi\) = 1.4. The thermal and dilution effects of ethanol blending lead to a reduction in LBV, while chemical effects enhance LBV and dominate. According to sensitivity analysis and reaction pathway analysis, the addition of ethanol increases the abundance of active free radicals H, O, and OH, accelerating the generation rates of representative species CH\({}_{3}\), CH\({}_{2}\)O, and HCO, thereby enhancing LBV. From a molecular reaction perspective, the rapid decomposition of C\({}_{2}\)H\({}_{5}\)OH into CH\({}_{2}\)O, followed by oxidation to CO\({}_{2}\) without needing extensive breaking, helps increase the LBV of the blend.
## 2 Results and Discussions
### Photocatalytic Efficiency (PE) Results
#### 2.1.2 Digital Image Processing-Based Colorimetry (DIP) of Surface-Polluted Samples  
The images analyzed to obtain the color coordinates and calculate the PE using DIP are shown in Figure 3. The PE results and their respective standard deviations are provided in Supplementary Material Table S2.  
Figure 2: Photocatalytic efficiency measured using SPC.  
Visual analysis showed the photocatalytic degradation of the RhB dye on the sample surfaces during UV-Vis irradiation. The functionalized samples showed a more effective degradation result. The control samples (G_Ref and W_Ref) showed a lower degradation efficiency than the functionalized samples due to the weaker photolysis effect.  
The photocatalytic efficiencies obtained by DIP are shown in Figure 4. The reference samples (G_Ref and W_Ref) maintained low photocatalytic efficiencies throughout the irradiation period, with values below 10%, which was expected since these samples had not been functionalized with photocatalytic materials. On the other hand, the white (W_T16) and gray (G_T16) functionalized cementitious substrates showed higher photocatalytic efficiencies, with results of 35% and 33%, respectively.  
The PE values obtained through DIP were approximately 13% and 7% higher than those measured by spectroscopy for W_T16 and G_T16, respectively. This may be attributed to the greater accuracy of the measurements. The DIP analysis considered the entire surface area of the samples. It is also important to highlight that DIP factors such as lighting conditions and image resolution directly influence the accuracy of the color coordinates  
Figure 4: Photocatalytic efficiency measured using DIP.  
Figure 3: Photocatalytic degradation of RhB in samples after irradiation under UV-Vis light.  
extracted from the images. To ensure consistency and comparability between DIP and spectroscopic methods, it is essential to ensure strictly controlled lighting conditions and optimize image resolution.
# Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network
## 2 Converter Cabinet System and Transfer Path Analysis Method  
### Converter Cabinet System  
The converter cabinet mainly relies on the transformer, which uses the fan to dissipate heat, so the main noise sources are transformers and fans. The fan and transformer in the converter cabinet need to be operational at the same time, and the fan air duct must enter from both sides and exit through the transformer from the middle. The air outlet is right under the electric reactor. Figure 1 depicts the converter cabinet's structural layout. The transformer is suspended on the left and right crossbeams through four rigid lifting eyes, and the fan is suspended on the same crossbeams through two rigid lifting eyes. The fan base is also connected to the bottom plate through four bolt holes. Zoom in on these installation locations in Figure 1, and as shown in Figure 2, the bolted connect locations between the converter cabinet, transformer, and fan are indicated by numbers 1 through 10.  
Figure 1: Schematic diagram of the converter cabinet structure.  
By analyzing the working principle and structural characteristics of the converter cabinet, the following main noise sources are identified. The fan produces strong eddy current noise while rotating, as well as flow noise at the flow channel's corners and variable cross-section, which is primarily transmitted from the air channel's inlet and outlet. Under the influence of an electromagnetic field, the transformer body produces electromagnetic force, which stimulates it to produce loud noise. The entire cabinet vibrates and emits noise when the transformer is operating owing to the magnetostrictive effect of the silicon steel sheet material used in the construction of the transformer, which is transmitted to the cabinet system through the bolt installation point. The fan vibrates when it is operating because it is periodically struck by airflow. This vibration is then transmitted to the cabinet's equipment via the bolts holding the fan in place, which increases cabinet vibration and emits noise.  
According to the different transmission path, the noise of the transformer and fan to the target point is divided into two categories: structural noise and air noise. When the transformer and fan work, they vibrate themselves and transmit vibration to the whole cabinet through their respective connected structures (beams and baseboards), and they radiate noise from the cabinet to the target point. The noise radiated by the vibration of the shell of the transformer and the fan during work or the aerodynamic noise generated during work is transmitted to the evaluation point through the flow channel or leaks from the structural gap to the target point, or it is transmitted from the cabinet wall panel to the target point (due to the strong sound insulation ability of the wall panel, the transmitted part can be ignored).
## 4 Controller Design
### RBFNN Adaptive Terminal Sliding Mode Controller
\begin{array}{cc}x_{1}&x_{2}\end{array} \big{]}^{\mathsf{T}}\), and the output is as follows:  
\[\hat{f}(\mathbf{x})=\tilde{\mathbf{W}}^{\mathsf{T}}\mathbf{h}(\mathbf{x}) \tag{34}\]  
Define error function  
\[\widetilde{f}(\mathbf{x})=f(\mathbf{x})-\hat{f}(\mathbf{x})=\mathbf{W}^{\mathsf{T}}\mathbf{h}(\bm {x})+\varepsilon(\mathbf{x})-\hat{\mathbf{W}}^{\mathsf{T}}\mathbf{h}(\mathbf{x})=\widetilde{ \mathbf{W}}^{\mathsf{T}}\mathbf{h}(\mathbf{x})+\varepsilon(\mathbf{x}) \tag{35}\]  
where \(\widetilde{\mathbf{W}}=\mathbf{W}^{\mathsf{T}}-\hat{\mathbf{W}}\) is the estimation error of the weight vector. **Assumption 5**. : \(f(\mathbf{x})\) _is time-varying and bounded; that is, there is an unknown constant \(f^{*}\), which satisfies the inequality \(|f(\mathbf{x})|\leq f^{*}\)._  
The control law and adaptive law of RBFNN parameters are designed as follows:  
\[u=-\frac{q}{p}\beta\varepsilon_{2}^{2-p/q}+\hat{a}+\hat{b}x_{2}+\varepsilon x _{2}^{2}-\hat{f}(\mathbf{x})+\tilde{x}_{d}-ks-\eta\mathrm{sgns} \tag{36}\]  
\[\hat{\mathbf{W}}=\Gamma\bigg{[}\frac{p}{q}\varepsilon_{2}^{p/q-1}sh(\mathbf{x})- \sigma_{4}\hat{\mathbf{W}}\bigg{]} \tag{37}\]  
where \(\Gamma=\Gamma^{\mathsf{T}}>0\) is an adaptive parameter matrix, and \(\sigma_{4}>0\) is a control parameter. **Theorem 1**. : _According to the train dynamics model described by Equation (5), under the Assumption of 1-5, an adaptive law (22) for the coefficients of the basic resistance Davis equation, a controller (36) for the system, and an adaptive law (37) for approximating the weight vector of the neural network with additional resistance disturbance are designed. When the Davis equation coefficients are uncertain, the additional resistance disturbance cannot be accurately modeled, the train can track the desired train operation curve in real time, the tracking error converges to a small enough neighborhood quantified by the control parameters, and the closed-loop system is stable. The initial state of the closed-loop system is as follows:_  
\[\frac{1}{2}s^{2}(0)+\frac{1}{2\lambda_{1}}\tilde{a}^{2}(0)+\frac{1}{2\lambda_ {2}}\tilde{b}^{2}(0)+\frac{1}{2\lambda_{3}}\tilde{c}^{2}(0)+\frac{1}{2}\Gamma ^{-1}\tilde{\mathbf{W}}^{2}(0)\leq\zeta\]  
_where,\(\zeta\) is an any positive constant, \(\beta\), \(p\), \(\eta\),\(\Gamma\lambda_{i}\) and \(\sigma_{i}\), \(i=1,2,3,4\) are control parameters._  
Proof. : **of Theorem 1.