esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Model Validation  
In order to validate the FSH coupling model, the journal bearing parameters given by [PERSON] et al. [19] were used to obtain the temperature distributions under different external loads. As shown in Figure 7, a comparison of the temperature distribution between the present model and the experimental data provided by [PERSON] et al. [19] was performed. A good agreement can be found between the predicted results and the experiment data. Verification of the thermal distribution of the bearing-lubricant-journal system was carried out by comparison with [PERSON]'s experiment results ([PERSON] et al. [17]), as shown in Figure 8, and the geometry, material, and lubricant parameters used in the presented model are consistent with those from [PERSON]'s experiment. It was observed that the maximum temperature predicted by the present model agrees well with that tested by [PERSON], and the distribution trends of the temperature obtained by the developed model agree well with those given in [PERSON]'s experiment. Therefore, the comparisons of the temperature distribution support the correctness of the developed thermal model.  
Figure 6: Flow chart for the numerical simulation process of the fluid–solid–heat (FSH) performance of the JWR system.  
Figure 7: Temperature analysis results in comparison with the experiment data from [PERSON] et al. [19] under different working conditions (6672 N and 1000 rpm).  
The proposed model was further validated by comparing the predicted load capacity with the experiment results from [PERSON] et al. (2018). Similarly, the geometry, material, and lubricant parameters used in the simulations were all the same as [PERSON] parameters. As shown in Figure 9, the load capacities under various eccentricity ratios agree well with those given in [PERSON] experiment ([PERSON] et al., 2018). Consequently, the comparisons above demonstrate the correctness of the present model, and therefore, it can be used to analyze the FSH coupling performance of the WLRSB.  
In this study, the basic parameters, including the dimensions, materials, thermal parameters, and surface texture of the JWR system, used in the proposed model are listed in Table 1.  
Figure 8: Structure temperature analysis results in comparison with the experimental data given in [PERSON] et al. (2017): (**a**) results obtained by the proposed model; (**b**) the experimental results.  
Figure 9: Comparison of simulated load capacity with experimental data of [PERSON] et al. (2018).
# Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication
## 6 Discussion  
Although our study mostly focuses on the Intel Xeon processor and Redis, it can be generally applied to different architectures and applications. With regard to the underlying processors, AMD uses a power management technology called AMD PowerNow1, in which fine and dynamic control of the voltage and frequency of the cores is possible [14]. Similarly, ARM processors, which have been tailored mostly for embedded systems powered by batteries but are now seriously considered for use in data centers, provide various built-in hardware design methods to reduce power usage levels [15]. They not only support the dynamic on-off switching of the cores (HotPlug) but also allow for modifications of the clock speed according to the computation requirements (DVFS). Furthermore, Amazon EC2 also supports processor state control (C-states/P-states) of the underlying physical machine for the several specific types of instances [41]. These processors all empower an application to optimize power and performance to best satisfy the user's requirements. With regard to applications, the relaxation of performance requirement for replicas is not unique in Redis. Distributed data systems that asynchronously maintain replicas, such as Mongodb [7], HDFS [10], and Ceph [9], share this property. Taking into account all of these aspects, we argue that our approach is timely and applicable in a wide range of circumstances, although the implementation methodologies may vary across different systems.
## 3 Experimental Results
### In Vitro Experiment
After the estimation of the relative rotation angle between two views (\(V_{\textit{rrf}}\), \(V_{\textit{corr}}\)), we conducted a 3D centerline reconstruction of the simulated blood vessel (copper wire) in the jig utilizing the proposed method in Section 2.3. And, we analyzed the accuracy of the 3D centerline reconstruction, as shown in Table 1. First, we analyzed the 2D errors of the reconstructed vascular map. The 2D position errors mean the distances between the final \(\textit{p}_{\textit{rrf}}\) that corresponds to the 596-pixel points of the 2D centerline and the projected pixel points of the actual coordinates of the 3D centerline for each \(I_{\textit{corr}}\). Second, we analyzed the 3D position errors of the reconstructed vascular map. The 3D errors mean the distances between the calculated 3D coordinates of every pixel point along the centerline and the  
Figure 5: A mock-up and a jig that simulates an MNS workspace and a blood vessel for an in vitro experiment. (**a**) Top view; (**b**) side view; and (**c**) photograph. actual 3D coordinates of a jig. After calculating the 2D and 3D errors, we calculated the maximum, minimum, and mean of them. According to the results, the 3D coordinates of the centerline pixel points indicated on the jig were calculated with the mean 2D errors between 0.073 mm and 0.125 mm and the mean 3D errors between 0.279 mm and 1.168 mm. The calculated 3D errors were within the diameter range of the femoral artery (8.2-9.8 mm) [23]. In conclusion, the overall pixel points of the centerline were accurately calculated considering the 2D and 3D position errors.
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 4 Exosome Contents May Serve as Biomarkers for the Pathogenesis and Treatment of Psoriasis  
Many biologically active cargoes or contents in exosomes are involved in the pathogenesis and treatment of psoriasis. Therefore, they have been used as a biomarker for predicting and evaluating the effectiveness of psoriasis treatment. [PERSON] et al. compared the expression of miRNAs in exosomes derived from the plasma of good responders (GRs) and non-responders (NRs) after methotrexate (MTX) therapy. They found that the expressions of miR191-5p and miR-21-5p were positively correlated with the severity of psoriasis. Importantly, they identified four microRNAs, including miR-199a-5p, miR-195-5p, miR196a-5p and miR-1246, which could potentially distinguish between GRs and NRs. These findings indicated that these four miRNAs in exosomes might be good biomarkers to predict the efficacy of MTX [83]. Additionally, [PERSON] et al. investigated the lipid composition of exosomes derived from the plasma of healthy donors (HD) and psoriaticatie patients treated with or without vustkeinumab, an inhibitor of IL-12/IL23 p40. They found that the phospholipid components of exosomes from the psoriaticatie patients showed an increase in the levels of phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol and lysoPC compared to those from HD. Moreover, therapy with vustkeinumab reverted the PE and PC levels in exosomes to those of HD. Thus, the levels of PE and PC in plasma exosomes may be used to assess the response of psoriatic patients to vustkeinumab treatment [84].  
The biologically active cargoes in exosomes can also be utilized to evaluate the effectiveness of traditional Chinese medicine (TCM) in psoriasis treatment. [PERSON] et al. explored the therapeutic effects of Yangxue Jiedu Soup (YJS) on imiquimod (IMQ)-induced psoriasis-like mice. The results indicated that YJS significantly inhibited Toll-like receptor 4 (TLR4) activation and NF-kB p65 translocation by suppressing the expression of heat shock protein 70 (HSP70) in exosomes isolated from the plasma, thus alleviating the skin lesions and inflammation in the psoriatic mice [85]. Another study reported by [PERSON] et al. confirmed that luteolin, a natural flavonoid ingredient, significantly relieved the lesions and symptoms of psoriasis by reversing the effects of IFN-\(\gamma\) and inhibiting the expression of HSP90\(\alpha\) and HSP90\(\delta\) within exosomes [86]. These findings imply that HSP in exosomes mediates the anti- psoriaticate effects of TCM and that it may serve as a biomarker for evaluating the therapy of psoriasis. Taken together, the contents of nucleic acids (microRNAs, such as miR-199a-5p and miR-195-5p), lipids, such as PC/PE, and proteins (eg. HSP90\(\alpha\)/\(\beta\)) in exosomes derived from the plasma of psoriasis patients or imiquimod-induced psoriatic mice are closely associated with the pathogenesis of psoriasis and their treatment efficacy.
## 4 Numerical Method for Solution  
The nonlinear coupled ordinary differential Equations (11) and (12) subject to boundary conditions (13) and (14) form the two-point boundary value problem and are solved numerically using the shooting method. In order to use the shooting method, first we convert (11) and (12) to a system of first order differential equations:  
\[f^{\prime}=p,\quad p^{\prime}=q,\quad\theta^{\prime}=z, \tag{18}\]  
\[q^{\prime}=\frac{1}{(a+A-A\theta)}\left[(1-\phi)^{2.5}(1-\phi+\phi\frac{\rho_ {s}}{\rho_{f}})(-\frac{1}{2}fq)+Azq+k^{*}(a+A-A\theta)(p-1)\right], \tag{19}\]  
\[z^{\prime}=\frac{1}{(1+\epsilon\theta)}\left[-\epsilon z^{2}-P_{r}(\frac{k_{ f}}{k_{n_{f}}})\left(1-\phi+\phi\frac{(\rho C_{p})_{s}}{(\rho C_{p})_{f}} \right)\left(\frac{1}{2}fz\right)\right]. \tag{20}\]  
The boundary conditions become:  
\[f(0)=S,\quad p(0)=1+\delta q(0),\quad\theta(0)=1+\Delta z(0). \tag{21}\]  
In order to solve the initial value problem (19)-(21) with the shooting method, we require an initial guess for \(q(0)\) and \(z(0)\). The required values of \(q(0)\) and \(z(0)\) are chosen randomly, and numerical solutions are obtained using fourth order Runge-Kutta method. The numerical values for \(q(0)\) and \(z(0)\) are adjusted using [PERSON]'s method to give better approximation to the solution. The step size is taken as \(0.01\), and the process is repeated until the solutions achieve the accuracy of \(10^{-6}\). To ensure the numerical accuracy, we have compared our results with the results of [PERSON] et al. [43] for velocity and temperature profiles with \(A=\epsilon=\phi=0,a=1,S=0.2,\delta=\Delta=0.1\) and \(P_{r}=0.3\). The comparison is shown in Figure 2 and is found to be in excellent agreement.  
The thermophysical properties of the base fluid and nanoparticles are given in Table 1
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 4 Details of the Implementation  
We now explain how we realized this algorithm, as it is not evident how to proceed when performing only digital image manipulations instead of using mathematical constructs. In particular, we describe the preprocessing of the data, some small sub-algorithms that are the fundamental building blocks of the shortest path algorithm, the data representation that we used, and some pseudo-code that expands the succinct description of Section 3.6.  
The data required for the algorithm is a digital 2D image of the terrain and the two points \(S\) and \(T\). This image would typically be a photograph of the scene from above or a slice of a 3D digital surface model. This image is then binarized and segmented, which sets to 1 all of the pixels with a value above a prescribed threshold and sets the other ones to 0, which produces an image that we call the \"Map\". The Matlab function \"_imbinarize_\" performs this task. The image Map can replace the image that has initially been provided in the computer memory since the latter is thereafter inutile.  
Before running the algorithm, we can determine if a shortest free path exists between \(S\) and \(T\). This can straightforwardly be done by labeling the blobs in the negative image of the Map, i.e., in not(Map), and checking whether the two points \(S\) and \(T\) belong to the same blob. If, and only if, they do there exists a path joining them. Obviously, if there are such paths, there exists a shortest path.
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 4 Performance Evaluation
### Performance Results
Figure 15: Spectrum utilization ratio under different primary user (PU) cars access network probabilities (PU\({}_{\text{number}}\) = 10, V = 10, SU\({}_{\text{number}}\) = 10). (3)  
The handover procedure is performed when the BS switches, the PU suddenly occupies the spectrum, or there is a transmission collision of the SU. If the spectrum handover is frequent, it means that the stability of the spectrum scanning mechanism is not good. From the results in Figures 19-21, we find that the more the numbers of SUs, the more handovers occur. The reason is that the SU competes for the spectrum or the transmission collision. Our cooperative assistance method has a lower total number of handovers because the passive handover mode is adopted. In addition, when switching to a new BS, the spectrum usage status of the new BS can be obtained in advance from the cooperative SU. Then the most suitable spectrum for handover is chosen. The random access method has the worst performance, because this method does not scan the spectrum, the access is performed directly, so collisions often occur, which leads to a rapid increase in the total number of handovers. When the moving speed of SU is faster, the total number of handovers will also increase. This is because SU will constantly switch BSs, so the SU is forced to do handover behavior. Our method has the lowest total number of deliveries because we consider the stability factor. When the PU often accesses the network, the total number of handovers will increase. This is because the SU may often transmit the data, and the PU will use the spectrum, which causes the SU to be forced to switch to other spectrums. Figure 17: Average scan time under different moving speeds (PU\({}_{\text{number}}\) = 10, P\({}_{\text{PUaccess}}\) = 0.5, SU\({}_{\text{number}}\) = 10). Figure 18: Average scan time under different PU cars access network probabilities (PU\({}_{\text{number}}\) = 10, V = 10, SU\({}_{\text{number}}\) = 10). Figure 21: The total number of spectrum handovers under different PU cars access network probabilities (\(\text{PU}_{\text{number}}=10\), \(\text{V}=10\), \(\text{SU}_{\text{number}}=10\)). Figure 19: The total number of spectrum handovers under different number of SU cars (\(\text{PU}_{\text{number}}=10\), \(\text{P}_{\text{PUaccess}}=0.5\), \(\text{V}=10\)). Figure 20: The total number of spectrum handovers under different moving speeds (\(\text{PU}_{\text{number}}\)=10, \(\text{P}_{\text{PUaccess}}\)=0.5, \(\text{SU}_{\text{number}}\)=10).
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 5 Fundamental Issues
### Safety and Risks  
As described in the previous sections, hydrogen can offer significant environmental benefits, but it also poses unique safety challenges and risks, particularly in storage [47]. Low ignition energy, wide flammability range, and high diffusivity lead to leaks and explosions if not properly managed (Section 1.2). High-pressure hydrogen storage, essential for applications such as FCEVs, increases these risks due to the potential for rapid pressure and temperature changes during refueling [40; 132; 181; 182]. Studies have shown that advanced storage technologies, such as Type III and IV composite cylinders, can mitigate some risks (Section 2). Still, issues like thermal stress and material fatigue remain concerns [135; 187]. Additionally, innovations like onboard cold thermal energy storage systems and phase change materials are being explored to enhance safety by managing temperature spikes during hydrogen filling [22; 181]. Ensuring hydrogen safety requires a comprehensive approach involving robust engineering solutions, stringent safety standards, and continuous research into material behavior and risk mitigation strategies [188].
## 5 Conclusions  
This study showed that the SCOPE in its inverted form can be used to retrieve the vegetation traits such as LAI for peatland vegetation, which is not easy to measure through traditional methods such as SunScan, whereas the SIF retrieved through SCOPE correlates well with measured SIF, indicating the possibility of using the SCOPE model for the purpose of retrieving SIF from peatland vegetation. Along with the application of the SCOPE, this is the first time we have shown the whole growing year spectral data along with SIF and LAI for peatland vegetation.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14164010/s1](https://www.mdpi.com/article/10.3390/rs14164010/s1)]([https://www.mdpi.com/article/10.3390/rs14164010/s1](https://www.mdpi.com/article/10.3390/rs14164010/s1)). Table S1: \(p\)-values of one-way analysis of variance for modeled leaf area index (LAI_mo), Normalize Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-Infrared Reflectance of vegetation (NIRv), and measured leaf area index (LAI_meas) in every day of measurements. The bold values present the statistically significant differences between treatments (control, warming, and warming and reduced precipitation) at alpha < 0.05. Table S2: \(p\)-values of t-test between CL and CR vegetation in every day of measurements. The bold values present the statistically significant differences between treatments at alpha < 0.05  
A.R.: Conceptualization, collection of data, Formal analysis, Investigation, Funding acquisition, Preparation of first draft; M.A.: Collection of data, Analysis, editing; E.P.: Analysis, review and editing; P.Y.: Analysis, review and editing; C.v.d.T.: Supervision, Review and editing; R.J.: Experimental design, Conceptualization, management, Review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the projects Polish National Agency for Academic Exchange (NAWA) Program im. Bekker grant no. PPN/BEK/2019/1/00090 and National Science Centre of Poland (NCN) grants 2016/21/B/ST10/02271 and 2020/37/B/ST10/01213. [PERSON] is supported by the National Natural Science Foundation of China (NSFC) under grant 42101349.  
Readers can obtain the datasets through request to corresponding author.  
The authors declare no conflict of interest.
# Residual Life Prediction of Gas-Engine Turbine Blades Based on Damage Surrogate-Assisted Modeling
## 2 Methods  
### GTE Model  
Figure 2 shows the architecture of the primarily used gas-turbine-engine model. The physics-based model of a gas turbine was separated into submodel blocks that were further sequentially connected. Each submodel is described by equations that define its physics. The applied-physics-based model uses a six-species gas model (oxygen, nitrogen, water vapor, carbon dioxide, argon, and fuel) and requires real operation data to define initial working conditions, such as atmospheric air temperature, pressure and humidity, and pressure in the chambers.  
Figure 1: Proposed methodology for surrogate model: usage scenario of surrogate model in real operation conditions. \(T_{atm}\), atmospheric air temperature; \(H_{atm}\), atmospheric air humidity; \(Q_{fuel}\), fuel consumption; \(t_{work}\), regime duration; \(W_{GTE}\), gas-turbine-engine power; \(COT\), compressor-outlet temperature; \(TIT\), turbine-inlet temperature; \(D_{zone}\), separately estimated damage for each critical zone.  
The atmospheric source on its own represents a submodel with zero input and constant thermodynamic conditions. The input parameters that it requires in order to calculate the specific enthalpy and capacity ratio are pressure, temperature, gas-species fraction, and molar mass. Under the first law of thermodynamics for an open system, a submodel of Cp volume (the chamber with constant volume) is then used to solve the variation of internal energy on the basis of the assumption that thermal losses to the environment are negligible [6; 8]. Next, the compressor submodel with a performance map that defines several steady-state regimes of the compressor performance was applied to evaluate enthalpy and outlet temperature. The combustion chamber was characterized by the same equations as those of the chamber submodel with a correction for some additional heat produced due to the occurring chemical reaction.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 4 Channel Sum-Capacity in LoS and Favorable Propagation Conditions
### High-SNR Regime  
For the high-SNR regime, we have the following approximation: \(\log_{2}(1+x)\approx\log_{2}(x)\), when \(x\gg 1\), then Equation (9) can be expressed as  
\[\begin{split} C&\approx K\mathbb{E}\left[\log_{2} \left(\frac{\rho M\eta}{d_{k}^{2}}\right)\right]\\ &=\frac{K}{2(R-R_{min})}\int_{1/R^{2}}^{1/R_{min}^{2}}\log_{2}( \rho M\eta z)\frac{1}{z\sqrt{z}}dz,\end{split} \tag{25}\]  
where \(z=\frac{1}{d_{k}^{2}}\). Solving Equation (25) with an integral solver [37], we find an exact closed-form expression for the capacity when the channel offers favorable propagation in the high-SNR regime. The exact closed-form expression for the approximated sum-capacity in Equation (25) is given by Equation (26).  
\[C\approx\frac{K}{(R-R_{min})}\frac{\log(\rho M\eta)(R-R_{min})+2\left[R-R_{min }-R\log(R)+R_{min}\log(R_{min})\right]}{\log(2)}. \tag{26}\]  
In Equation (26), differently from Equation (24), we see that the sum-capacity logarithmically increases with the average SNR, \(\rho\), and/or with the number of antennas, \(M\). Therefore, in the high-SNR regime, an increase in the transmit power and/or in the number of antennas, \(M\), is much less impressive than in the low-SNR regime case.
## 2 Background and Motivation
### Comparison between the Characteristics of Metro Trajectory and GPS Trajectory  
There are significantly different characteristics between metro trajectory and GPS trajectory. Details are listed in Table 1. The main reason for these differences lies in the data sampling methods of the two trajectories. Specifically, GPS trajectories usually use positioning devices for high-frequency data recording, so each GPS trajectory usually consists of thousands of trajectory points per trip. However, the metro trajectory is produced by the smart card records collected by the automatic fare collection (AFC), and each trip of metro trajectory has only two points, namely, the original and destination stations and the spatiotemporal information. In addition, due to the precision of GPS positioning devices, the GPS trajectory usually has serious noises, while the metro trajectory is very accurate. Thus, it is hard to apply GPS-oriented solutions to trajectory similarity searches in metro transportation systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Density of** \\ **Trajectory Point** \\ \end{tabular} & \begin{tabular}{c} **Noise** \\ **Level** \\ \end{tabular} & \
\begin{tabular}{c} **Spatiotemporal Characteristics** \\ **of Trajectory Point** \\ \end{tabular} &
\begin{tabular}{c} **Semantic** \\ **Degree** \\ \end{tabular} \\ \hline Metro & & & & Limited in spatial & Weak \\ Trajectory & & & & Limitless in temporal & \\ GPS & & & Limitless in spatial & \\ Trajectory & & & Limitless in temporal & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic differences between metro trajectory and GPS trajectory.  
Figure 1: The difference between trajectory spatiotemporal intersection (TSI) and trajectory similarity search (TSS).