esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 3 MC-BDP Reference Architecture
### MC-BDP Architectural Layers
#### 3.4.8 The Monitoring Layer  
The monitoring layer consists of services aimed at providing metrics related to the performance of specific components. Since diverse aspects of a system can be, and usually are, monitored, this layer is also orthogonal to the others, and would likewise benefit from a systematic approach such as a multi-layer and multi-purpose framework. Separate advances have been made to provide a monitoring framework for cloud systems [63] and for big data systems [85]. However, this study believes an integrated approach similar to [62]'s CCAF is more suitable to the domain of big data in the cloud, since monitoring incorporates diverse aspects pertaining to different layers.  
As an example, MC-BDP's prototype implementation utilised, at the application level, the Flink metrics functionality of the Flink framework [86]. At the orchestration layer, there were two monitoring perspectives employed: swarm monitoring and Prometheus monitoring. Swarm monitoring provided a terminal-based interface through which the state of the cluster could be monitored. Information, such as which nodes were part of the overlay network and which joined the cluster and which containers were running which services, was available by running simple command queries [87]. Prometheus monitoring, implemented as part of a Weave Cloud installation, provided similar information, but displayed it in a more user-friendly way through a graphical user interface provided by Weave Cloud [64].  
At the networking layer, monitoring for the MC-BDP prototype was provided by the Weave command line interface, which showed the nodes connected to the overlay network and their status [81]. At the container level, information such as the status of each container, the services they were running, and CPU, memory, and network usage were monitored by three different technologies: Prometheus, Weave Cloud, and Azure Container Monitoring. Essentially, Weave Cloud provided an easy-to-use and aesthetically pleasing user interface for Prometheus, on which it is based. Prometheus queries were also run directly to obtain more fine-grained data. Finally, the Azure Container Monitoring service was used to obtain and analyse container performance metrics.  
At the virtual machine level, Weave Cloud was used in combination with Azure Log Analytics. At the persistence level, Azure storage analytics was used for monitoring the Azure database commissioned as a service. Finally, at the messaging level, the Confluence Control Centre was used to monitor the health of the Kafka server [88].
# Identification of Novel Markers of Prostate Cancer Progression, Potentially Modulated by Vitamin D
## 3 Results
### Immunohistochemistry Analysis of the Six PCa Genes Selected
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Protein** & **State** & **Antibody** & **Staining** & **Intensity** & **Quantity** & **Localization** & **Cells** & **Biopsies** & **Patient ID** \\ \hline \multirow{2}{*}{**PRSS8**} & Healthy & Low & Moderate & \textless{}25\% & Cytoplasm/membonous & Standard cells & 3 & 2472 \\ \cline{3-10}  & HGPAC & Medium & Moderate & 75\%–25\% & Cytoplasm/membonous & Tumor cells & 4 & 3563 \\ \hline \multirow{2}{*}{**SOX4**} & Healthy & \multirow{2}{*}{HPAQ29901} & Low & Weak & 75\%–25\% & Nuclear & Standard cells & 3 & 2472 \\ \cline{3-10}  & HGPAC & Medium & Moderate & \textgreater{}75\% & Cytoplasm/membonous/nuclear & Tumor cells & 7 & 3570 \\ \hline \multirow{2}{*}{**SMYD2**} & Healthy & \multirow{2}{*}{HPAQ29023} & Medium & Moderate & \textgreater{}75\% & Cytoplasm/membonous & Standard cells & 3 & 2472 \\ \cline{3-10}  & HGPAC & Low & Weak & 75\%–25\% & Cytoplasm/membonous & Tumor cells & 1 & 2825 \\ \hline \multirow{2}{*}{**MCCC2**} & Healthy & \multirow{2}{*}{HPAQ16546} & High & Strong & \textgreater{}75\% & Cytoplasm/membonous & Standard cells & 3 & 2472 \\ \cline{3-10}  & HGPAC & High & Strong & \textgreater{}75\% & Cytoplasm/membonous & Tumor cells & 12 & 2816 \\ \hline \multirow{2}{*}{**CCNG2**} & Healthy & \multirow{2}{*}{HPAQ304684} & Low & Moderate & \textless{}25\% & Cytoplasm/membonous & Standard cells & 3 & 2472 \\ \cline{3-10}  & HGPAC & Medium & Moderate & \textgreater{}75\% & Cytoplasm/membonous & Tumor cells & 2 & 3978 \\ \hline \multirow{2}{*}{**CD2 AP**} & Healthy & \multirow{2}{*}{HPAQ03267} & High & Strong & \textgreater{}75\% & Cytoplasm/membonous & Standard cells & 3 & 2053 \\ \cline{3-10}  & HGPAC & High & Strong & \textgreater{}75\% & Cytoplasm/membonous & Tumor cells & 15 & 250 \\ \hline \hline \end{tabular} HGPAC: High grade prostatic adenocarcinomas. Healthy: normal prostate tissue biopsies. The % indicates the fraction of stained cells (true, 25%, 25-75%, or >75%).  
\end{table} 
Table 1: Immunostaining details recovered from The Human Atlas Project.
## 4 Case Study and Discussion
### Case Study
As shown in Table 4, identifiers of static nonlinearity type are found to perform poorly on the IFGD identification problem. Specifically, the BP network and the Hammerstein model performed similarly in all four seasons. In comparison, dynamic models can achieve better identifying performance. Among static and dynamic models, by choosing the best results for the four seasons separately, it is found that the score of MAPE (RMSE) is reduced from 0.0126 to 0.0041 (0.0801 to 0.0321) in spring, 0.0103 to 0.0039 (0.0761 to 0.0299) in summer, from 0.0109 to 0.0031 (0.0775 to 0.0236) in autumn, and from 0.0102 to 0.0025 (0.0758 to 0.0199) in winter. Overall, in all four seasons, the TCN model outperforms the other three models that appeared in the previous literature on IFGD identification in terms of MAPE and RMSE. Furthermore, with the proposed BEMD-TCN approach, the identification performance can be further improved, which has the minimum performance metrics, consequently, we can draw the conclusion that the BEMD-TCN is effective and can be well applied in the IFGD identification problem.
# Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
## 4 Results
### Verification and Evaluation of Industry 4.0 Index
#### 4.3.1 Industry 4.0 Index Distribution
We proved the existence of a linear dependence, which was also proven by the positive Pearson correlation coefficient (0.5277). At the same time, in terms of Spearman correlation, where the \(p\)-value was very close to zero with \(R\) = 0.5147, the null hypothesis was rejected in favor of H2\({}_{\text{A}}\) on the dependence of both variables. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Median w1** & **Median w2** & **W** & **\(p\)-Value** \\ \hline VPi4\% total & 44.39 & 49.65 & 8230.0 & 0.1431 \\ VPi4\% level 1 & 55.33 & 57.72 & 8898.0 & 0.6611 \\ VPi4\% level 2 & 47.48 & 53.49 & 8134.0 & 0.1070 \\ VPi4\% level 3 & 27.30 & 33.19 & 7741.0 & 0.0267 \\ Enterprise perception & 2.00 & 3.00 & 6771.5 & 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Industry 4.0 index (VPi4) distribution, using Mann–Whitney–Wilcox test. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Pearson**} & \multicolumn{3}{c}{**Spearman**} \\ \hline
**Research** & **Variable** & **Perception** & **VPi4** & \(p\)**-Value** & **Perception** & **VPi4** & \(p\)**-Value** \\ \hline \multirow{2}{*}{Wave 1} & Perception & 1.0000 & 0.5277 & \multirow{2}{*}{0.0000} & 1.0000 & 0.5147 & \multirow{2}{*}{0.0000} \\  & Index VPi4 & 0.5277 & 1.0000 & & 0.5147 & 1.0000 & \\ \hline \multirow{2}{*}{Wave 2} & Perception & 1.0000 & 0.4129 & \multirow{2}{*}{0.0001} & 1.0000 & 0.4054 & \multirow{2}{*}{0.0001} \\  & Index VPi4 & 0.4129 & 1.0000 & & 0.4054 & 1.0000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relation to subjective perception of Industry 4.0, based on Pearson and Spearman coefficients. Similarly, a second questionnaire survey was used for the second wave of the research (Table 5). The coefficient of determination was lower than in the first wave of the research (\(R^{2}=0.1705\)). Dependence, therefore, explained only 17% of the variability. In the normality verification by the Shapiro-Wilcox test, the values were low to zero in both cases. Therefore, the normality of data, for the VPi4 index and the perception values of Industry 4.0 by the enterprises, was not considered. It was, therefore, better to compare the dependence of the Spearman coefficient. Its value was 0.4054, i.e., compared to the results in the first wave, the level of dependence was lower. Its value was, however, statistically significant. Given the proven dependence in both surveys, it was possible to conclude the correct setting of VPi4 by means of factor analysis and the suitability of the questions, as it largely corresponded to the perception of the enterprises in terms of Industry 4.0.
# Moon Sensor Station to Improve the Performance of Lunar Satellite Navigation Systems
## 3 Differential Reference Station
Equation (15) indicates that the magnitude of the satellite position error, \(\left|\Delta\mathbf{R}\right|\), is reduced by a factor of \(\frac{\left|\Delta\mathbf{r}\right|}{R}\) and the variance of the pseudorange error, after differentiating, becomes:  
\[\sigma_{diff}^{2}=\left(\frac{\left|\Delta\mathbf{R}\right|}{R}\sigma_{S-p} \right)^{2}+2\cdot\sigma_{RX}^{2}+2\cdot\sigma_{M}^{2} \tag{16}\]  
This last equation allows for the computation of the error budget for the CRLB computation in case of DRS, by substituting Equation (7) in the formulation of the problem. Also, for the DRS, the performance can be estimated using Monte Carlo trials, with the following main steps:  
* Trajectory and Sensor Simulation: nominal satellite, user, and local sensor positions are generated. * Error Injection: Gaussian, uncorrelated noise is added to both the satellite and local sensor positions. * Pseudorange Generation: user and sensor pseudoranges are computed with the inclusion of receiver clock bias and other error sources (receiver noise, multipath, satellite clock), all modeled as independent Gaussian noise. * Differential Correction: the differential corrections are computed and applied to the user pseudoranges. * Position Estimation and Analysis: the iterative WLS algorithm is used for position estimation at each trajectory point, and \(N\) trials are conducted per point to evaluate the RMS error. Finally, it must be noted that on the Moon, the ionospheric and tropospheric effects are negligible, and this approach can have higher performance than on Earth. In fact, neglecting the ionospheric and tropospheric errors, the most significant error contributions are given by the satellite errors (position and time) that are usually much more correlated in space and time, resulting in higher accuracy and higher coverage at the end of the process. Figure 2: Vector relationship for the satellite position error computation in the differential approach.
## 2 Load Sensitivity Analysis
### Handling Quality Evaluation Using TB Factor  
The objective and subjective handling quality evaluation is an important part of chassis control development. There are a several studies regarding a suitable methodology to consider the subjective evaluation by the driver of the vehicle responsiveness and controllability. According to [PERSON] [25], the vehicle motion response time is an important vehicle dynamics parameter for evaluation of vehicle controllability. [PERSON] et al. proposed several handling quality evaluation methods [26]. Here the handling quality of the vehicle is evaluated based on the performance index called \"TB factor\". This index combines the evaluation of both responsiveness (time to peak of the yaw rate response to steering wheel angle step input) and controllability (steady-state side slip angle with respect to unit lateral acceleration) into one index. According to driving simulator studies the handling quality becomes worse with increasing value of the TB factor [26].  
\[TB=t_{p}\left(\frac{G_{\beta}^{\delta}(0)}{G_{\dot{y}}^{\delta}(0)}\right), \tag{13}\]  
\[\frac{G_{\beta}^{\delta}(0)}{G_{\dot{y}}^{\delta}(0)}=\frac{l_{r}}{V^{2}} \left(1-\frac{ml_{f}}{2 ll_{r}K_{r}}V^{2}\right) \tag{14}\]  
where, the time to peak of the yaw rate response can be calculated as follows  
\[t_{p}=\frac{1}{\omega_{n}\sqrt{1-\xi^{2}}}\left(\pi-\arctan\left(\frac{\sqrt{ 1-\xi^{2}}\omega_{n}T_{r}}{1-\xi\omega_{n}T_{r}}\right)\right). \tag{15}\]  
Table 2 shows the values of representative vehicle dynamics parameters relevant to the handling quality of the vehicle. Based on the values in Table 2 it can be concluded that the TB factor is increasing with increasing load which implies worse handling quality.  
Figure 3: The frequency response of (**a**) lateral acceleration and (**b**) yaw rate with respect to the front steering angle at the velocity of 100 km/h.
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 3 Validation
### Feasibility of the Separation of Modulus and Damping Contributions
#### 3.1.2 Estimation of the Relative Weight of Nonlinearities  
As discussed in the previous subsection, the method was proven to be efficient in qualitatively identifying the functional dependence of \(c\) and \(\alpha\) on strain. This is useful to support the development of physical models, aiming to describe nonlinearity. Furthermore, this approach in principle allows comparing nonlinearities in different media studied experimentally in the same conditions, with obvious implications for materials characterisation and particularly for quantitative non destructive testing.  
To demonstrate the feasibility of such analysis, we demonstrate here the proportionality between the true nonlinear parameters (input of simulations) and the corresponding nonlinear coefficients of the fitting of Equation (17). To this purpose, velocity and attenuation coefficient variations vs. amplitude \(A\) are plotted, in log-log scale, in Figure 4 for different cases.  
Results reported in Figure 4 show that:  
* The curves are straight lines with slope 2 (we recall that the slope in log-log scale of a power law function corresponds to the power law exponent), except for velocity in Case 4. Indeed, in this case, the modulus dependence on strain is not a power law (see also Equation (12)). It is important to observe that in this case the power law quadratic dependence of damping is still well reconstructed by the analysis.
* Cases 5 and 6 differ from Case 3 only because the nonlinear parameters are scaled: \(\delta_{S}\to k\delta_{S}\) and \(\delta_{\lambda}\to k\delta_{\lambda}\). In both cases, the slope of the curves describing \(c\) and \(\alpha\) vs. amplitude remains 2 (as expected). Furthermore, the vertical shift of the curves of a quantity \(\Delta\) with respect to that of Case 3 preserves the proportionality with respect to the theoretical nonlinear coefficients. Indeed, results of the fit show that for each of the two cases \(\Delta\approx 20 log_{10}(k)\). Therefore, monitoring nonlinearity  
Figure 4: Comparison of results for increasing nonlinear coefficients \(\delta_{S}\), \(\gamma_{S}\) and \(\delta_{\lambda}\), as shown in Table 1. Plots of \(\Delta c/c_{0}\) and \(\Delta\alpha/\alpha_{0}\) vs. amplitude \(A\), in log-log scale. Solid lines correspond to the best fitting obtained using the function defined in Equation (17). The same colours were used in the two subplots and are related to the cases reported in the legend.  
evolution as a function of time, in samples subjected to progressive damage, is feasible with the proposed approach.
## 5 Conclusions and Clinical Implications  
From a clinical perspective, the findings of this investigation indicate that HTO patients do not present with improvements in postural performance following surgery and rehabilitation. Therefore, similar to ACL surgery and rehabilitation [16], the rehabilitation program should implement unexpected disturbances in order to improve feedforward mechanisms. This could contribute to improvements in the somatosensory and cerebellar systems. For example, [PERSON] et al. [49] described and examined a rehabilitation concept using unexpected cues that cause a response time of less than 200 ms five-months after an ACL reconstruction. The study results identified the IBS as a scientific evaluation and a useful posturographic assessment in terms of HTO surgery and following rehabilitation. The center of pressure measurement using IBS allows for a functional distinction in postural stability of HTO patients and asymptomatic subjects. Based on these study results and our extensive experiences, the IBS might support the work of physicians and physical therapists and adjust rehabilitation demands in a more precise and evidence-based way.  
The outcomes of this investigation indicate that patients with medial tibofemoral osteoarthritis have deficits in postural stability and regulation. HTO and rehabilitation is not able to generate substantial improvements in these parameters during the observed period of six months. Sole overload of the injured side foot decreased significantly over the six months after HTO. In contrast, pain perception and quality of life (physical component) significantly improved after HTO and rehabilitation.  
Conceptualization, R.S., K.B., T.B. and M.J.; methodology, R.S., K.B. and M.P.; formal analysis, K-S.D., R.S. and K.G.L.; investigation, K.B., M.J. and R.S.; resources, T.B.; data curation, R.S.; writing--original draft preparation, R.S., M.J., K.B. and K.G.L.; writing--review and editing, R.S., K.G.L. and K.-S.D.; visualization, R.S.; supervision, M.P.; project administration, T.B., K.B. and R.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We are grateful to all the patients, who participated after HTO surgery and made this study possible.  
The authors declare no conflict of interest.
## 8 Relevant Applications of Melt Electrospinning
### Biomedical Applications and Tissue Engineering
Another promising application of melt electrospinning is the fabrication of skin replacements as burns and wounds have low regeneration capability. [PERSON] et al. [242] prepared a PCL blend scaffold with bioactive milk proteins for potential applications in deep skin tissue regeneration. Whey protein (WP) and lactoferrin (LF) were chosen as the milk proteins for this study, not only because of their bioactivity, but because of their specific properties. For example, WP acts as an antioxidant, anti-tumour, antiviral, and antibacterial agent. Another property WP possesses is the capacity to promote cell proliferation and osteogenic differentiation [243]. WP/PCL blend shows lower mechanical strength than PCL, which suggests that this blend may be suitable for soft tissue engineering applications [244]. On the other hand, LF is thought to have an innate immunomodulatory of effect [245]. To investigate if tissue regeneration was feasible using these scaffolds, human adult low calcium temperature (HaCaT) keratinocytes and neonatal human dermal fibroblasts were seeded onto them and incubated for 21 days. Results showed that milk proteins  
Figure 15: Scaffolds for advance functional human myocardial tissue formation. Representation of the workflow and fabricated microfiber scaffolds. (**a**) Designed hexagonal microstructure as a 3D fiber scaffold. The combination with iPSC-CMs combination has in vivo application through minimally invasive delivery. Additionally, optical image of the fabricated scaffolds: detail of microstructure with hexagonal cells (with a side length of 400 \(\upmu\)m) composed of multiple stacked aligned microfibers. Images acquired from top perspective. (**b**,**c**) Mechanical behavior of the fiber scaffolds under tensile loading. Corresponding deformed microstructures at different strain levels for both microstructures: top, rectangular, and down, hexagonal microstructures (**b**). Representation of the mechanical parameters determined from the engineering stress–strain curves, where: \(\varepsilon_{d}\) is the elastic limit strain, \(U_{0}^{el}\) is the elastic strain energy density, and \(E_{T}\) is the tangent modulus (**c**). (**d**–**g**) Injectability and in vivo placement of cardiac patch with hexagonal geometry. In-vitro culture of cardiac patch consisting of iPSC-CMs in cardiac-like ECM on large hexagonal scaffolds. Application and shape recovery of cardiac patch on beating porcine heart (**d**). Cell viability of in vitro noninjected (**e**) and injected (**f**) cardiac patches. Spontaneous beating rate of in-vitro noninjected and injected cardiac patches 30 min and 2 d after injection. * Significantly different (\(p<0.05\)) (**g**). Copyright 2018 John Wiley & Sons [237]. enhanced tissue regeneration and thus, it makes a suitable tissue engineering construct for full thickness dermal skin wounds [242].
###### Abstract  
In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky-Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH)Pl plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.  
paddy rice; object-based; Google Earth Engine; Sentinel-1/2; monsoon region +
Footnote †: journal: remote sensing