esa-sceva/satcom-chunk-collection huggingface dataset:
_J. Sens. Actuator Netw._**2012**, \(1\), 123-152; doi:10.3390/jsan1020123  
_Journal of Sensor and Actuator Networks_  
ISSN 2224-2708  
www.mdpi.com/journal/jsan/  
_Review_  
**Wireless Industrial Monitoring and Control Networks:**  
**The Journey So Far and the Road Ahead**  
**[PERSON] \({}^{*}\), [PERSON], [PERSON] and [PERSON]**  
Pervasive Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands;  
E-Mails: [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (K.D.); [EMAIL_ADDRESS] (P.H.)  
\({}^{*}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +31-53-489-3798.  
_Received: 23 July 2012; in revised form: 20 August 2012 / Accepted: 28 August 2012 / Published: 31 August 2012_  
**Abstract:** While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today's industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks.  
**Keywords:** WirelessHART; ISA100.11a; WIA-PA; real-time; industrial; automation
# Influence of Food Pigments and Thermal Aging on the Color Stability of Denture Base Resins
## 5 Conclusions  
Based on the findings of this study, it can be concluded that the color stability of denture acrylic resins is influenced by external factors such as beverage pigments and thermal variations, rather than by the type of resin used. Among the beverages tested, red wine caused the most pronounced chromatic changes due to its acidic pH, dark pigment, and ethanol content, which increased the resin's susceptibility to discoloration. Coffee and the carbonated soft drink also caused chromatic changes, albeit to a lower extent, while water, used as a control, caused minimal alterations due to the hydrophilic nature of the resins.  
Thermal aging through thermocycling emerged as a significant factor affecting color stability in the present study. The simulation of one month of clinical use with 1000 temperature-alternating cycles probably led to microfractures, polymer matrix degradation, and increased water absorption, all of which contributed to pigment penetration and discoloration. This underscores the importance of considering thermal stress when evaluating the long-term performance of acrylic resins.  
The results showed no statistically significant differences between resin types after thermocycling and beverage immersion. Although the self-cured resin S V4 demonstrated a tendency for greater chromatic changes due to its higher residual monomer content and increased surface porosity, this trend did not reach statistical significance. The intrinsic pigmentation of the resins may have played a protective role, reducing pigment absorption and contributing to the absence of significant differences.  
Despite the inherent limitations of an in vitro study, including the absence of factors like brushing, abrasion, and enzyme activity, this research highlights the key mechanisms of chromatic instability in acrylic resins. Future studies should explore longer immersion periods, additional resin brands, and more realistic conditions to provide a more comprehensive understanding of the long-term performance of dental prostheses in clinical settings.  
Conceptualization, B.C., C.B.N. and J.C.R.; methodology, B.C. and C.B.N.; software, V.A. and J.C.R.; validation, V.S., C.B.N. and V.A.; formal analysis, C.B.N.; investigation, B.C., C.B.N. and V.S.; resources, C.B.N.; data curation, C.B.N.; writing--original draft preparation, B.C.; writing--review and editing, V.A.; visualization, V.A. and J.C.R.; supervision, C.B.N. and V.S.; project administration, C.B.N. and V.S.; funding acquisition, C.B.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author/s.  
The authors gratefully acknowledge the support from FCT-Fundacao para a Ciencia e Tecnologia (Portuguese Foundation for Science and Technology), through IDMEC, under LAETA, project UIDB/50022/2020.  
The authors declare no conflicts of interest.
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
## 3 Analytical Time-Constrained Reentry Guidance  
Hypersonic vehicle reentry flight can be divided into the initial reentry phase, the quasi-equilibrium glide phase, and the terminal energy management phase. The hypersonic reentry time cooperative guidance studied in this paper mainly refers to the consistency of the cooperative arrival time from the start of reentry to the end energy management phase. The hypersonic reentry guidance command solution can actually be seen as finding a suitable AOA and bank profile, accurately hitting the target while optimizing the performance index. Compared with the traditional single-aircraft guidance problem, the reentry cooperative guidance of multi-hypersonic vehicles increases the constraint of time coordination.
## 5 Results
### Scenario with ASI Interference  
ASI interference, calculated using Equation (18), is added to determine its impact on ideal end-to-end performance (Equation (27)). The results are shown in Table 4.  
\[\left[\frac{\text{C}}{\text{N}_{0}}\right]_{\text{Total}}^{-1}=\left[\frac{ \text{C}}{\text{N}_{0}}\right]_{\text{up}}^{-1}+\left[\frac{\text{C}}{\text{ N}_{0}}\right]_{\text{Down}}^{-1}+\left[\frac{\text{C}}{\text{ASI}} \right]^{-1} \tag{27}\]  
The decrease in [C/N] and [E\({}_{\text{b}}\)/N\({}_{0}\)] suggests a reduction in the signal quality and an increased likelihood of errors in the received data. The magnitude of the degradation, around 1 dB to 1.2 dB, is significant and should be considered in the overall system design and interference mitigation strategies to ensure reliable communication.  
In the next section, the impact of the MVDS broadcaster on the received signal at ES4 is studied.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CaseRatio** & \(\left[\frac{\text{C}}{\text{N}_{0}}\right]_{\text{total}}\) & \(\left[\frac{\text{C}}{\text{N}}\right]\) & \(\left[\frac{\text{E}_{\text{b}}}{\text{N}_{0}}\right]\) \\ \hline Ideal case (no interference) & 71.2 dB-Hz & 6.4 dB & 4.9 dB-Hz \\ Real case with ASI & 70.2 dB-Hz & 5.4 dB & 3.7 dB-Hz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for end-to-end calculations.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.1 Virtual Network Request Acceptance Ratio  
The virtual network request acceptance ratio is one of the most important metrics for evaluating the VNE algorithm. Figure 3 reflects the result that the virtual network request acceptance ratio of each algorithm changes with the virtual network request arrival ratio. As can be seen from the figure, as the number of VNRs per unit time increases, the request success ratio gradually decreases. This is consistent with our intuitive prediction because the substrate network resources are limited. With the increase in the number of virtual network requests, the acceptance ratio will inevitably decline. Obviously, our algorithm has a better acceptance ratio than other algorithms. When the virtual network request arrival ratio is 2 per 100 time units, our algorithm and that of [14] have acceptance ratios of 98.977 and 97.098%, respectively, while other algorithms are below 80%. Our algorithm shows the best acceptance ratio at any arrival ratio, with a maximum improvement of about 15% compared with other algorithms. Ref. [14] performs well when the arrival ratio of virtual network requests is low, but the acceptance ratio decreases as the arrival ratio increases. This is because the location constraints limit the range of solutions, which causes some virtual nodes to have no suitable candidate nodes to embed, thus reducing the virtual network requests' success ratio to a certain extent.  
Figure 3: VNR acceptance ratio under various VNR arrival ratios.
## 3 Results
### Spatial-Temporal Co-Clustering Result Analysis
The southeast monsoon blows from the Pacific to the SCS, bringing relatively warm air. Tropical currents flow from the Pacific to the SCS, bringing relatively warm water, both of which help maintain a higher SST. The third category may be a transition zone between the cold and warm water regions, possibly affected by two different climatic and oceanographic processes, and therefore have a moderate SST. In summer, the SST in the SCS is divided into five categories. The SST between the southernmost and northernmost parts of the study area exceeds 5 degrees. We have summarized the following reasons for this. First, the geographical position of the SCS within the monsoon-impacted zone of Southeast Asia renders it susceptible to the southwest  
Figure 4: Spatial–temporal co-clustering results for the SST data between 2003.12–2004.11. The four time clusters correspond to the four seasons, and each season displays unique spatial pattern represented by colors. monsoon's quiet warmth and humidity, predominantly during the summer. Consequently, the southwest part of the SCS exhibits an enhanced SST. Conversely, the northern maritime region, inclined towards the cooler northeast monsoon, features a relatively reduced SST [41]. Second, ocean circulation profoundly influences the SST disparities across the different areas within the SCS. The dominant ocean streams within the SCS include the West Drift, the North Equatorial Stream, and the SCS Vortex. These ocean currents' strength and direction are perpetually changing in correlation with temporal and spatial shifts, resulting in the marked contrast of SST across diverse sea area within the SCS. In conclusion, the pronounced disparity in summer SST distribution in the SCS is attributed to several factors, such as the monsoon, ocean circulation, depth, topography, and monsoon precipitation, resulting in significant variations in SST distributions. The seasonal characteristics of the SST in the SCS have direct and indirect effects on global climate patterns. The monsoon system not only affects the climate in Asia, but also affects distant climate systems through air-sea interactions. Seasonal characteristics in heat and humidity in the SCS can spread through atmospheric circulation and affect global climate patterns. In addition, the seasonal characteristics of the SST in the SCS have an important impact on the distribution and diversity of marine life. SST and salinity changes in different seasons affect the reproduction, migration, and distribution of marine life. SST changes in the SCS are particularly important for coral reef ecosystems. Excessively high water temperatures can cause coral bleaching and cause damage to coral reef ecosystems. By understanding these influential variables, we can enhance our understanding of the SST variation within different regions of the SCS. This comprehension is crucial for unraveling the intricate marine climatology of the region.
## 4 First and Conventional Quadratic Edge Elements  
In the edge-based FE method, we can use regular rectangular, general tetrahedral, general hexahedral, or other complex elements to discretize the modeling domain. To simplify the problem and simulate bathymetry, hexahedral elements were used to discretize the modeling domain in this study.  
To calculate the stiffness matrix of the hexahedral element, a general hexahedral element should be transformed into an origin-centered cubic element. A general hexahedron in Cartesian coordinates \(xyz\) is shown in Figure 1a, and a transformed reference domain (cubic element) in Cartesian coordinates \(\xi\zeta\eta\) is shown in Figure 1b.  
The transformation can be described by the following formulas [25]:  
\[x =\sum_{i=1}^{8}N_{i}^{e}(\xi,\eta,\xi)x_{i}^{e} \tag{17}\] \[y =\sum_{i=1}^{8}N_{i}^{e}(\xi,\eta,\xi)y_{i}^{e}\] (18) \[z =\sum_{i=1}^{8}N_{i}^{e}(\xi,\eta,\xi)z_{i}^{e} \tag{19}\]  
where \(N_{i}^{e}\) is the scalar node-based shape function, which can be defined as follows:  
\[N_{i}^{e}(\xi,\eta,\zeta)=\frac{1}{8}(1+\xi\xi_{i})(1+\eta\eta_{i})(1+\zeta\xi _{i}) \tag{20}\]  
where \(i\) is a local node index of the element.  
Figure 1: (**a**) Hexahedron element in Cartesian coordinates \(xyz\); (**b**) the transformed cubic element in Cartesian coordinates \(\xi\zeta\eta\).  
In the analysis of the vector finite element, two volume integrals (12)-(14) must be evaluated. For a hexahedron element, it is easy to transform with two integrals in the \(\xi\eta\xi\)-coordinate using the Jacobian transform:  
\[K_{i,j}^{e}=\int_{-1}^{1}\int_{-1}^{1}\int_{-1}^{1}(\
abla\times\mathbf{N}_{i}^{ e}(\xi,\eta,\zeta))\cdot\Big{(}\
abla\times\mathbf{N}_{j}^{e}(\xi,\eta,\zeta) \Big{)}|J(\xi,\eta,\zeta)|\mathrm{d}\xi\mathrm{d}\eta\mathrm{d}\zeta, \tag{21}\]  
\[M_{1i,j}^{e}=\int_{-1}^{1}\int_{-1}^{1}\int_{-1}^{1}\mathbf{N}_{i}^{e}(\xi, \eta,\zeta)\cdot\boldsymbol{\sigma}\cdot\mathbf{N}_{j}^{e}(\xi,\eta,\zeta)|J( \xi,\eta,\zeta)|\mathrm{d}\xi\mathrm{d}\eta\mathrm{d}\xi, \tag{22}\]  
\[M_{2i,j}^{e}=\int_{-1}^{1}\int_{-1}^{1}\int_{-1}^{1}\mathbf{N}_{i}^{e}(\xi, \eta,\zeta)\cdot\Delta\sigma\cdot\mathbf{N}_{j}^{e}(\xi,\eta,\zeta)|J(\xi, \eta,\zeta)|\mathrm{d}\xi\mathrm{d}\eta\mathrm{d}\zeta, \tag{23}\]  
\(\boldsymbol{J}(\xi,\eta,\zeta)\) and \(|J(\xi,\eta,\zeta)|\) denote the Jacobian matrix and determinant of \(\boldsymbol{J}(\xi,\eta,\zeta)\), respectively.  
\[J(\xi,\eta,\zeta)=\begin{bmatrix}\frac{3\alpha}{\delta\zeta}&\frac{3\vartheta} {\delta\zeta}&\frac{3\alpha}{\delta\zeta}\\ \frac{3\alpha}{\delta\eta}&\frac{3\vartheta}{\delta\eta}&\frac{3\alpha}{ \delta\eta}\\ \frac{3\alpha}{\delta\zeta}&\frac{3\vartheta}{\delta\zeta}&\frac{3\alpha}{ \delta\zeta}\end{bmatrix} \tag{24}\]  
In the next section, we introduce several conventional first-order and quadratic hexahedral elements and two quadratic edge hexahedral elements of the new type.
## Appendix E G-Wishart Objective Function  
Next, we provide the calculation details for the derivation of the gradient and Hessian of \(\Psi(\zeta)\), as defined in Equation (15). First, we can compute the terms of the gradient element-wise by taking the derivative of \(\Psi\) with respect to the free elements of \(\zeta\),  
\[\frac{\partial\Psi(\zeta)}{\partial\zeta_{ij}}= \begin{cases}\sum_{(r,s)\in \mathcal{V}}\zeta_{rs}\frac{\partial\zeta_{rs}}{\partial\zeta_{ii}}-\frac{( \delta+v_{i}-1)}{\zeta_{ii}}+\zeta_{ii}&i=j,\\ \sum_{(r,s)\in\mathcal{V}}\zeta_{rs}\frac{\partial\zeta_{rs}}{\partial\zeta_{ ij}}+\zeta_{ij}&i\
eq j,(i,j)\in\mathcal{V}.\end{cases}  \tag{12}\]  
Note that because the non-free elements are functions of the free elements, each gradient term involves additional recursive derivative calculations. As given in Equation (16), for \((r,s)\in\mathcal{V}\) and \(r<s\),  
\[\zeta_{rs}=\sum_{j=r}^{s-1}\left(-\zeta_{ri}\frac{\lambda_{js}}{\lambda_{ss}} \right)-\sum_{i=1}^{r-1}\left(\frac{\zeta_{ir}+\sum_{j=i}^{r-1}\zeta_{ij}\frac {\lambda_{ji}}{\lambda_{rr}}}{\xi_{rr}}\right)\left(\zeta_{is}+\sum_{j=i}^{s- 1}\zeta_{ij}\frac{\lambda_{js}}{\lambda_{ss}}\right). \tag{13}\]  
Finally, using the expression for the gradient in Equation (12), we can perform a similar calculation for the Hessian. The elements on and above the diagonal are defined as follows,  
\[\frac{\partial^{2}\Psi(\zeta)}{\partial\zeta_{ij}\partial\zeta_{ kl}}= \begin{cases}\sum_{(r,s)\in\mathcal{V}}\left[\left(\frac{\partial\zeta_{rs}}{ \partial\zeta_{ii}}\right)^{2}+\zeta_{rs}\frac{\partial^{2}\zeta_{rs}}{ \partial\zeta_{ii}^{2}}\right]+\frac{(\delta+v_{i}-1)}{\zeta_{ii}^{2}}+1,&i=j= k=l,\\ \sum_{(r,s)\in\mathcal{V}}\left[\frac{\partial\zeta_{rs}}{\partial\zeta_{ kl}}\frac{\partial\zeta_{rs}}{\partial\zeta_{ij}}+\zeta_{rs}\frac{\partial^{2} \zeta_{rs}}{\partial\zeta_{ij}\partial\zeta_{kl}}\right]+\frac{\partial\zeta_ {ij}}{\partial\zeta_{kl}},&i\
eq j,(i,j),(k,l)\in\mathcal{V}.\end{cases}  \tag{14}\]  
Below, we provide the derivation for the partial derivative terms of the non-free elements taken with respect to the free elements by again starting with the recursive definition of \(\zeta_{rs}\) in Equation (16). In the following calculations, we define \(\zeta_{ks}=\lambda_{ks}/\lambda_{ss}\), where \(\Lambda=(\lambda_{ij})\), where \(1\leq i,j\leq p\). We consider two cases.
## 2 Methodology
### RZTD-Constrained RTK Model  
The troposphere parameters are highly correlated with the height component, particularly for satellites with high elevation angles [19,20]. In an ill-conditioned model, a small error in the observation will cause a large error in the result. For GPS medium-long baseline RTK, the position error of the height component can reach a few decimeters, even if the ambiguities are correctly fixed [15,17,18]. Compared to the GPS system, the height component for the BDS system may be affected considerably by the introduction of the troposphere parameters because of the heterogeneous satellite distribution.  
However, if the troposphere parameters are properly constrained by precise a priori RZTD information, the ill-conditioned problem can be alleviated and a better position solution can be obtained for BDS. In the RZTD-constrained model, the precise a priori troposphere correction \(T_{rztd}^{0}\) and the corresponding variance should be provided as follows:  
\[T_{rztd}^{0}=T_{rztd}+\varepsilon_{T_{rztd}^{0}}\sigma_{T_{rztd}^{0}}^{2}. \tag{5}\]  
In the current study, a priori zenith wet troposphere delays are derived from the high-precision GPT2 model [29], and \(\varepsilon_{T_{rztd}^{0}}\) is the error of the a priori troposphere information. \(\sigma_{T_{rztd}^{0}}^{2}\) is the variance. A smaller troposphere variance means a tighter constraint, thus the height component can be more easily distinguished from the troposphere parameter. These troposphere corrections are introduced as pseudo-observations together with the pseudo-range and carrier observations into every epoch. Meanwhile, we regard the RZTD parameter as a random walk process. The corresponding state transition equation between two consecutive epochs is:  
\[T_{rztd,m}=T_{rztd,m-1}+w_{rztd,m}\text{ with }w\sim N(0,\sigma_{w}^{2}), \tag{6}\]  
where \(w\) is the process noise, which obeys Gaussian distribution \(N(0,\sigma_{w}^{2})\), \(\sigma_{w}^{2}=q_{w}\cdot\Delta t\), \(\Delta t\) is the time interval between two consecutive epochs, and \(q_{w}\) is the spectral density. Combining Equations (4) and (5), the unknown parameters can be estimated by extended Kalman filter.
# Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR
## 3 Results  
For wind retrieval, we use simulated compact polarization SAR data and parameters from buoy observations to validate the new RV-polarized GMF, CoVe-Pol. Thus, the wind speed will be determined after we substitute values for the RV-polarized NRCS, incidence angles and wind directions. We compare the wind speeds retrieved by the CoVe-Pol model with the wind speeds observed by buoys in Figure 7b. The bias is 0.07 m/s, the RMSE is 2.48 m/s and the correlation coefficient is 81.3%. Although the accuracy of CoVe-Pol model appears encouraging, additional tests and validation are still needed in the future, when RCM data is available.  
The performance of the regression model for compact RH polarization data, CoHo-Pol, is shown in Figure 8, which indicates that the model is an effective methodology for wind retrieval from RH polarized data. Comparing model results to wind speeds observed by buoys, the RMSE is 2.37 m/s.  
The wind retrieval models for RV and RH data are presented for compact polarimetry measurements. We test CoVe-Pol model for the NRCS values and wind speed, in Figure 9a,b. The RMSE for \(\sigma_{RV}^{0}\) is only 1.28 dB and for wind speed, 2.36 m/s, and the values of the correlation coefficients are 97.9% and 82.4%. These results indicate that CoVe-Pol model is a potentially good method for wind retrieval; the computed NRCSs and retrieved winds are in good agreement with the simulated NRCSs and the independently measured buoy winds.  
Comparing the retrieved wind speeds and the buoy observations, the RMSE for winds retrieved by the CoHo-Pol model is 2.39 m/s, and the correlation coefficient is 81.5%, which is shown in Figure 10.  
Validations demonstrate that the CoVe-Pol and CoHo-Pol models are reliable and useful retrieval models for RV and RH polarized SAR data, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(a_{0}\) & \(a_{1}\) & \(a_{2}\) & \(a_{3}\) & \(a_{4}\) & \(a_{5}\) \\ \hline \(-\)17.8296 & 0.9490 & 1.8640 & 0.0447 & \(-\)0.0034 & 0.0525 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regression coefficients for the horizontal-horizontal (HH) model.  
Figure 8: SAR-retrieved wind speeds from RH polarization mode model denoted CoHo-Pol vs. buoy-measured U\({}_{10}\) for the reserved training subset.  
Figure 9: Validation of CoVe-Pol model by inputting wind speed and RV-polarized NRCSs: (**a**) \(\sigma_{RV}^{0}\) from CoVe-Pol model vs. \(\sigma_{RV}^{0}\) simulated by the RCM simulator; (**b**) wind speed retrieved by CoVe-Pol model vs. wind speed measured by buoys.
### Heuristic-Based Power Allocation Methods  
Equal power allocation and max-min power allocation are representative heuristic power control methods frequently used as baseline comparisons in cell-free network studies. The equal power allocation strategy assigns the same amount of power to all APs, which then uniformly distribute it among their served UEs. Although this method is simple and computationally efficient, it fails to account for spatial variations in channel quality and user distribution, resulting in inefficient resource utilization and reduced SE. The max-min power allocation method enhances fairness by maximizing the minimum SINR across users, ensuring that even the weakest UEs maintain basic service quality; however, this fairness-driven approach can limit overall SE by underutilizing UEs with stronger channels. While max-min allocation is mathematically formulated as an optimization problem, it is often used as a practical benchmark due to its interpretability and fairness-centric allocation. Overall, heuristic-based methods are easy to implement but cannot dynamically adapt to channel conditions or user density, motivating the development of more flexible data-driven strategies.