esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Fusion
### State-of-the-Art Research on Data Fusion
#### 3.1.3 Data Fusion Computing
In Reference [56] it is proposed that if hidden nodes are inserted into BSNs, system stability can be enhanced, while Reference [75] has reported some similar work about introducing hidden nodes by using a distributed Bayesian network. References [24] and [56] indicate that hidden nodes can be used to coordinate the inherent redundancy of sensor networks and are able to detect node failures. Furthermore, irrelevant sensor data is not sent across the network, which will reduce power consumption. In recent years, researchers have proposed a multi-sensor data fusion algorithm based on D-S evidence theory, taking D-S theory as an alternative of Bayesian inference, which is a methodology for the representation and combination of empirical evidence. In Reference [76], researchers design a BSN system for human posture recognition based on the algorithm. Experimental results show that the system can achieve recognition accuracies between 98.5% and 100%. As a kind of Markov chain, HMM is a probabilistic model used to represent non-deterministic processes, which consists of states, actions, and observations. Generally, in applications of activity recognition, it is required not only to sense and simulate the context of static scene, but also to identify the dynamic scene, therefore the HMM is applied to the supervising layer, generating a model of context transitions, in order that the analysis and identification of dynamic activities in real world can be realized. HMM plays an important role in the study of dynamical influence models of human interactions. In Reference [77], researchers collect the social behavioral signals of each entity in a social network using various sensors, modeling by the signals and time stamps based on HMM. They then infer the underlying pattern of interpersonal influence. Furthermore, the paper points out that HMM can also speculate on the functional roles of people and make classification accurately. In Reference [78], researchers develop Graph-Coupled Hidden Markov Models (GCHMM) for studying the model of local infectious disease spread in a social network, analyzing the transition dependenciesbetween multiple HMM. This paper discusses a case of infectious disease which spreads among students, collecting and processing information by mobile phones. The model can forecast the spread path of an infectious disease, so as to control its spread.
# Designed Circularly Polarized Two-Port Microstrip MIMO Antenna for WLAN Applications
## 6 MIMO Antenna System Parameters
### Diversity Gain
To the best of the authors' knowledge, this paper is the first to implement an antenna with MIMO-CP circular polarization by implementing a square-cut at the corner of a square patch antenna. Our antenna has several advantages over previously published CP antennas. Circular polarization is typically obtained when such an antenna structure is excited with two orthogonal transverse modes of equal amplitudes, but 90\({}^{\circ}\) degrees out of phase. Another traditional method is to use patches that are cut at two opposite corners of the radiating patch. Here, we simulated a square-cut at the corner of the MS antenna. As stated in the manuscript, many techniques include truncated corners, two corners of patches are truncated; stubs, slits, notches, and embedded slots for the CP antenna. We found that the advantages of our presented antenna are very different from those of the corner-truncated square patch antenna. To summarize, none of the designs combine the advantages of a simpler design, a high isolation high gain, broadband CP characteristics, and MIMO capabilities in one package, such as our proposed design. Moreover, our design is unique and simple because it is the only available MIMO-CP antenna that covers the WLAN band (5.23-6.42 GHz) applications.
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
## 4 Conclusions  
Based on the numerically simulated results of Pingnan Xiangszhou Bridge, reasonable loading mode and loading conditions were determined. On this basis, the experimental study of mode, displacement, strain, and cable force of the cable-stayed bridge was carried out. Our conclusions are as follows:  
(1) The theoretically calculated results of bridge frequency are in good agreement with the experimentally measured results, indicating that the quality of Pingnan Xiangszhou Bridge's construction is up to standard. Additionally, in the whole field loading test process, the vertical bending mode was easier to identify than the transverse bending mode;  
(2) The experimentally measured deflection values were less than the theoretically calculated values for all tested sections, and the corresponding calibration coefficients were less than 1, indicating that Pingnan Xiangszhou Bridge has higher stiffness and safety than designed;  
(3) The experimentally measured strain values were less than the theoretically calculated strain for all tested sections, and the calibration coefficients were also less than 1. Therefore, the whole bridge has adequate resistance to external loadings, and the design is reasonable;  
(4) Under the same load state, the change in measured cable force was smaller than the designed value, proving that the actual stiffness of the stay cables is better than designed.  
Conceptualization, X.W., H.W. and W.W.; methodology, X.W., L.W., H.W. and W.W.; validation, Y.N. and K.H.; formal analysis, X.W., L.W. and H.W.; investigation, X.W., Y.N. and K.H.; writing--original draft preparation, X.W. and W.W.; writing--review and editing, L.W. and H.W.; project administration, X.W., H.W. and W.W.; funding acquisition, X.W., H.W. and W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific and Technological Project of the Science and Technology Department of Jilin Province (grant number: 20210508028 RQ), the Scientific and Technological Project of the Science and Technology Department of Guangxi Province (grant number: 2021 AC19125), the Nanning Excellent Young Scientist Program (grant number: RC20180108), the Nanning Excellent Young Scientist Program and Guangxi Beibu Gulf Economic Zone Major Talent Program (grant number: RC20190206), the Science and Technology Base and Talent Special Project of Guangxi Province (grant number: AD19245152,) and the \"Yongjiang plan\" of Nanning Leading Talents in Innovation and Entrepreneurship (grant number: 2018-01-04). This research was also supported by the China Postdoctoral Science Foundation (grant number: 2021T140262).  
Not applicable.  
The testing and analysis data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## Data and Methods
### Methodology
#### 3.3.5 Statistical Analysis  
Data processing, statistical analysis and visualization were performed in R 3.1.1 (www.r-project.org). Both linear and multiple linear regression (MLR) were used to examine the relationships between VPD, PAW, LST, EVI, NBAR Band 7, and EC GPP. The relationships were used to build GPP models composed of field metrics of water stress and vegetation indices as input data. Additionally partial correlations were performed on both the field and Earth-observation variables during the greening and browning phases using [PERSON]'s rank order correlation [64]. This was done to assess the relationship of each variable with EC GPP while keeping the influence of the other variables constant. Within each year, the greening phase was delineated as the period from the lowest to the highest GPP value, and the browning was delineated as the following period from maximum GPP to its lowest value. The coefficient of determination (R\({}^{2}\)) was calculated as a measure of the amount of EC GPP variance explained by each set of predictor variables. Bayesian information criterion (BIC) [65] was chosen to identify the model which best describes the EC GPP data:  
\[BIC=-2\ln(L)+q\ln(n) \tag{7}\]  
where \(L\) is the maximum value of the likelihood function of the model, \(q\) is the total number of parameters and \(n\) is the number of sample points. \(BIC\) considers both the goodness of fit and the number of parameters that achieve a certain degree of fit by levying a penalty term \(q\ln(n)\) on increasing the number of parameters to prevent over-fitting. Root-mean-square error (RMSE) was calculated to evaluate the performance of each model output relative to EC GPP. RMSE calculates the square root of the variance and smaller values denote better model performance. An RMSE of 0.0 indicates perfect simulation of the EC GPP data.  
\[RMSE=\sqrt{\frac{\sum_{t=1}^{n}\left(OBS-PRED\right)^{2}}{n}} \tag{8}\]where \(PRED\) is the model-estimated GPP (g C m\({}^{-2}\) d\({}^{-1}\)), \(OBS\) is the EC GPP (g C m\({}^{-2}\) d\({}^{-1}\)), and \(n\) is the number of sample points.
## 4 Results
### Obtaining the Peak Load Profile
#### 4.1.3 Obtaining the Approximate Load during a Peak  
The last steps for obtaining the proper load profile refer to the calculation of the load during a peak. This could be obtained using the Algorithm 3 presented below:  
```
(input:sFopt,subs_constr;output:peak_load) 01//sFopt-normalizedsmoothedvectoroff_iforabhole-daysection 02//ssubs_constr-descriptionofsubsectionandloadconstrains-Table2 03//peak_load-hevectorofloadduringsubsectionthatloadpeakexists 04m_peak=findimdexwhereR_m_T_mhasbiggvalueinsubs_constr 05//m_peakisanindexoftmostloadedsubsectionS_m_peak 06R_m_peak=findvalueofabsoluteloadform_peaksubsectioninsubst_constr 07//calculatethesumofnormalizedloadisinturevalsbelongetingotheS_m_peak 08 sum=090 foreach(I_kinsubsectionS_m_peak) 10 sum=sum+sFopt[k] 11//calculatepeakloadbyscalingR_m_peak 12 peak_load=[] 13 foreach(I_kinsubsectionS_m_peak) 14 peak_load[k]=sFopt[k]/sum*R_m_peak
```  
**Algorithm 3**Obtaining adjusted load profile during a peak according to load restrictions.  
In line 04 of Algorithm 3, the subsection with the highest value of request per time \(R_{m}/T_{m}\) from Table 2 is selected. Let us denote its index as _m_peak_.  
Each value \(f_{k}\) of the smoothed optimal normalized load that belongs to the _m_peak_-th subsection is scaled using the absolute load, i.e., \(R_{m\_peak}\).  
Lines 08-14 are responsible for distributing the \(R_{m\_peak}\) value according to the \(f_{k}\) distribution, among the intervals included in the _m_peak_-th subsection, which can be expressed by Equation (8):  
\[r_{j}=\frac{f_{j}}{\sum_{k\in S_{m\_peak}}(f_{k})}R_{m\_peak} \tag{8}\]  
Such \(r_{j}\) values are obtained (line 14) in values of the output vector \(peak\_load\).  
Figure 3: Smoothed (red) and broken (blue) curves describing the continuous approximation of normalized load.  
For the considered real example, according to the data from the column \(R_{m}/T_{m}\) in Table 2, the highest load takes place for the 3 rd subsection. This means that \(m\_peak\) is 3, so the indices of intervals belonging to the peak are \(j=24\ldots 29\). The value of \(R_{m}=36\),360 requests (Table 2) should be distributed among those intervals according to Equation (8). The resulting load at the peak for the assumed resolution \(n=48\) (30 min intervals) is shown in Figure 4.  
The resulting load profile is presented in Figure 5. The first part (the red bar) of the experiment for a time from 0 to 30 min is designated for the so-called warming of the system (to achieve a stable state of running pods). The exact peak (blue bars) is defined from 30 to 210 min. This part of the load profile is later used for gathering statistics data for obtaining the cost-efficiency characteristic of the considered system.
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Semi-Automatic Green Tide Extraction Method
#### 3.2.2 Land Mask  
As the spectral characteristics of a green tide and vegetation are similar [15], in this study, land masking was performed on RS images to prevent terrestrial vegetation from interfering with threshold selection.  
We used shoreline information and the land vertices of RS images (i.e., the vertex of the pentacle in Figure 2) for flexible masking based on the principle of the region of interest (ROI) extraction; i.e., we selected polygon vertices. Finally, a landmark was generated with a buffer of 20 grids extending from the shoreline to the outer sea. For low-resolution RS images, i.e., those from MODIS, the shoreline information of each image was obtained by using prior known shoreline information in the geographic information system and the latitude and longitude coordinates of RS images for point-to-point distance calculation and matching the point with the shortest distance between prior known shoreline information and RS images. For high-resolution RS images (i.e., those from GF-1, Landsat 5 TM, Landsat 8 OLL_TIRS, HJ-1A/B, and HY-1C), which are larger, the shoreline matching method is more computationally intensive, and the masking efficiency is lower; therefore, to improve the masking efficiency, the shoreline was first extracted directly using our proposed semi-automatic shoreline extraction algorithm [26], and then the next step of masking was performed.
## 3 Related Work  
The concept of maintenance has evolved over time, due to developments in research and maintenance policies related to the realities of institutions, ensuring the efficiency, quality, and availability of assets [14].  
Maintenance strategies in industrial systems are traditionally classified into corrective, preventive, and predictive approaches. Each reflects a different balance between operational risk, cost, and system reliability.  
Corrective maintenance is the most basic strategy, where action is only taken after a failure has occurred. While it requires minimal planning and resources upfront, this reactive model often leads to unplanned downtime, higher repair costs, and potential safety hazards. In critical systems, such as those involving continuous production or vital machinery, this strategy is typically avoided due to its disruptive nature [17].  
Preventive maintenance, on the other hand, is based on performing scheduled interventions according to fixed time intervals or usage cycles. It aims to reduce the probability of unexpected failures by replacing or servicing components before they reach the end of their expected lifespan. However, preventive maintenance can lead to inefficiencies, including premature part replacement and excessive maintenance effort, especially when failure modes are not easily predicted by time-based criteria [17; 20].  
Predictive maintenance (PdM) has emerged as a more intelligent and cost-effective alternative. By leveraging sensor data, condition monitoring, and machine learning techniques, PdM enables the early detection of performance degradation and forecasts potential failures with greater precision [16; 21; 22]. This approach minimizes unnecessary interventions while reducing the risk of unplanned downtime.
## 7 Simulations and Results
### Patients in Department  
Figure 3 shows a comparison of actual observed data (in black) for the Patients in Department indicator compared with the ARIMA (green), Prophet (yellow) and GRNN (purple) predictions. Here, the red coloured square indicates the start of lockdown due to the COVID-19 pandemic. Three regions of the chart can be clearly distinguished (to the left, inside and to the right of the square) and indicate the normal number of Patients per hour in the Medway Foundation Trust ED before, during and after the lockdown, the sudden change, and the way data are slowly growing back in an apparent linear trend, from May to June. It is particularly interesting to see that ARIMA and Prophet overestimate their predictions for a five-week period, whilst GRNN is quick to adjust to the data ingress change. Another interesting observation is that the variance of the data is reduced after the lockdown, making the data easier to predict for all models. This is confirmed in the following figures which show predictions and actuals (top) and residuals (bottom) in more detail for each model.  
In Figure 4 it can be observed that the ARIMA model predicts the Patients in Department time-series with a fair accuracy until the lockdown period, where the residuals  
Figure 3: Comparison of Actuals, ARIMA, Prophet and GRNN, February 2020–June 2020.  
negatively increase. Then, after five weeks the model learns the new levels and the residuals are again back to acceptable levels.  
In Figure 5 it can be observed that Prophet, similar to ARIMA, predicts the Patients in Department indicator with fair accuracy until lockdown, during which the predictions are overestimated and the residuals increase by an even greater margin than ARIMA. However, after five weeks the model learns the new levels and the residuals are again back to acceptable levels.  
In Figure 6 it can be observed that GRNN, in contrast to the ARIMA and Prophet models, is able to quickly adapt to the change in observed values of Patients in Department due to COVID-19, keeping a good level of accuracy throughout the whole data sample.
# PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field
## 3 Experiments
### MATLAB Implementation
#### 3.1.4 Conclusion of MATLAB Simulation  
The above three experiments show that APF will fall into the local minimum point and fail to reach the goal point in MATLAB environment simulation. Even without falling into the local minimum point, there is a great deviation between the actual path and the original planned path because of obstacle avoidance. PK-APF simulations show that the goal points can be reached and high-precision path-keeping is achieved. Regarding data analysis, the maximum deviation path-keeping error of PK-APF was reduced by 30.07% to 80.92% compared with APF. When obstacles interfere with the USV navigation path planning, PK-APF can avoid obstacles, reach the goal point, and achieve high-precision path-keeping.  
Table 2 shows the path sampling points, 0.5-error points, and path-keeping precision information of APF and PK-APF. The total sampling point is the travel length of the USV when planning the path. A point with a 0.5-error is considered sailing on the original planned path less than 0.5 m-deviation. Path-keeping precision for the USV is calculated with the precision of sampling points where the error is below 0.5 m. In the first experiment, because the APF falls into the local minimum point, the path planned to the goal point was not completed, so it did not calculate the path-keeping precision. In the Table 2 experiments, the path-keeping performance of PK-APF is higher than APF, which indicates that PK-APF has a higher path-keeping performance than APF.  
Figure 10: Path-keeping error result in multiple obstacles.  
Figure 9: Path planning with multiple obstacles. **(a)** APF path planning; **(b)** PK-APF path planning.
# Numerical Simulation of Random Cavitation Suppression Based on Variable NACA Airfoils
## 4 Results and Discussion  
### Cavitation Nephogram Discussion  
Figure 4a shows the cavitation nephogram of original airfoil, and Figure 4b,d shows the cavitation nephogram of the airfoil with convexity of 1 mm, 3 mm, and 6 mm, respectively. The cavitation bubbles of the original airfoil are tight against the upper surface and extend in the direction of the oncoming flow, which accounts for about 2/3 of the chord length of the airfoil, and the upper surface of the airfoil is heavily cavitated. Compared with the original airfoil, it is found that the cavitation range of airfoil with maximum variable of 1 mm is obviously reduced, the main body of the cavitation area is inverted trapezoid, and there is partial vortex cavitation at the edge of the airfoil, which is mainly caused by the initial protrusion of the covering in the middle of the deformation. The cavitation area of airfoil with convex degree of 3 mm is further reduced to about 1/4 of the upper surface. The cavitation deviation caused by the height difference still exists, but it decreases with the increase of convex degree. As for the airfoil with a maximum variable of 6 mm, cavitation occurs only at the raised position of the covering, and cavitation and the low-pressure area are no longer generated elsewhere on the upper surface. Moreover, the cavitation scale of the covering is significantly reduced, which indicates that the bulge of the covering plays an effective role in inhibiting the development of cavitation.
## 5 Conclusions  
This article presented algorithms and circuit designs for simulating pairing Hamiltonians based on various nearest-neighbor interactions, such as the Heisenberg Hamiltonian, longitudinal Ising Hamiltonian, XY Hamiltonian, and transverse Ising Hamiltonian, all of which can be executed on solid-state quantum devices. These Hamiltonians were implemented using an IBM superconducting qubit quantum computer. The study involved quantum state tomography for these diverse Hamiltonians, examining fidelity in relation to number of iterations using Suzuki-Trotter decomposition. The experimental density matrix was observed to gradually converge toward the theoretical density matrix as the number of iterations increased. This demonstrates the potential for transforming Hamiltonians from various models into quantum circuits and simulating them on quantum computers to address complex physics problems. Quantum state tomography emerged as a valuable approach for determining the density matrices of such models. By increasing the number of iterations through Trotter decomposition, the experimental outcomes on quantum hardware closely matched the theoretical predictions. The fidelity versus iteration analysis served as a metric to assess how the experimental matrix evolved with increasing number of iterations. This variation in fidelity also highlighted the presence of noise in the quantum system, an inherent characteristic of the \"Noisy Intermediate Scale Quantum (NISQ)\" era.  
Conceptualization, B.K.B. and F.J.S.; methodology, S.C.; formal analysis, S.C.; resources, S.C., B.K.B. and F.J.S.; data curation, S.C., B.K.B. and F.J.S.; writing--original draft preparation, S.C.; writing--review and editing, B.K.B. and F.J.S.; visualization, S.C.; supervision, B.K.B. and F.J.S.; funding acquisition, F.J.S. All authors have read and agreed to the published version of the manuscript.  
This work at Hampton University was supported by NASA NNX15 AQ03A and ARO W911 NF-15-1-0535.  
Not applicable.  
The data presented in this study are available in the article.  
The authors acknowledge the support of IBM Quantum Experience for providing the access to quantum processors. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Quantum Experience team.  
Author [PERSON] was employed by the company Bikash's Quantum (OPC) Pvt. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Decision Support Models for Operations and Maintenance for Offshore Wind Farms: A Review
## 7 Discussion
### Modeling  
The existing decision support tools and models are based on DES, Markov models and autoregressive models. Alternatives to these models are other stochastic processes, or differential equations. Since solutions to differential equations are not always guaranteed, one needs to be careful with using these. For simulation-based models, DES and Markov models will therefore likely stay popular choices--also for offshore wind farm maintenance scheduling.  
The existing models can capture most influencing factors; however, handling of uncertainty in the inputs is still an area that can be improved upon. Different wind farm layouts and locations can be included in the existing models, albeit being complex in implementation. The existing models lack an accessible way to handle uncertainties in the input and provide information about the variability in the output. Currently, mean values are reported and sensitivity analyses must be done by manually varying the inputs.