esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Techniques to
## 3 Methodology
### Climate Zoning  
Climate zoning/regionalization is the most important step in this framework and the basis for the subsequent steps. For regionalization, solid evaluations of different precipitation attributes require accessibility to long records of chronicled estimations at various stations inside an area [2]. For climate zoning, 35 years' (2006-2075) daily precipitation data of APHRODITE were used. These data were resampled seasonally to develop the climate zones for each of the seasons (defined in the subsequent sections). Regionalization was performed by grouping the rain gauges with homogeneous precipitation statistics in an area [54]. This step enables partitioning of the entire area into several climate zones for every season.  
The regionalization of the precipitation statistics has numerous applications in various water resource management fields, such as agriculture practices, spatial and temporal rainfall patterns, hydrological analysis, extreme event forecasting [55] and watershed management. There are several methods available to delineate climate regions, for example, subjective and objective partitioning, geographical convenience and multivariate analysis [54; 56; 57]. The arbitrary and slightly misleading demarcation approach for a region, which is based on administrative boundaries and physical and geographical groupings, is referred to as the geographical convenience method. The subjective partitioning method is based on homogeneous statistical characteristics of the rainfall and previous knowledge of the region. Objective partitioning demarcates a region by grouping the sites of similar climates. Principal  
Figure 3: Flow chart of the methodology employed in this study.  
component analysis (PCA), clustering techniques and correlation analysis are examples of multivariate analysis techniques, which are widely used for climate zone delineation [51; 58; 59]. These analysis techniques require the development of a large matrix that defines the characteristics of the climate in the region. We adopted the method of PCA and agglomerative hierarchical clustering (AHC) to group the sites of homogeneous precipitation. These groups of stations were validated using different cluster validity indices.
## 4 Results  
### Feature Selection Results  
VI values of 303 image features of ZY-3 data in 2013 were calculated using random forest and were sorted in ascending order. According to the feature hierarchical selection  
Figure 2: Change identification using feature hierarchical selection and training samples transfer learning methods.  
method, features with similar VI values were divided into the same feature group. The red dots in Figure 3 are the points distinguishing different feature groups, corresponding to the 35, 71, 109, 158, 225, 265, and 281 st features. In this way, the 303 features were divided into eight feature groups, represented as \(F=\{f_{1},f_{2},f_{3},f_{4},f_{5},f_{6},f_{7},f_{8}\}\), in which \(f_{1}=[1\sim 35]\), \(f_{2}=[36\sim 71]\), \(f_{3}=[72\sim 109]\), \(f_{4}=[110\sim 158]\), \(f_{5}=[159\sim 225]\), \(f_{6}=[226\sim 265]\), \(f_{7}=[266\sim 281]\), and \(f_{8}=[282\sim 303]\).  
Ground reference sample data of six different wetland categories were collected using UAV flight, including sea, open water, farmland, aquaculture pond, salt marsh, and building. In this case, small amounts of training samples in 2013 were obtained, and they were used for wetland classification in 2013. In order to verify the influence of different feature combinations on wetland classification, different feature subsets were input into random forest classifiers for wetland classification. Overall accuracy and Kappa coefficient of different feature subsets are listed in Table 4. Generally, the overall accuracy of wetland classification decreased sequentially, along with the feature groups with low VI values being deleted sequentially. The feature groups that are beneficial to wetland identification were deleted, which reduced the distinction of wetland classes. The overall accuracy of wetland classification increased when the feature groups \(\{f_{1}\}\) were deleted. The overall accuracy of \(F_{1}=\{f_{2},f_{3},f_{4},f_{5},f_{6},f_{7},f_{8}\}\) for wetland identification was the highest, at 98.51%. The Kappa coefficient of \(F_{1}=\{f_{2},f_{3},f_{4},f_{5},f_{6},f_{7},f_{8}\}\) was 0.9763, and was also the highest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Different Feature Subsets** & **Overall Accuracy (\%)** & **Kappa Coefficient** \\ \hline \(F=\{f_{1},f_{2},f_{3},f_{4},f_{5},f_{6},f_{7},f_{8}\}\) & 98.36 & 0.9731 \\ \(F_{1}=\{f_{2},f_{3},f_{4},f_{5},f_{6},f_{7},f_{8}\}\) & 98.51 & 0.9763 \\ \(F_{2}=\{f_{3},f_{4},f_{5},f_{6},f_{7},f_{8}\}\) & 98.17 & 0.9700 \\ \(F_{3}=\{f_{4},f_{5},f_{6},f_{7},f_{8}\}\) & 98.24 & 0.9711 \\ \(F_{4}=\{f_{5},f_{6},f_{7},f_{8}\}\) & 97.99 & 0.9670 \\ \(F_{5}=\{f_{6},f_{7},f_{8}\}\) & 97.59 & 0.9605 \\ \(F_{6}=\{f_{7},f_{8}\}\) & 96.57 & 0.9439 \\ \(F_{7}=\{f_{8}\}\) & 96.07 & 0.9359 \\ \hline \end{tabular}
\end{table}
Table 4: The difference of wetland classification using different feature subsets.  
Figure 3: Hierarchical features in which the red dots are the points distinguishing different feature groups.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 3 Situation and Strategy
### Data Processing Strategy
For the tropospheric process noise, it is calculated by the process noise of wet ZTD and the sampling interval in this study, the empirical model can be expressed as follow:  
\[\sigma_{\textit{trop}}^{2}=q_{\textit{trop}}^{2}\,\Delta t \tag{11}\]  
where \(\sigma_{\textit{trop}}\) is the ionospheric process noise; \(q_{\textit{trop}}\) is the process noise of wet ZTD, which is set as \(10^{-4}\,\,m/\sqrt{s}\). In addition, the code and phase error ratio of each frequency can be set based on an empirical value, e.g., 100:1. The observation weight is also used according to the criterion of elevation-dependent weight. Noteworthily, the large LEO constellation brings the challenge of high-dimensional ambiguity resolution. Once all ambiguities are simultaneously failed to fix, partial ambiguity-fixed strategy can be adopted to resolve a subset of the candidate ambiguities during data processing [32].
## 3 Undifferentiated Cells (Stem Cells)
### Adult Stem Cells
#### 3.4.7.2 Applications in BTE  
The usability of MDSCs for bone repair and regeneration has been shown in several studies [360; 361]. As an interesting point, [PERSON] et al. showed that donor sex has a great influence on the osteogenic potential of MDSCs [362]. They stimulated both human male-derived and female-derived cells by BMP4 in order to provide an osteogenic condition. With respect to the in vitro results, the authors stated that male MDSCs (M-MDSCs) are more potent cells than female ones (F-MDSCs) regarding bone healing strategies. Moreover, the in vivo results showed that BMP4-induced M-MDSCs could form a more consistent and denser ectopic bone as compared to BMP4-induced F-MDSCs. The authors claimed that the greater osteogenic potential of M-MDSCs is likely related to the higher number of osteo-progenitor cells within them.  
The in vivo osteogenic differentiation of rat MDSCs (rMDSCs) has been examined within an injectable in situ-forming chitosan scaffolds [363]. For this aim, the isolated cells were seeded onto the scaffolds and then implanted in the subcutaneous dorsum of a rat as follows: G1 (0.5 mL chitosan gel), G2 (0.5 mL chitosan gel + 0.1 mL osteogenic factor solution), G3 (0.5 mL gel + 5 \(\times\) 10\({}^{5}\) rMDSCs), and G4 (0.5 mL gel + 5 \(\times\) 10\({}^{5}\) rMDSCs + 0.1 mL osteogenic factor solution). The in vitro results showed that chitosan is a suitable substance for cell attachment and proliferation. The newly formed bone was only observed in the animals receiving the chitosan gel containing both rMDSCs and osteogenic factors. Furthermore, adding cells to the chitosan gels resulted in lower host tissue responses as compared to the chitosan gel alone, which clarifies the immunosuppression activity of the transplanted rMDSCs.  
In 2014, [PERSON] et al. compared the osteogenic potential of human MDSCs and BM-MSCs in vitro and in vivo [355]. Their results showed that both cell types have an equal in vitro osteogenic differentiation potential after being transduced to express BMP2. In addition, the in vivo results showed that fibrin sealant scaffolds containing the transduced cells could regenerate new bone in the critically-sized calvarial defects in mice. No significant differences were reported regarding the newly formed bone volumes and bone defect coverage between the MDSCs and BM-MSC groups. Quicker bone remodeling was observed in the animals treated with the scaffolds contacting BMP2-transduced MDSCs. The authors concluded that MDSCs could be considered as efficient alternatives to BMMSCs in terms of bone repair and regeneration.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 3 Materials and Methods  
This section first describes the design of our hardware prototype and explains critical design decisions. We give an architecture overview in Figure 2. We first focus on the energy harvesting unit at the tag and then move on to the backscatter modulator. After that, we describe the reader circuit and software-defined receiver pipeline that mitigates the effects of ISI by equalization and corrects carrier frequency and timing offsets, thereby enabling communication with inaccurate low-power oscillators in tag devices.
# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 5 Conclusions  
In this paper, we have proposed a novel semantic segmentation and lightweight-based method (Real-time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation, MDNet-LBPC) for motion detection. In order to improve the efficiency of model inference, we first propose a single linear bottleneck operator to make the lightweight network. In order to handle the problem of information loss due to the use of pooling for the reduction of feature dimensions in the network, we also propose a pooling compensation mechanism to supplement useful information. The experimental results show that, for the segmentation accuracy, it can be seen from Table 8 that our method achieved third place, and each index is close to the method ranking first. For the efficiency of segmentation, this method achieved first place, and the segmentation speed is far ahead of other methods. Specifically, compared with the second-ranking similar method FgSegNet_V2 [9] our precision (P) has a difference of 1.47%, and the segmentation speed has been greatly improved, reaching 151 frames per second.  
The proposed real-time unsupervised motion detection network model considers the balance of real-time and accuracy, but it only makes a preliminary lightweight treatment on  
Figure 7: Change curve of PWC relative to each category of the dataset: the bold curve represents the change curve of PWC value of each category corresponding to the method in this paper, which is one of the several broken lines closest to the straight line 0, indicating that our method has a low segmentation error rate in each scene.  
the network. In actual application, we can prune the network, and compress and accelerate the quantitative model according to specific application scenarios to improve the inference efficiency of the network. Moreover, our research has certain limitations. In the training process, we rely too much on data sets, so we are not completely unsupervised in the real sense. Therefore, in the future work, we can consider how to design a completely unsupervised real-time unsupervised motion detection and segmentation algorithm, so that the model is no longer limited by data set annotation. We expect to further reduce the weight of the model to improve its efficiency.  
Conceptualization, L.Y.; Formal analysis, L.Y.; Investigation, Y.D.; Methodology, H.C.; Supervision, Y.D.; Validation, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by NSFC (No. 61871074).  
Not applicable.  
The data used in this study included, i.e., CDNet2014 image sets, are openly available.  
The authors declare no conflict of interest.
## 3 General Framework for the Multi-Time-Scale Coordinated Operation of Combined System
### MPC-Based Real-Time Corrective Scheduling  
Inspired by the rolling optimization and feedback correction idea of model predictive control (MPC), an MPC-based real-time corrective scheduling model is established. The objective is to achieve the minimal power generation adjustment cost and ensure that batteries approach the planned power output determined in the day-ahead scheduling. On basis of the 15-min ahead updated wind-photovoltaic power load and the actual operating state of power generation equipment, the sequential power output adjustments of battery-thermal-hydro-pumped storage are solved for the next 15 min. Compared with the traditional real-time power correction, which is only optimized in one single time section, the proposed MPC-based real-time corrective scheduling model can optimize the power output of battery-thermal-hydro-pumped storage units in advance for 15 min by considering the changing tendency of wind-photovoltaic power load variations.  
The proposed multi-time-scale coordinated scheduling framework for the combined WPTHB system is shown in Figure 1.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 3 Position and Uncertainty Estimation Methods
### Kalman Filter with Higher Order Noise
#### 3.2.2 Ornstein-Uhlenbeck Noise  
Correlated noise can be included in the Kalman filter schemes by extending the state vector to include one or more parameters to represent the noise model. When an OU process generatesthe noise in the state variable, \(x\) is augmented by a new variable, \(x_{t}\), determined by the OU process. The corresponding forward and measurement maps are \(x_{t}\mapsto\left(x_{t}-\mu\right)\exp\left(-\theta\Delta t\right)+\epsilon\), where \(\epsilon\sim\mathcal{N}\left(0,\sigma^{2}\left(1-e^{-2\theta\Delta t}\right)/2 \theta\right)\) and \(\mathbf{g}(\mathbf{x})=x_{t}\). The noise is thus shifted to the process noise from the observation noise (cf. Section 3.2.1); hence, the former can now be treated as inconsequential. Specifically, we choose a mean position coordinate \(\mu\) and variance \(\mathbf{K}_{\mu\mu,0}\) with the same values as \(x\) and \(\mathbf{K}_{xx,0}\) in the prior for the uncorrelated noise model. The state also includes the OU variate \(x\); we chose a prior for \(x\) equal to the first data point of the GPS time-series and therefore started the filtering from the second data point onwards. Consequently, we chose a small value for the prior variance in the noise parameter \(\mathbf{K}_{xx,0}=0.1\) m\({}^{2}\). We also included the reversion rate parameter \(\theta\) (or, more specifically, its logarithm) in the state, with the prior mean/variance of \(\theta\) equal to the mean/variance of the fitted parameters for the Dunedin GPS datasets for latitude, longitude and altitude, respectively (see Section 2.2). Therefore, the inferences for the North Stradbroke Island, Port Douglas and Thursday Island time-series (which had no role in the parameter fitting) serve as a test of the general applicability of the algorithm in independent locations.  
It is not possible to estimate the OU variance parameter, \(\sigma^{2}\), using orthogonal sigma point selection for which the covariances of the state parameters with the process noise are set to zero (see Algorithm A1, Step 2). Hence, here, \(\sigma^{2}\) was set for each coordinate as the mean of the fitted \(\sigma^{2}\) for the Dunedin GPS datasets. The simultaneous estimation of \(\sigma^{2}\) with the state parameters is left for future research.
## 4 Discussion
### Key Elements of Lean Six Sigma's Suitability for the Redesign of Surgical Pathways  
#### 4.1.1 Customer Voice and Co-Design  
The importance of interdisciplinary teamwork and communication in surgical teams is well documented [81; 82] with shared goals, knowledge, and respect for persons creating the conditions for timely problem solving [83]. Lean Six Sigma encourages a collaborative and cross-functional approach to problem solving [20; 21; 25], which, for surgical pathways, engages surgeons, nurses, anaestists, administrative, and support staff in identifying and resolving issues related to pathway efficiency. By bringing together diverse perspectives and expertise, Lean Six Sigma can foster a culture of continuous improvement, communication, and engagement among OR staff leading to sustained improvements in efficiency [55; 84].  
The voice of the customer approach to understanding requirements is synergistic with person-centredness and the principle of respect for person [20; 25]. Participants in Lean Six Sigma initiatives have found that the methodology enabled them to better engage with colleagues and patients, actively listen to their voices, and try to meet their expectations [85]. The significance of open and clear communication, at the levels of department, unit, ward, or practice area, was found to be a cornerstone of Lean Six Sigma practice [25] that facilitated process change by engaging staff from all specialities and levels of seniority in co-designing solutions [21].  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{1}{c}{**Pathway**} & \multicolumn{1}{c}{**Results**} \\ \hline Preparation for surgery & \(\bullet\) & Reduced wait time from referral to surgical consultation \\  & \(\bullet\) & Reduced wait time for required preoperative testing \\  & \(\bullet\) & Enhanced patient education leading to improved post-operative outcomes \\  & \(\bullet\) & Reduced clinical staff time on administrative function \\ \hline  & \(\bullet\) & Efficient and smooth task coordination \\  & \(\bullet\) & Reduced clinical risk, e.g., wrong site surgery, medication error \\  & \(\bullet\) & Reduced surgical set up times \\ Surgery & \(\bullet\) & Reduced theatre turnaround time (TAT) \\  & \(\bullet\) & Enhanced quality of care in the OR \\  & \(\bullet\) & Increased staff morale and teamwork in the OR \\  & \(\bullet\) & Reduced resource wastage \\  & \(\bullet\) & Minimized overtime and associated cost saving \\ \hline  & \(\bullet\) & Restored function through enhanced therapy processes \\  & \(\bullet\) & Improved processes for post-operative pain management \\ Rehabilitation & \(\bullet\) & Reduced post-operative hospital acquired infection (HAI) rates \\  & \(\bullet\) & Reduced patient length of hospital stay \\  & \(\bullet\) & Consistent, evidence-based care \\ \hline \end{tabular}
\end{table}
Table 1: An overview of the application of Lean Six Sigma to surgical pathways.
## 8 Conclusions  
This study investigated the effect of a proposed reinforcement method on the compressive performance of upright frames. An extensive experimental program was conducted on 75 upright frames, and 9 single uprights with different thicknesses and heights, and the corresponding results obtained from the tests were analyzed. The reinforcement system was proposed in such a way that bolts and spacers were attached along the upright height. Experimental tests showed that the application of the reinforcement system is significantly effective for increasing the load-bearing capacity when distortional buckling failure mode governs. However, the application of this method is not particularly useful for taller frames. This justifies the fact that this reinforcing method (i.e., using bolts and spacers to partially close off the open profile) is predominantly effective on distortional buckling mode and has minimal effect on other buckling modes (torsional, flexural/torsional, local, and flexural). In addition, compared to thinner uprights (1.6 mm thick), thicker uprights (2.5 mm thick) showed higher capacity improvement (percentage of increased load compared to unreinforced frame) when reinforced with bolts and spacers. The results also showed that the reinforcement has a significant influence not only on the ultimate load capacity but also on the buckling failure mode of low length upright frames (1200 mm, 1800 mm, and 2400 mm). Numerical simulation was also employed in order to investigate the effect of different reinforcement spacing on the performance of the upright frame. It was indicated that up to 100 mm reinforcement spacing, decreasing the reinforcement spacing can increase the ultimate strength of the upright under compression load; while less improvement was observed when reinforcement spacing decreased from 100 mm to 50 mm.  
Further experimental and theoretical attempts are required in order to better understand the behavior of open perforated profiles reinforced with bolts and spacers as well as other materials. Finite element method is also needed for a parametric study of different types of reinforcement systems.  
[PERSON], [PERSON], [PERSON] and [PERSON] devised the project, the main conceptual ideas and proof outline. [PERSON], [PERSON] and [PERSON] conceived and planned the experiments. [PERSON], [PERSON] carried out the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] planned and carried out the simulations. [PERSON], [PERSON] contributed to sample preparation. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the interpretation of the results. [PERSON] and [PERSON] took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript.  
The research was funded from R&D contract budget of Western Sydney University. The materials for laboratory testing was provided by Dexion Australia.  
The authors would like to acknowledge the contribution by Dexion Australia in supporting this study.  
The authors declare no conflict of interest.  
Figure 26: Percentage of increased ultimate load with different reinforcement spacing.
## 4 Discussion
### Qualitative Performance Comparison with Prior Methodologies
The above qualitative comparison shows in total that the full capabilities and strengths of the digital image processing field remain to be explored. Nonetheless, it becomes clear that deep learning algorithms can achieve high classification rates, by fully automating the image analysis process without the extensive need for image enhancement and object segmentation techniques. In the next subsection, new techniques, which have been implemented by many experts in the field of deep learning, are discussed and could lead to an improvement of the current methodology.