esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
[68] proposed a multi-dimensional fusion modelling method to enhance DT accuracy in smart production lines. [PERSON] et al. [18] introduced a structured VC framework that simplifies DT integration into manufacturing systems. [PERSON] et al. [69] developed a cloud-based VC platform for IEC 61499 automation systems, enabling real-time remote testing without local installations. [PERSON] et al. [70] created an automated DT generation tool for EV charging stations, improving commissioning efficiency. [PERSON] and [PERSON] [71] applied process mining to DT workflows, optimizing VC time by selectively validating process changes. While virtual commissioning and digital twin technology are well established in research and industry, a structured methodology for integrating 3D models with behavioural models remains underexplored. Many studies focus on theoretical foundations, software tools, or case studies but provide limited guidance on systematically developing and validating combined 3D and behavioural models in virtual commissioning. Additionally, while significant progress has been made in real-time data exchange, interoperability, and automation validation, the role of detailed behavioural models within virtual commissioning is not yet fully standardized. Existing research often discusses software capabilities individually, but a comprehensive approach that aligns 3D visualization, LB, and system interactions for accurate DT simulation is still evolving. This study aims to address these gaps by focusing on the structured development and validation of 3D models and behavioural models within virtual commissioning, ensuring their effective application in real-world industrial environments. By bridging the gap between simulation fidelity and practical implementation, this research contributes to the broader advancement of digital twin methodologies for system validation, efficiency improvement, and automation reliability. \begin{table}
\begin{tabular}{c c c} \hline
**Type of Software** & **Name** & **Resource** \\ \hline \multirow{5}{*}{Digital twin simulation} & NX Mechatronics Concept & [57] \\  & Designer & \\  & Tecnomatix Process & [11] \\  & Simulate & \\  & Tecnomatix Plant & [58] \\  & Simulation & \\  & ABB RobotStudio & [59] \\  & CIROS & [60] \\ \hline Digital twin logical & WinMOD & [61] \\  & SIMIT & [62] \\ \hline PLC emulator & PLC Sim & [63] \\  & PLC SIM Advanced & [64] \\ \hline \multirow{3}{*}{PLC program} & Simatic Manager & [65] \\  & TIA Portal & [66] \\ \cline{1-1}  & Studio 5000 & [67] \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of software solutions for virtual commissioning and digital twin environments, categorized into digital twin simulation, logical behaviour modelling, PLC emulation, and PLC programming, with corresponding references.
# Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
## 3 Kalman Filter with Inequality Constraints
### Standard Kalman Filter  
The state and observation equation of a system can be expressed in linear form as follows [29]:  
\[\left\{\begin{array}{l}\mathbf{X}_{t}=\mathbf{F}_{t-1,t}\mathbf{X}_{t-1}+\mathbf{w}_{t}\\ \mathbf{y}_{t}=\mathbf{H}_{t}\mathbf{X}_{t}+\mathbf{v}_{t}\end{array}\right. \tag{20}\]  
where \(\mathbf{X}_{t}\) and \(\mathbf{X}_{t-1}\) represent the state vector at epoch \(t\) and epoch \(t-1,\mathbf{F}_{t-1,t}\) represents the transition matrix from epoch \(t-1\) to epoch \(t\), \(\mathbf{y}_{t}\) represents the measurement, and \(\mathbf{H}_{t}\) represents the design matrix. \(\mathbf{w}_{t}\) and \(\mathbf{v}_{t}\) represent uncorrelated white noise vectors of the  
Figure 4: A flowchart of the distance constraint process.  
system and measurement. Their covariance matrices are \(\mathbf{Q}_{wt}\) and \(\mathbf{R}_{t}\). \(\mathbf{Q}_{wt}\) is calculated as follows [5]:  
\[\mathbf{Q}_{wt}=\sigma_{a}^{2}\begin{bmatrix}\frac{\Delta t^{4}}{2\Omega}&\frac{ \Delta t^{3}}{8}&\frac{\Delta t^{2}}{6}\\ \frac{\Delta t^{3}}{8}&\frac{\Delta t^{2}}{3}&\frac{\Delta t}{2}\\ \frac{\Delta t^{2}}{6}&\frac{\Delta t}{2}&1\end{bmatrix} \tag{21}\]  
where \(\sigma_{a}^{2}\) represents empirical variance, which we set to 0.012 \(m^{2}\) in this paper. \(\mathbf{R}_{t}\) represents the prior observation variance, which is related to the stochastic model, and we choose the C/N0-based stochastic model to estimate the variance in satellite observations [43].  
In this paper, we only estimate the position, velocity, and acceleration. The transition matrix can be computed as follows:  
\[\mathbf{F}_{\mathbf{t},t-1}=\begin{bmatrix}1&\Delta t&0.5\Delta t^{2}\\ &1&\Delta t\\ &&1\end{bmatrix}\otimes diag(1,1,1) \tag{22}\]  
where \(diag(1,1,1)\) represents an identity matrix, and \(\otimes\) represents the Kronecker product operator. The state vector \(\mathbf{X}_{t}\) and its covariance \(\mathbf{Q}_{t}\) can be estimated as follows [44]:  
\[\left\{\begin{array}{l}\mathbf{X}_{t-\mathbf{1},t}=\mathbf{F}_{t-\mathbf{1},\,\mathbf{k}}\mathbf{X}_ {t-\mathbf{1}}\\ \mathbf{V}_{t-\mathbf{1},t}=\mathbf{y}_{t}-\mathbf{H}_{t}\mathbf{X}_{t-\mathbf{1},t}\\ \mathbf{Q}_{t-\mathbf{1},t}=\mathbf{F}_{t-\mathbf{1},t}\mathbf{Q}_{t-\mathbf{1},t}\mathbf{F}_{t-\mathbf{1},t}^ {\mathbf{\Gamma}}+\mathbf{Q}_{wt}\\ \mathbf{K}_{t}=\mathbf{Q}_{t-\mathbf{1},t}\mathbf{H}_{t}^{\mathbf{\Gamma}}\left(\mathbf{H}_{t}\mathbf{Q}_{t -\mathbf{1},t}\mathbf{H}_{t}^{\mathbf{\Gamma}}+\mathbf{R}_{t}\right)^{-1}\\ \mathbf{X}_{t}=\mathbf{X}_{t-\mathbf{1},t}+\mathbf{K}_{t}\mathbf{V}_{t-\mathbf{1},t}\\ \mathbf{Q}_{t}=(\mathbf{I}-\mathbf{K}_{t}\mathbf{H}_{t})\mathbf{Q}_{t-\mathbf{1},t}(\mathbf{I}-\mathbf{K}_{t} \mathbf{H}_{t})^{\mathbf{\Gamma}}+\mathbf{K}_{t}\mathbf{R}_{t}\mathbf{K}_{t}^{\mathbf{\Gamma}}\end{array}\right. \tag{23}\]  
where \(\mathbf{I}\) represents an identity matrix and \(\mathbf{K}_{t}\) represents the Kalman gain.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 2 Electromyography
### Detection of the Activation Timing (Onf)  
Precise detection of onset and offset events in the EMG signal is a key feature in the analysis of the motor system under investigation. Differing from other measures, the activation timing can be estimated in isometric or anisometric conditions. However, several confounding factors may affect the detection, including a physiological limit for accuracy (about a 10 ms interval) due to fiber nervous transmission velocity and the delay with force production, with respect to the EMG signal, which depends on muscle fiber composition. Apart from recording recommendations, such as placing the electrodes in the midline of the muscle belly [35], the detection accuracy depends on the employed detection algorithm. Several methods have been proposed for detecting the on and off timing of the muscle; they can be divided into categories, depending on the application. Some algorithms are suitable for online detection [37], or are designed for offline analysis [38]. Algorithms may follow different computational approaches: The single threshold technique is based on the comparison of the rectified raw signals and an amplitude threshold whose value depends on the mean power of the background noise [37]. Alternatively, the double threshold algorithm [39] follows a probabilistic approach, selecting the thresholds such that their values minimize the false-alarm probability and maximize the probability of correct detection for each specific signal-to-noise ratio.
## 2 Methodology
### Objective Function  
To analyze the fitness of each constellation, we make use of the objective functions \(J_{so}\) and \(J_{to}\). \(J_{so}\) represents the objective value analyzing the global spatial fitness, whereas \(J_{to}\) is the analog value for the global temporal fitness. Both objective values are being optimized simultaneously through the multiobjective NSGA-II algorithm.
## 6 Conclusions  
This paper has presented a no-reference quality evaluation model aimed to assess the quality of views rendered using DIBR. The novelty of this model comes from the direct use of a new depth map quality measure, called the DEC measure, as a reliable indication of the overall rendered view quality. As such, developing the proposed DEC measure proved to be a very powerful tool in quantifying the quality of a depth map. The no-reference feature of the developed model has diverged from the conventional no-reference quality assessment concepts. Conventional no-reference methods perform the quality assessment directly on the colour view, while the model developed in this research has provided such measure through assessing the quality of the depth map used in the rendering process. The experiments conducted to assess its performance have resulted in a 27% improvement in correlation with subjective results recorded by the real viewers over that offered by VQM, which was taken as the benchmark objective quality assessment method in this research. This model has also provided superior performance results compared to those of the conventional no-reference quality assessment methods reported in the literature.  
The developed no-reference quality evaluation model is expected to provide great benefits in-depth map production, as it has the potential to offer a reliable measurement of depth map quality. This is particularly significant when there is a lack of depth reference in immersive video applications for depth comparison and generation. Utilising the developed DEC measurement-based model will also offer benefits to the 3D multi-view research field, as it eliminates the need for time and effort consuming subjective tests, which are traditionally used for assessing the quality of rendered views. In turn, this can lead to faster improvements in-depth map production and can also be used in live broadcast services for the immersive video applications whilst offering higher QoE to the users.  
Conceptualization, S.D., N.H. and A.M.K.; methodology, N.H. and S.D.; software, N.H.; validation, N.H. and E.E.; formal analysis, E.E.; investigation, N.H. and S.D.; resources, E.E. and A.M.K.; data curation, N.H. and E.E.; writing--original draft preparation, N.H. and S.D.; writing--review and editing, S.D. and E.E.; visualization, N.H. and S.D.; supervision, A.M.K. and S.D.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Sensor Allocation Model and Algorithm
An exhaustive search algorithm is a possible solution, which enumerates and tests the candidate solutions one-by-one in a certain order to determine the solutions that meet the requirements. However, it is almost impossible to obtain the optimal solution in polynomial time [25]. Mathematical programming can also be employed to achieve a good solution. However, it is sensitive to the shape of the Pareto frontier and must be differentiable for the objective function and the constraints. Comparatively, the metaheuristic algorithm is not subject to such restrictions and is considered a potential and powerful tool to solve MOO problems. Therefore, this work uses the shuffled frog leaping algorithm (SFLA) for sensor-deployment optimization in a wet-grinding system [34].
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 3 Materials and Methods
### Adapting the A* Algorithm for Such Graphs
#### 3.5.2 Expanding a Node on an Open Obstacle  
Expanding a node \(n\) on an obstacle \(O\) implies generating all of the edges that issue from that node. This involves determining all of the free rectilinear bitangents that leave obstacle \(O\) toward other obstacles and determining the convex arcs tangent to the boundary of \(O\), which go from \(n\) to the point of departure of these tangents. When the convex arc and the bitangent do not intersect another obstacle, they correspond to an edge that starts at \(n\).  
Figure 8(a) illustrates a situation in which there are 5 _end_ nodes, represented by black dots, on the boundary of the obstacle \(+O\) and numbered \(n_{j}\), \(i=1\cdots 5\). Figure 8(b) illustrates  
Figure 8: Examples of nodes and edges in our tangent graph.  
the situation that results after one of these nodes, say \(n_{5}\) for example, has been selected for expansion. The expanded node \(n_{5}\) is outlined with a circle and all of the bitangents that leave \(+O\) are shown as rectilinear segments issuing from their point of contact, represented by a white dot. The edges that start at \(n_{5}\) correspond to the tangent convex arcs \(n_{5}\),\(a\), \(n_{5}\),\(b\), \(n_{5}\),\(c\) and \(n_{5}\),\(d\) followed by the bitangents that start, respectively, at the points \(a\), \(b\), \(c\) and \(d\). The bitangent that starts at \(e\) is not part of one of these edges because \(e\) cannot be reached from \(n_{5}\) by a free tangent convex arc.
## 3 Results
### Geographical Detection of the Spatial Differentiation of CI in the Sanjiang Plain
#### 3.4.2 Detection of the Single Factors  
The factor detector of GDM was applied to investigate the impact of each driving factor on the CI by calculating the \(q\) value. The higher the \(q\) value, the stronger the spatial association between the CI and division factors. In this study, we defined the driving factors with \(q\) values greater than 50% as the dominant driving factors, and driving factors with \(q\) values greater than 10% as the secondary driving factors. Natural factors and anthropogenic factors were ranked in descending order according to the magnitude of their impacts on the spatial distribution of CI: Elev > Geomt > Slop > Veget > Landt > Soilt > Temp > Prec > Aspe (first row in Table 5). All of the driving factors passed the significance test (\(p\) < 0.05).  
The \(q\) values of the elevation, geographic type, and slope were the largest (0.7122, 0.7073, and 0.6102, respectively) and accounted for more than 50% of the spatial distribution of CI. Therefore, the dominant factors affecting CI were elevation, geomorphic type, and slope. Furthermore, the secondary driving forces of CI were vegetation type (0.4800), land use type (0.4664), and soil type (0.1818), all of which accounted for more than 10% of the spatial distribution of the CI. The \(q\) values of average annual temperature, annual precipitation, and aspect were below 10%, indicating that they had only a slight impact on the spatial differentiation of CI. However, the factor detector only revealed the impact of a single factor on CI based on the \(q\) value; thus, some driving factors might have a significant influence when interacting with others.
## 5 Conclusions  
In this study, the SM mapping retrieved from the water cloud model, using Sentinel-1 C-band SAR data and Sentinel-2 MSI optical data, was assimilated into WOFOST model with the EnKF algorithm to improve winter wheat yield estimation at a regional scale in Hengshui city. The following conclusions can be drawn:  
* First, SM retrieval results demonstrated that it is feasible to retrieve soil moisture content with the water cloud model by combining remotely sensed Sentinel-1 and Sentinel-2 data. Results showed an acceptable accuracy on three dates where R\({}^{2}\) was 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively.
* Second, the assimilation results indicated that, with assimilation of the SM in the WOFOST water-limited mode, the winter wheat yield estimation achieved a higher accuracy, with R\({}^{2}\) = 0.35 and RMSE = 934 kg/ha, than that without assimilation, with R\({}^{2}\) = 0.21 and RMSE = 1330 kg/ha. Consequently, our results highlight the usefulness and ability of assimilating SM, which was retrieved by combining Sentinel-1 C-band SAR and Sentinel-2 MSI optical data into the WOFOST model to improve regional winter wheat yield estimations.  
The following are available online at [[http://www.mdpi.com/2072-4292/11/13/1618/s1](http://www.mdpi.com/2072-4292/11/13/1618/s1)]([http://www.mdpi.com/2072-4292/11/13/1618/s1](http://www.mdpi.com/2072-4292/11/13/1618/s1)), Table S1: Values of soil parameters for WOFOST model; Table S2. Values of crop parameters for WOFOST model.  
[PERSON], [PERSON] and [PERSON] proposed the original idea for the study; [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments and processed and analyzed the data, [PERSON] wrote the original manuscript. [PERSON], [PERSON], [PERSON], and [PERSON] reviewed the paper.  
This study was funded by the National Natural Science Foundation of China (Project No. 41671418), Fundamental Research Funds for the Chinese Central Universities (Project No. 2019 TC117), Science and Technology Facilities Council of UK-Newton Agritech Programme (Sentinels of Wheat), National Natural Science Foundation of China-Science and Technology Facilities Council Joint Project (Project No. 6151101278) and China Scholarship Council (No.201806350193).  
We thank the journal's editors and anonymous reviewers for their kind comments and valuable suggestions to improve the quality of this paper.  
The authors declare no conflict of interest.
## 2 Background
### Related Work on Multiplicative Noise Reduction
A homomorphic filter is used to convert multiplicative noise into additive white Gaussian noise and then the noiseless signal is reconstructed over image patches (atoms) to create the SR from a learned dictionary. However, noise in flat regions still exists and poor edges make the reconstructed images difficult to analyze. In [34], the authors proposed an image denoising technique that operates directly on multiplicative noise and is based on three terms: SR over an adaptive dictionary, a TV regularization term, and a data-fidelity term. However, the proposed model is nonconvex because of the product between the unknown dictionary and sparse coefficients and the data-fidelity term is a log function. Therefore, solving the squared \(l_{2}\) norm is difficult. This optimization problem is overcome by the split Bregman technique. However, these methods do not contain high- and low-frequency components of the image. We obtain this information using an enhanced homomorphic filter designed to improve the final image. Furthermore, we utilize the advantages of combining a TV regularization term and SR learned over two modified dictionaries.
## 3 Virtual Space-Time Diversity Turbo Equalization
### FS-SE with Anti-Doppler Module
In the tracking stage of equalizer, \(x_{n}\) in Equation (12) is replaced by \(Q[\hat{d}_{n}]\), the estimate produced by the decision function. Figure 3 shows the step updating diagram for ADM. The instantaneous \(\theta_{n}\) generally contain serious  
Figure 2: Anti-Doppler time-domain channel equalizer. noise and cannot be used for numerically controlled oscillator (NCO) control directly. We introduce the integral-zeroing module, a lowpass filter (LPF), for high-frequency noise cancellation. The output of the integral-zeroing module is given as  
\[\bar{\theta}_{i}=\frac{1}{N_{k}}\sum_{n=(i-1)N_{k}}^{iN_{k}-1}\theta_{n} \tag{13}\]  
where \(i\) and \(N_{k}\) are the index and period of carrier NCO update, respectively. The step of the carrier NCO is changed every \(N_{k}\) symbol period. Generally, \(N_{k}\) is in direct proportion to the length of the AD sampling block in our design. We utilize the collected data in a 200 m horizontal range lake trial to verify the performance of the integral-zeroing module. Figure 4 is the comparison of phase detectors, and the integral-zeroing module performs superiorly. The processed \(\bar{\theta}_{i}\) is obviously smoother than \(\theta_{n}\) and able to reflect the true phase error better. Then, \(\bar{\theta}_{i}\) is sent to the one-order loop filter (LF), and the Doppler factor estimate and carrier NCO step are updated as:  
\[ \begin{split}\omega_{i}=(K_{1}+K_{2})\bar{\theta}_{i}-K_{1}\bar{ \theta}_{i-1}+\omega_{i-1}\\ \phi_{i}=\phi_{0}+\frac{\omega_{j}\pi}{2M_{\text{NCO}}}\\ \mu_{i}=\bar{f}_{s}-K_{3}\omega_{i}\end{split}  \tag{14}\]  
where \(\omega_{i}\) is the output of LF, \(K_{1}\) and \(K_{2}\) are the parameters of LF, \(\phi_{i}\) and \(\phi_{0}=2\pi f_{c}/f_{s}\) are the updated step and initial step of carrier NCO, respectively. \(M_{\text{NCO}}\) is the order of carrier NCO, \(\mu_{i}\) is the resampling frequency of interpolator, \(\bar{f}_{s}=f_{s}/(1+\hat{\alpha})\) and \(K_{3}\) is a scaling factor. Moreover, \(K_{4}\) in Figure 3 is given as \(K_{4}=\pi/2^{M_{\text{NCO}}}\) for simplification.
# A Novel Prefix Cache with Two-Level Bloom Filters in IP Address Lookup
## 1 Introduction
In Section 2, we briefly describe the prefix caching for fast packet forwarding and discuss the research related to the prefix expansion techniques to overcome a problem that the prefix caching may cause. Section 3 introduces the Bloom filter and explains how to use it for solving the incomplete caching problem in the prefix caching. Section 4 proposes a two-level Bloom filter-based prefix caching (BFPC) scheme in IP address lookup. In Section 5, the performance is evaluated in terms of the cache miss ratio and other parameters. Finally, we conclude the paper in Section 6.
# Automatic Medical Image Segmentation with Vision Transformer
## 4 Experiments and Discussions
### CT Segmentation Dataset Results  
In this experiment, we compiled a CT infection segmentation dataset, leveraging unlabeled images to augment the limited labeled training data. Our labeled data \(D_{Labeled}\) come from the CT segmentation dataset [13], which has 45 CT images randomly chosen for training, 5 for validation, and 50 for testing.  
The infection segmentation results in Figure 4 show that our network markedly outperforms the baseline algorithms. Specifically, it yields segmentation maps that closely match the ground truth with significantly fewer incorrectly segmented tissue regions. Additionally, U-Net results in inadequate segmentation with many mis-segmented areas. Our success is attributable to a strategy of coarse-to-fine segmenting. First, a parallel partial decoder localizes infection regions roughly, then the segmentation is refined by multiple edge attention modules. This mimics radiologists' workflow of segmenting infections from CT images, and consequently achieves a promising performance. Furthermore, Figure 4 confirms the superiority of our semi-supervised approach over baselines. Our model produces segmentation with more accurate boundaries, while baselines result in relatively fuzzy boundaries, particularly in regions of subtle infection. The additional unlabeled data and multi-scale edge attention allow our model to capture fine-grained infection patterns and boundaries.  
As shown in Table 1, our proposed model has specificity 0.01 higher than U-Net and 0.03 than Attention U-Net. It also has a lower MAE than the baselines. This improvement is likely due to our model's implicit mechanism of reverse attention and unambiguous models of edge attention modules providing strong feature extraction abilities.  
Overall, our model outperforms the baselines on most metrics. The additional unlabeled data and refined attention mechanisms allow our model to better distinguish infection regions.