esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Data Communication of Enhanced Loran Systems Using 128-_ary_ Polar Codes
## 2 Materials and Methods
### The Proposed 128-ary Polar Coding Scheme for eLoran
#### 2.2.3 Construction of 128-ary Polar Code for eLoran  
The performance of a polar code is critically dependent on the selection of the information set \(A\) as defined in Section 2.2.1, which comprises the indices of the subchannels designated to carry information symbols. The process of identifying \(A\) is known as polar code construction, and commonly used methods for polar code construction include the Bhattacharyya parameter [19], density evolution [27], Gaussian approximation [28], Monte Carlo simulation [29], and the polarization weight method [30].  
In this paper, we adopt the Monte Carlo construction method to incorporate the specific characteristics of 128-\(\it{ary}\) PPM and the proposed SS demodulation into the construction process. In the Monte Carlo construction method, a large number of trials are simulated for each polarized subchannel to accurately estimate its channel error rate (CER). Crucially, these simulations incorporate both 128-ary PPM and the proposed SS demodulation to yield realistic CERs for the system. Subsequently, the CERs of all polarized subchannels are sorted. The \(K\) subchannels with the smallest CERs are selected for transmitting the information symbols, forming the information set \(A\). The indices of the remaining subchannels constitute the frozen set, \(A^{c}\).  
For each trial of the Monte Carlo construction, we first perform 128-_ary_ polar encoding on a random sequence with length \(N=32\) to generate a codeword. Subsequently, the QUP algorithm is applied to produce a punctured codeword of length \(M=30\). This punctured codeword is then mapped to 128-_ary_ PPM and transmitted over an Additive White Gaussian Noise (AWGN) channel. The channel operates at a signal-to-noise ratio (SNR) of 2.5 dB, with the specific definition of the SNR provided in Section 3. At this SNR, the largest CER among the \(K\) smallest CERs is approximately 0.01. Following ideal synchronization, the received signal undergoes SS demodulation, which is then followed by SC decoding.  
By repeatedly executing this process \(10^{6}\) times, the number of errors for the \(i\)-th (\(i\in\{1,\,2,\ldots,N\}\)) polarized subchannel is counted. During this error counting, it is assumed that the decoding results of the preceding subchannels (from the 1 st to the \((i-1)\)th) are perfectly known and correct. Subsequently, the CERs are sorted, and the indices of the \(K=10\) polarized subchannels exhibiting the lowest CERs are designated as the information set \(A\).  
Figure 3 illustrates the resulting CERs. As expected, for one subset of polarized subchannels, the CER approaches zero, while for another subset, the CER approaches one (specifically, 127/128). As indicated by the \" markers in Figure 3, the \(K=10\) subchannels with the smallest CERs are selected to form the information set \(A=\{16,23,24,26,27,28,29,30,31,32\}\), and the '\(\cdot\)' markers represent the frozen set \(A^{c}\). Notably, the subchannels numbered 31 and 32 are reserved for CRC symbols.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### Pseudo-Code of the Algorithm Used for Simulations  
In Section 4, many options for linear computation coding are presented with various trade-offs between performance and complexity, some of them even being NP-hard, which prevents them from being implemented unless the target matrices are very small. In order to clarify the algorithm we have used in our simulation results, we provide its pseudo-code here.  
Algorithm 1 requires a zero mean target matrix \(\mathbf{T}\) as input. The number of additions per row for the \(\ell\)-th wiring matrix is a free design variable, which is conveniently set to unity. Any initial codebook can be used. Algorithm 1 calls the subroutine Algorithm 2 to perform the decomposition in (14) by means of greedy wiring.  
```
1:procedureMatrixFactorization
2:\(\mathbf{T}\leftarrow\) zero-mean target matrix
3:\(\mathbf{B}\leftarrow\) identity matrix with same size as \(\mathbf{T}\) or other codebook matrix
4:\(S_{\ell}\leftarrow\) number of additions per row for \(\ell\)-th wiring matrix
5:\(\ell\gets 0\)
6:loop:
7:ifT andB differ too muchthen
8:\(\ell\leftarrow\ell+1\)
9:\(\mathbf{W}(\ell)\leftarrow SubroutineGreedyWiring(\mathbf{T},\mathbf{B},S_{ \ell})\)
10:\(\mathbf{B}\leftarrow\mathbf{W}(\ell)\mathbf{B}\)
11:goto loop.
12:else
13:return the matrix factors \(\mathbf{W}(1)\ldots\mathbf{W}(\ell)\)
```  
**Algorithm 1** Algorithm used in the simulation results.  
Algorithms 1 and 2 are suited for tall matrices as used in the simulation section. This is in contrast to the wide matrices used in Sections 4.2-4.5. For wide instead of tall matrices, just transpose both inputs and outputs of the two algorithms.  
```
1:procedureSubroutineGreedyWiring(\(\mathbf{T}\), \(\mathbf{B}\), \(S\))
2:\(k\leftarrow\) number of rows in \(\mathbf{T}\)
3:\(\mathbf{W}\gets k\times k\) all zero matrix
4:outer loop:
5:if\(S\geq 0\)then
6:\(k\leftarrow\) number of rows in \(\mathbf{T}\)
7:inner loop:
8:if\(k>0\)then
9:\(n\leftarrow\) index of row in \(\mathbf{B}\) that if scaled with a signed power of 2  is closest to \(k\)-th row of \(\mathbf{T}\)
10:\(\mathbf{W}(k,n)\leftarrow\mathbf{W}(k,n)+\) signed power of 2 that was used to find \(n\)
11:\(k\)-th row of \(\mathbf{T}\gets k\)-th row of \(\mathbf{T}-\mathbf{W}(k,n)\times n\)-th row of \(\mathbf{B}\)
12:\(k\gets k-1\)
13:goto inner loop.
14:\(S\gets S-1\)
15:goto outer loop.
16:else
17:return the matrix \(\mathbf{W}\)
```  
**Algorithm 2** Subroutine used in Algorithm 1.
## 3 Aim, Materials, and Methods
### Multi-Enhancement Command and Scheduling Model for Communication Network
#### 4.2.2 Disaster Research and Decision-Making  
Through the utilization of received early warning information, a combination of big data processing and expert support facilitates scientific disaster research and decision-making, thereby providing a foundation for emergency command. The collected early warning information undergoes rapid analysis, mining, and processing using advanced big data techniques to ascertain the correlation, impact magnitude, and occurrence probability among different types of disasters. This analysis identifies key areas and time periods that may pose threats to human lives and property while offering pertinent decision-making support for subsequent disaster research and judgment processes. Furthermore, relevant departments coordinate with experts in meteorology, geology, and water conservancy to consult on and evaluate the outcomes derived from big data analysis. Leveraging their extensive experience and professional knowledge enables more accurate forecasting of disaster development trends while delivering comprehensive research findings to emergency commanders.
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 3 PL-Based RAIM Availability Assessment
### Deficiency of Classic- and Enhanced-PL-Based RAIM Availability Assessment  
Here defines H\({}_{0}\) as \"\(\forall\xi_{b},P(\text{HMI}|1F)<P_{r}(\text{HMI}|1F)\)\" and H\({}_{1}\) as \"\(\exists\xi_{b},P(\text{HMI}|1F)\geq P_{r}(\text{HMI}|1F)\)\" in the worst case. Therefore \"\(\text{VPL}<\text{VAL}|H_{1}\)\" or \"\(\text{VPL}\geq\text{VAL}|H_{0}\)\" respectively mean the RAIM availability assessment is optimistic or conservative.  
For \(\forall\xi_{b}\)  
\[\begin{split}\text{VPL}<\text{VAL}\Leftrightarrow P(|\text{VPE} |\geq\text{VPL}|1F)P(\text{Ts}<Td|1F)>\\ P(|\text{VPE}|\geq\text{VAL}|1F)P(\text{Ts}<Td|1F)\\ \Leftrightarrow P_{a}(\text{HMI}|1F)>P(\text{HMI}|1F)\end{split} \tag{11}\]  
and  
\[\text{VPL}\geq\text{VAL}\Leftrightarrow P_{a}(\text{HMI}|1F)\leq P(\text{ HMI}|1F) \tag{12}\]  
where \(P(|\text{VPE}|\geq\text{VPL}|1F)P(\text{Ts}<Td|1F)=P_{a}(\text{HMI}|1F)\),  
Similar to \(P(\text{HMI}|1F)\), \(P_{a}(\text{HMI}|1F)\) changes with the \(\xi_{b}\) value. Moreover, the value of \(P_{a}(\text{HMI}|1F)\) is known at \(\xi_{b}=\sigma_{m}^{2}\sqrt{\frac{\lambda_{a}}{s_{mn}}}\), \(P_{a}(\text{HMI}|1F)=0.5P_{MD}\) for \(\text{VPL}_{c}\) and \(P_{a}(\text{HMI}|1F)=P_{MD}^{2}\) for \(\text{VPL}_{e}\). A \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve can be generated, which is higher than the \(\xi_{b}-lgP(\text{HMI}|1F)\) curve with \(\text{VPL}<\text{VAL}\) and lower than the \(\xi_{b}-lgP(\text{HMI}|1F)\) curve with \(\text{VPL}\geq\text{VAL}\). The positional relationship among the \(\xi_{b}-lgP(\text{HMI}|1F)\) curve, the \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve, and the \(lgP_{r}(\text{HMI}|1F)\) line jointly determine whether the PL-based RAIM availability assessment is conservative or optimistic. The following is a RAIM availability assessment discussion according to the positional relationship between the \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve and the \(lgP_{r}(\text{HMI}|1F)\) line.
# Online Deformation Measurement of Laser Repair Substrate Based on Orthogonal Sampling Moire
## 4 Results and Discussion
### Serpentine Scanning in \(x\) Direction  
The cladding results corresponding to different scanning speeds under the serpentine scanning path in the \(x\) direction are shown in Figure 17a. Although the laser continues to act on the surface of the component in the serpentine scanning, there is still a heat dissipation time for the component due to the long scanning path in the \(x\) direction, resulting in the periodicity of the displacement curve in Figure 17b. Compared with Figure 15b, when scanning in the unidirectional mode, the curves are more compact under the serpentine scanning, indicating that the deformation results under different scanning speeds are closer. Furthermore, under the serpentine scanning, the laser continuously excites the component to accumulate the heat of the component, which leads to a larger final residual deformation.  
The distribution of equivalent strain and the main direction of strain at the beginning and end of printing are shown in Figure 18a. Additionally, the variation curves of equivalent strain with time are shown in Figure 18b. Similar to unidirectional scanning, with the progress of scanning, the equivalent strain of the four points increases linearly with different slopes at the same time until the end of printing. The difference is that there is a cooling process in unidirectional scanning. Thus, during the cooling period, the equivalent strain changes greatly and the curve fluctuates regularly. However, when scanning in serpentine paths, the laser continues to excite the substrate. Therefore, the fluctuation is not obvious, although it still shows periodic changes due to laser scanning back and forth.  
Figure 16: (**a**) The equivalent strain field at the beginning and the end of printing at 10 mm/s, and (**b**) the time-dependent curve of the equivalent strain at four points A, B, C and D.  
Figure 17: (**a**) Cladding results at different scanning speeds, (**b**) displacement evolution at different scanning speeds and (**c**) displacement field in \(z\) direction in a single cycle at 10 mm/s.  
Figure 18: (**a**) The equivalent strain field at the beginning and end of printing at 10 mm/s and (**b**) variation curve of equal effect change at four points A, B, C and D with time.  
The above comparison results show that the scanning form of unidirectional or serpentine has little influence on the residual deformation of the component. However, the residual deformation after scanning along the \(x\) direction varies a lot from scanning along the y direction. When scanning is performed along the \(x\) direction of the cantilever beam, the residual deformation can be up to twice that along the \(y\) direction. In addition, it is shown that the lower the speed of the laser scanning is, the more severe the deformation is.
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 1 Introduction
Unlike feature-level fusion, pixel-level fusion methods directly integrate the raw data provided by multiple sensors, compensating for errors during the data processing stage to provide high-information, low-error input data for subsequent data analysis. This study, concerning multi-source melt pool data obtained from an infrared thermal camera and a high-speed camera, proposes the use of data-level fusion methods to generate rich, low-error fusion data, addressing the inaccuracy of temperature distribution data from the infrared thermal camera and overcoming the limitations of insufficient information from a single data source. Currently, multi-source data collected by high-speed cameras and infrared thermal cameras exhibit data errors, information discrepancies, and differences in data volumes. Moreover, the frame rate and resolution of high-speed cameras are both higher than those of infrared thermal cameras. In additive manufacturing monitoring with multiple sensors, downsampling methods are commonly used to balance data volume differences, but this limits the ability to effectively utilize disparate data for melt pool monitoring. Therefore, effectively integrating the distinctly different data obtained from high-speed cameras and infrared thermal cameras is a challenge in enhancing melt pool monitoring capabilities and ensuring processing quality. Deep learning-based video frame interpolation technology is commonly used to address the issue of insufficient frame rates in visual sensors, as this technology generates intermediate frames by analyzing the motion characteristics of the subject [16]. However, the non-linear motion characteristics of the melt pool in additive manufacturing and the difficulty of data acquisition make it challenging for models to effectively learn the motion features of the melt pool. In response to the differences in multi-source data, this paper proposes a multi-source data fusion method for laser processing melt pools based on video frame interpolation. This method combines video frame interpolation models with image fusion models and designs a training strategy based on pre-training to achieve the fusion of infrared and visible-light video data with different frame rates and information differences, thereby obtaining high-quality fused data, which are ultimately applied to the analysis of melt pool evolution behavior during the laser cladding process. The structure of this paper is as follows: Section 2 describes the construction of the data acquisition system and the establishment of the dataset; Section 3 elaborates in detail on the multi-source data fusion method proposed in this study; Section 4 analyzes the experimental results and verifies the effectiveness of the proposed method; Section 5 summarizes the main contributions of this research and discusses future research directions.
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 4 Experimental Results
### Segmentation Results of Noisy Images  
The following subsection describes the experimental segmentation results obtained for noisy images. The SPFLIF-IS model is still compared with the C-V, LBF, SBGFRLS, LIF, and LSACM models. Figure 4 illustrates the original images with different noise intensities and compares the results of the six state-of-the-art segmentation methods, where the original images without noise in Figure 4a are derived from [46]. In Figure 4, Row 1 shows the original images and the segmentation results. Row 2 to Row 5 show the added Gaussian noise with zero means and different variances (\(\sigma\) = 0.01, 0.02, 0.03, 0.05). Figure 4c,f shows that the LBF model and the LSACM model cannot analyze the five images. Although the C-V model and the SBGFRLS model can segment the first and the second image, neither model performs well when the noise intensity increases; the results are shown in Figure 4b,d. Figure 4e shows that the LIF model could analyze the images without Gaussian noise well. With respect to the segmentation of the images containing Gaussian noise, the LIF model exhibits poor performance. As shown in Figure 4g, the object boundaries are accurately extracted by our proposed SPFLIF-IS model. Table 3 describes the segmentation results of Figure 4. Table 3 shows that the SPFLIF-IS model yields the best results, the C-V model performs as well as the SBGFRLS model, and the LBF model performs as poorly as the LSACM model. The experimental results demonstrate that the SPFLIF-IS model can effectively eliminate the interference of the noise and complete the segmentation of the noisy images.
## 4 System Design
### Technical Requirements  
Based on the described application functional requirements, a technical solution can be planned, and several technical capabilities can be extracted:  
1. Users can create accounts, and their credentials are stored securely in a persistent manner. With the same credentials, users can authorize actions and interact with the application resources
2. In a session, users can create the base document resource and act upon it with CRUD (Create Read Update Delete) operations [26]. All of the described operations must be persisted on the final resource. Only the appropriate document owner can interact with the resource at this level.
3. Being a real-time application, users should be informed about events in a near real-time manner (notification system). When a live update is not possible, users should receive the missing updates in an alternative manner.
4. The system must provide a real-time document editing experience to all the involved peers. Users should be able to dispatch events resembling the natural interactions a human would do normally on a text resource. The system must handle a considerable number of users simultaneously. As magnitude, the system must be able to support concurrent users in the order of thousands.
5. The system must use appropriate communication technologies to deliver the promised functionality. Data should be delivered in such a way that 95% of the users receive a response in an appropriate time frame with the designated action they executed.
6. The persistent solution must provide a robust and reliable storage layer where all the relevant generated user content is going to be stored.
## 3 Results and Discussion
### Structure and Surface Morphology  
The XRD patterns of the as-deposited thin films are shown in Figure 2. All the thin films were amorphous, which might be related to the low temperature of the sputtering process and long target-substrate distance; therefore, the particles reaching the substrates had low energy not sufficient for the nucleation of a crystalline structure. Other similar results have been presented for vanadium oxide thin films deposited by magnetron sputtering without additional annealing, e.g., [23; 24; 25]. Moreover, in the case of tungsten oxides thin films deposited by the same gas impulse magnetron sputtering method, the results of the XRD measurements also showed that they were amorphous [30]. In this way, further analysis of other properties does not depend on the microstructure of the thin films but rather on the sputtering atmosphere during the deposition process. It is also worth mentioning that amorphous thin films should exhibit better optical properties, e.g., transmission coefficient, compared with those of the crystalline thin films.  
Figure 1: Dependence of the deposition rate of vanadium oxides thin films on the Ar:O\({}_{2}\) gas ratio during magnetron sputtering.  
The SEM images in Figure 3 show the surface morphology of the as-deposited vanadium oxide thin films. Thin films deposited with ArO\({}_{2}\) ratios between 8:1 and 6:1 are smooth, uniform, crack-free and composed of very small nanograins. A further increase in the oxygen content in the sputtering atmosphere resulted in an increase in grain size (Figure 3d-f), but the thin films were still homogenous and crack-free. In contrast, the sample deposited with an Ar:O\({}_{2}\) ratio of 2:1 was inhomogenous, rather rough, and composed of grains of various sizes. The EDS spectrum measured for a vanadium oxide thin film deposited with an Ar:O\({}_{2}\) ratio of 4:1 is shown in Figure 3h and has peaks for vanadium and oxygen related to the material composition of the film and for silicon, which is the substrate material. Since the thickness of the vanadium oxide thin film is approximately 320 nm, the silicon peak of the K\(\alpha\) line (at 1.74 keV) is large and visible because the penetration depth of the EDS beam is also large. No other peaks related to the other elements were found, indicating good purity of the deposited coatings.
# MUSIC: Cardiac Imaging, Modelling and Visualisation Software for Diagnosis and Therapy
## 4 Functionalities
### Electrocardiographic Imaging  
In the Simulation workspace of MUSICardio, we developed different tools in order to enable the use of computational models for simulations along with their integration with multimodality imaging data. Of particular concern are the tools developed to improve the resolution of ElectroCardioGraphic Imaging (ECGI) inverse problems. Briefly, ECGI is a non-invasive technique that allows clinicians to construct electrical information on the heart surface or volume from electrical measurements on the body surface and patient-specific heart and torso geometries. The ECGI tools that are developed in MUSICardio are based on the formulation of [PERSON] et al., who first related the epicardial and body surface electrical potentials by means of a transfer matrix that depends on the geometry of the heart and the torso of the patient [11]. Both surface and volume ECGI functionalities follow the following pipeline:  
* Load the 3D image data of the patient (i.e., CT image scan or MR images).
* Segment body surface and heart surface or volume geometry and construct their corresponding masks. The user can also segment other organs such as lungs, bones and the liver. Figure 9a shows a 2D axial cross-section view of a segmented geometry showing the volume of the heart, lungs, liver and the body surface.
* Generate computational meshes of the segmented geometries. Figure 9b shows an example of a generated 3D computational geometry.
* Identify the position of electrodes in the body surface mesh (semi-automatic).
* Construct a transfer matrix based on finite elements method (FEM) or on the method of fundamental solutions (MFS) [12]. Details about both methods can be found in [13].
* Load the body surface electrical recordings.  
Figure 8: Automatic contouring of targets and organs at risk. The contours can be exported to the standard DICOM Rt-Struct format. Each ablation target and organ at risk is segmented with a color code: the list of contour names is displayed in a table in the right part of the application, as shown in this screenshot.  
* Construct the electrical potential on the heart surface or the current density on the heart volume by solving the ECGI inverse problem. Several approaches have been implemented using different regularisation methods, see [13].
* Visualise the electrical information constructed on the surface or the volume of the heart. Figure 10 illustrates an example of the electrical potential reconstruction using MFS (b) and FEM (c) as well as the ground truth of the corresponding solution (a).  
For the post processing of the computed electrical potential, our ECGI pipeline script in Python allows one to compute activation maps.  
Figure 10 shows an example of typical results that can be obtained using MUSICardio.