esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sub-Zoning Pressure Prediction Results for the Supports in the Ultra-Long Mining Face
### Analysis of Support Resistance in Different Areas of the Ultra-Long Mining Face  
(1) Variation in support resistance with advancement distance in different areas.  
The supports in the #202 ultra-long mining face were divided into three groups along the strike, i.e., lower (#10-#30), middle (#40-#180), and upper (#190-#220) groups. The prediction results revealed the zoning characteristics of the ultra-long fully mechanized mining face.  
Figure 9 presents the working resistances of the representative supports. The average resistance of the lower supports (#10-#30) was 9723.17-10,141.65 kN, suggesting a low overall resistance. The average working resistance of the middle supports (#50-#140) was 11,423.47-13,016.48 kN, indicating a high resistance and large variations. The average working resistance of the upper supports (#200-#220) was 9821.96-11,151.89 kN, implying a low working resistance. In conclusion, during the advancement of the ultra-long fully mechanized mining face, the working resistance of the supports had obvious zoning characteristics along the strike and exhibited a low-high-low pattern.  
Figure 8: Comparison of the predicted and measured working resistance of the #60 support.  
Figure 9: Working resistance distribution characteristics of different areas along the strike, they should be listed as: (**a**) lower measuring area (#10–#30); (**b**) middle measuring area (#40–#180); (**c**) upper measuring area(#190–#220).  
(2) Zoning characteristics of the support resistance under the mining face under roof weighting.  
As shown in Figure 10a, when the mining face was first subjected to roof weighting, the average working resistance of the middle supports (#40-#90) was 15,598.39 kN, and the average working resistance of supports #130-#180 was 15,402.35 kN. The resistance of the lower and upper supports was low; the average resistance of supports #10-#30 was 12,335.15 kN, and that of supports #190-#220 was 12,155.7 kN. As can be seen from Figure 10b, when the mining face was first subjected to periodic roof weighting, the average resistance of the middle supports (#30-#90) was 15,549.95 kN, and that of supports #14-#190 was 15,915.20 kN. The working resistance of the lower and upper supports was low. The average resistance of supports #10-#20 was 11,698.79 kN, and that of supports #200-#220 was 12,505.33 kN. In contrast to mining faces with a normal length, the working resistance of the supports along the ultra-long mining face was high in the lower and upper parts of the middle area (i.e., #40-#80 and #120-#180) during the periodic roof weighting, exhibiting a low-high-medium-high-low distribution pattern along the strike.
## 2 Experimental design
### Test Loads
#### 2.4.1 The Calculation Theory of Wind, Wave and Current
The specific calculation process is as follows:  
The ocean current velocity term is introduced into the calculation of the drag force, and the drag force is calculated according to the following formula:  
\[f_{D}=\frac{1}{2}C_{D}\rho D(u_{x}+u_{c})|(u_{x}+u_{c})| \tag{5}\]  
For a cylinder, the inertial force acting on the pile can be calculated by formula:  
\[f_{I}=C_{M}\rho\frac{\pi D^{2}}{4}\frac{\partial u_{x}}{\partial t} \tag{6}\]  
The horizontal wave force acting on the unit column height at any position \(z\) of the upright column is:  
\[f_{H}=f_{D}+f_{I}=\frac{1}{2}C_{D}\rho D(u_{x}+u_{c})|(u_{x}+u_{c})|+C_{M}\rho \frac{\pi D^{2}}{4}\frac{\partial u_{x}}{\partial t} \tag{7}\]  
In the formula, \(f_{D}\) is the drag force; \(f_{I}\) is the inertial force; \(D\) is the diameter of the cylinder; \(\rho\) is the seawater density; \(C_{D}\) is the drag force coefficient; \(C_{M}\) is the inertial force coefficient; \(u_{x}\) is the horizontal velocity of wave water quality point; \(u_{c}\) is the current velocity. In addition, \(C_{D}\) and \(C_{M}\) can usually be obtained from model tests and field observations. Due to the differences in sea conditions in different countries, the results have great dispersion. According to the load conditions of this study and referring to the marine engineering code, thus \(C_{D}=1.2\), \(C_{M}=2.0\). Figure 3: Time history curve of wind load at fan hub. The velocity and acceleration of water quality point motion can be obtained by performing partial differential processing on the velocity potential function. The formulas are expressed as follows:  
\[u_{x}=\frac{\partial\phi_{V}}{\partial x}=\frac{\pi H_{V}}{T}\frac{\cosh kz}{ \sinh kd}\cos(kx-\omega t)+\frac{3}{4}\frac{(\pi H_{V})^{2}}{L_{V}T}\frac{\cosh 2 kz}{\sinh^{4}kd}\cos 2(kx-\omega t) \tag{8}\]  
\[\frac{\partial u_{x}}{\partial t}=\frac{2\pi^{2}H_{V}}{T^{2}}\frac{\cosh kz}{ \sinh kd}\sin(kx-\omega t)+3\pi\frac{(\pi H_{V})^{2}}{L_{V}T^{2}}\frac{\cosh 2 kz}{\sinh^{4}kd}\sin 2(kx-\omega t) \tag{9}\]  
According to the actual wave parameters, the water depth in this test is 5 m, the current velocity is 0.88 m/s, the wave height is 2.13 m, and the wave period is 10.86 s. According to the scope of application of the wave theory proposed by [PERSON] (1968) [20], it satisfies the conditions for the use of [PERSON] second-order waves. The calculation results of wave and current loads are shown in Figure 4.
## 4 Proposed Algorithm
### Genetic Algorithm
#### 4.3.1 Chromosome Representation  
Each chromosome consists of four vectors: camera position, type, azimuth, and elevation vectors, as shown in Figure 5. The length of a chromosome represents the maximum number of cameras. The first vector in a chromosome denotes the camera position (\(S(x_{s},y_{s},z_{s})\)). If there is no camera at a position, the value of the gene is 0. The second vector represents the type of the camera position. The third and fourth vectors represent the azimuth and elevation of the camera, respectively. These two vectors determine the pose of the camera (\(\psi(\psi_{h},\ \psi_{v})\))). The initial population is generated by selecting camera placements randomly within the maximum number of cameras until the chromosome satisfies the minimum coverage level.  
Figure 5: Chromosome representation. A chromosome has four vectors: 3-D camera position, camera type, azimuth, and elevation.
# Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation
## 5 Experiments
### Dataset  
To evaluate the performance of KGET, we conducted the experiment in two scenarios: music recommendations and book recommendations. So, we used two benchmark datasets: Last.FM and Book-Crossing, respectively. These datasets were obtained from different domains and have different sizes and sparsity.  
Last.FM. The dataset is provided by last.fm online music system, which contains music artist listening information from 2 thousand users, where tracks are defined as items.  
Book-Crossing. The dataset contains readers' ratings of different books from the book-crossing community. The ratings range from 0 to 10.  
Because these two datasets with explicit ratings are not suitable to be applied to the proposed model KGET, the explicit ratings are transformed explicit feedbacks. Thus, 1 indicates positive samples that the user has rated the items positively. On the contrary, 0 is marked as negative samples which are randomly sampled from unrated items for the user. Due to the sparsity of Last.FM and Book-Crossing, no threshold is set, and all ratings are viewed as the positive samples. Besides the interactions, the knowledge graph about the item needed to be constructed for the two datasets. We followed the work of [24] to construct sub-KGs for Last.FM and Book-Crossing from Microsoft Satori KG. The confidence level of the subset is greater than 0.9.  
To verify the proposed method KGET in data sparsity, where items have few triples, there are still a large number of items with more triples removed from the current dataset. The current dataset was processed as follows: First, we measured the frequency of itemsappearing in the knowledge graph. Second, we extracted random entities that appear less frequently. Finally, we only retained user interaction information associated with the extracted entities. (1) Last.FM contains 9366 entities and 60 relations which is used for the recommendation task. Particularly, we sample the items which have associated triplets between 3 and 40 in KG. Finally, there are 13,729 user-item interactions information extracted from 42,346 interactions. (2) Book-Crossing consists of 77,903 entities and 25 relations. We randomly sampled the items which have associated triplets between 4 and 80 in KG, and there are 11,615 user-item interactions information extracted from 139,746 interactions.  
We selected 60% of user-item interactions as the training set, 20% of the remaining interactions data were used as the validation set, and the rest of the interactions serve as the test set. This method ensures that the three subsets are not duplicated. Training sets were used to train the model. We fine-tuned the parameters in the validation set. We evaluated the performance of the model in the test set.  
The detail statistics for two datasets are shown in Table 2.
# Delamination Effect on the Buckling Behaviour of Carbon-Epoxy Composite Typical Aeronautical Panels
## 1 Introduction
Since most composite materials tend to be brittle, when subjected to compressive loads, structural failure can occur suddenly [21; 22]. Therefore, it is difficult to determine whether premature buckling or compressive failure has occurred by observing an experimental test, since both buckling and collapse occur too quickly. Similarly, it is not possible to determine whether buckling has occurred on the basis of a post-failure examination. Based on these statements, in the preliminary design phase of an aircraft component, if one wanted to optimise the structure to delay the buckling load and damage propagation according to operational load specifications, one would need to manufacture and test several configurations, with a huge economic burden. Hence, the idea at the base of this paper to perform a sensitivity analysis, varying specific geometric parameters, aimed at studying the local buckling load of the circular delamination, the buckling of the skin and the global buckling of the reinforced panel. Furthermore, through the use of the virtual crack-closure-technique-based sMart time XB numerical procedure [23; 24], the delamination initiation load and propagation were determined. The geometrical parameters were varied to highlight the criticalities associated with the propagation of delaminations in terms of damage-tolerant structures. The paper is structured into sections: Section 2 briefly describes the used numerical procedure, Section 3 reports the geometrical characteristics of the different analysed stiffened panels, Section 4 discusses the obtained results and finally Section 5 contains the conclusions of the manuscript.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 2 Preliminaries  
### SC Decoding Algorithm  
A polar code \((x_{1},x_{2},\cdots,x_{N})\) with length \(N=2^{n}\) can be encoded by \((x_{1},x_{2},\cdots,x_{N})=(u_{1},u_{2},\cdots,u_{N})\mathbf{F}^{\otimes n}\), \(\mathbf{F}=\begin{bmatrix}1&0\\ 1&1\end{bmatrix}\), \(\otimes\) is Kronecker operation. Considertwo independent and identically distributed random variables \(u_{1}\) and \(u_{2}\), which are encoded by polar codes:  
\[(x_{1},x_{2})=(u_{1},u_{2})\mathbf{F}=(u_{1},u_{2})\begin{bmatrix}1&0\\ 1&1\end{bmatrix}=(u_{1}\oplus u_{2},u_{2}). \tag{1}\]  
Obviously, at this time, \(N=2\), \(n=1\). As shown in Figure 1, \((x_{1},x_{2})\) passes through a binary discrete memoryless channel (BDMC) with transition probability \(W(y|x)\). Then, the received signal \((y_{1},y_{2})\) is obtained, but we always use its log likelihood ratio (LLR) \((L_{1},L_{2})\). The process of solving \((u_{1},u_{2})\) from \((L_{1},L_{2})\) is the decoding process.  
As shown in Figure 1, we solve \(u_{1}\) using the \(u_{1}=f(L_{1},L_{2})\) operation, and solve \(u_{2}\) using the \(u_{2}=g(u_{1},L_{1},L_{2})\) operation; indeed, \(f\) operation and \(g\) operation are conditional probabilities. Specifically, we solve \(u_{1}=0\) when \(\frac{\Pr(y_{1}y_{2}|u_{1}=0)}{\Pr(y_{1}y_{2}|u_{1}=1)}\geq 0\); otherwise, \(u_{1}\)\(=1\).  
\[\begin{array}{ll}\Pr(y_{1}y_{2}|u_{1})&=\frac{\Pr(y_{1}y_{2}u_{1})}{\Pr(u_{1 })}=\frac{\sum_{u_{2}\in(0,1)}\Pr(y_{1}y_{2}u_{1}u_{2})}{\Pr(u_{1})}\\ &=\frac{\sum_{u_{2}\in(0,1)}\Pr(y_{1}y_{2}|u_{1}u_{2})\Pr(u_{1}u_{2})}{\Pr(u_ {1})}\\ &\stackrel{{(a)}}{{=}}\frac{1}{2}{\sum}_{u_{2}\in\{0,1\}}\Pr(y_{ 1}y_{2}|x_{1}x_{2})\\ &\stackrel{{(b)}}{{=}}\frac{1}{2}{\sum}_{u_{2}\in\{0,1\}}\Pr(y_{ 1}y_{1}|x_{1})\Pr(y_{2}|x_{2})\\ &=\frac{1}{2}{\sum}_{u_{2}\in\{0,1\}}\Pr(y_{1}|u_{1}\oplus u_{2})\Pr(y_{2}|u_ {2})\end{array} \tag{2}\]  
where \((a)\) means \(\Pr(u_{1})=0.5\); \(\Pr(u_{1}u_{2})=0.25\); \((b)\) means \(W\) is a BDMC.  
\[\begin{array}{ll}\ln\frac{\Pr(y_{1}y_{2}|u_{1}=0)}{\Pr(y_{1}y_{2}|u_{1}=1)}& =\ln\frac{\sum_{u_{2}\in(0,1)}\Pr(y_{1}|u_{2})\Pr(y_{2}|u_{2})}{\sum_{u_{2}\in (0,1)}\Pr(y_{1}|\oplus u_{2})\Pr(y_{2}|u_{2})}\\ &=\ln\frac{\Pr(y_{1}|0)\Pr(y_{2}|0)+\Pr(y_{1  
arrow with chain line represents the \(g\) operation; the upward solid arrow with dotted line represents the direction of data return, which is calculated through (1). The serial numbers in the figure record the counting process of decoding time step. The total number of decoding step is 81. Note that the R0 does not need a data return. Unless otherwise specified, the meanings of the arrows in this paper are the same as above.
# Combined Channel Estimation with Interference Suppression in CPSS
## 3 Channel Estimation with Interference Suppression
### Noise Cancellation in CIR
\begin{array}{l}d^{2}(q_{m},v_{i})=\frac{(q_{m}-v_{i})^{2}}{\hat{\sigma}_{2} ^{2}},\\ d^{2}(q_{m},u_{i})=\frac{(q_{m}-u_{i})^{2}}{\hat{\sigma}_{1}^{2}},\\ D_{m}=\{d^{2}(q_{m},v_{i})\}\cup\{d^{2}(q_{m},u_{j})\}.\end{array}  \tag{18}\]  
**Step 2. ** Sort \(D_{m}\) according to ascending order and find out the first \(k\) elements. \(T_{m}^{NN}\) is a set of first \(k\) elements, which mean that the \(k\) nearest neighbor of \(q_{m}\), and \(q_{m}\) will be classified by majority vote in \(T_{m}^{NN}\):  
\[G(q_{m})=\operatorname*{arg\,max}_{G_{r}}\sum\limits_{t_{k}\in T_{m}^{NN}} \delta[G_{r}=G(t_{k})], \tag{19}\]  
where:  
\(G(q_{m})\) represents the classification of the training samples \(t_{k}\),  
\(\delta[G_{r}=G(t_{k})]\) is Kronecker Delta function,  
\(r\in\{1,2\}\) represents the two classifications as \(G_{1}\) and \(G_{2}\). **Step 3. ** Update \(G_{1}\) or \(G_{2}\) according to \(G(q_{m})\), and update the corresponding mean and variance. Reiterate the three steps until all the samples in \(\hat{h}_{LS}^{(2)}(m,I)\) have been classified. Then, we set all the samples to zero in \(G_{2}\) for noise cancellation. Finally, we obtain the channel estimation as:  
\[\hat{h}_{LS}^{(3)}(m,I)=h^{\prime}(k,I). \tag{20}\]  
Signal components and noise components are independent of CIR in the time domain. The classifying error of KNN in two categories is presented in [27]:  
\[R_{NN}^{*}\leq\rho_{k}(R_{NN}^{*})\leq\rho_{k-1}(R_{NN}^{*})\leq...\leq\rho_{ 2}(R_{NN}^{*})\leq\rho_{1}(R_{NN}^{*})=2R_{NN}^{*}(1-R_{NN}^{*}), \tag{21}\]  
where \(R_{NN}^{*}\) is Bayes probability of error, and \(k\) is the number of nearest neighbors. It can be seen from Equation (21) that noise cancellation with the KNN algorithm will be close to the Bayes probability of error when the nearest neighbors \(k\) is big enough. We have achieved interference suppression in the CIR of frequency domain by the \(K\)-means algorithm and noise cancellation in the CIR of time domain by the KNN algorithm. Although the original data on the interfered subcarriers can not be restored by the accurate channel estimation, the original data on the interfered subcarriers could be partially restored by channel decoding. Soft bitsdemodulated from interfered subcarriers should be identified in channel decoder in order to reduce the weight of these soft bits. The complete process of channel estimation with interference suppression is described in Figure 2. Steps in the red box are key contents of the process.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 5 MA with Code Separation
### Sequence-Based NOMA Schemes with Reconfigured Modulation Based on Sparse Templates  
Figure 6 illustrates a block diagram of a NOMA system with sparse patterns and modified 5G modulation on the transmitting side of subscriber stations. Following sparse bit-level encoding and processing, it is mapped to information symbols using a customized technique (modified modulation). The algorithm provided in the paper [32] is then applied to the resources based on particular sparse templates, displaying the sequence of symbols obtained at the modulator's output. Thus, the method depicted in Figure 6 calls for processing on the transmitting side to make use of new types of modulation and expansion using sparse patterns to create that sparse character sequence. Huawei's planned SCMA scheme is an example of a NOMA of this kind [33]. Sparse signals are produced by adding zeros to the code sequences used to construct group signals of subscribers in the SCMA scheme using special templates. In this scenario, SCMA employs codes where the number of leading zeros is constant across all templates. For the purpose of subscribers, below is a sample sequence template with four elements:  
\[S=\begin{bmatrix}1&1&1&0&0&0\\ 1&0&0&1&1&0\\ 0&1&0&1&0&1\\ 0&0&1&0&1&1\end{bmatrix} \tag{7}\]  
In this configuration, each column of the matrix \(S\) corresponds to a different subscriber, and the number of rows is equal to the total number of elements in each sequence. The SCMA system load factor for the seventh template is \(K/N=6/4\), which equals 1.5 (load 150%). In comparison to the SCMA scheme, the improved PDMA method developed by CATT does not have a set limit on the number of elements that are either zero or non  
Figure 6: Sparse pattern and modified 5G modulation NOMA system structure.  
zero [34]. The NOMA schemes allow for the selection of sequence members from more complicated sets, such as \(\{0,1,-1,j,-j\}\). In this particular instance, the input stream consisting of M bits is shown in sequence with the assistance of a unique transformation matrix that has the dimension \(N\times 2^{\text{M}}\),
## 2 Related Work
### Related Work of iBeacon Positioning Methods
#### 2.1.2 Positioning Using Fingerprinting Method  
The fingerprinting method is another approach for radio-based positioning. This method requires the collection of signal strengths (RSSI) from each of the transmitters at every evenly spaced grid, with true location recorded to establish a signal database. With the database collected, positioning can be achieved by matching the instantaneous signals collected by a mobile device, with the database using deterministic or probabilistic methods to determine the location of the device. Generally, fingerprint-based approaches are superior in terms of accuracy to trilateration techniques [30], [PERSON], [PERSON] and [PERSON] [31] proposed a radio-based, indoor stationary positioning system, by comparing the Wi-Fi and BLE signals with the database using the k-nearest neighbour algorithm. Accuracy was improved and variance was reduced moderately when using the system. In the study conducted by [PERSON] and [PERSON] [32], location estimation algorithms were also compared, and the nearest neighbour method had a 1-meter lower average error than the k-nearest neighbour. [PERSON], [PERSON], [PERSON] and [PERSON] [33] used an iterative weighted k-nearest neighbour method to improve the traditional k-nearest neighbour method, and reduced the mean error by 1.5 m to 2.7 m in their experimental environment. Similar work [34] reported a 1.2 m accuracy using a fingerprinting indoor positioning scheme in an ideal test space of 47.4 m \(\times\) 15.9 m.
## 4 Result and Discussion
### Model Training
#### 4.1.2 Determination of Optimal WAOA-RF Models  
For multi-output RF models, node splitting takes into account the variance of the different outputs in the child nodes when constructing the decision tree to achieve a comprehensive consideration of the different outputs. Figure 8c,d illustrate the variation in fitness during iterations of the WAOA-RF model. As can be seen from the figure, the distribution of the optimal fitness of the five WAOA-RF models for the prediction of \(\theta\) ranges from \(1.817\times 10^{-3}\) and \(1.863\times 10^{-3}\), and the distribution of the prediction of \(r_{b}/r\) ranges from \(2.34\times 10^{-3}\) and \(2.47\times 10^{-3}\). Table 6 shows the performance of the five WAOA -RF models in terms of evaluation metrics on the training and test sets, as well as their ranking scores. From Table 6, it can be noticed that the model with a pop size of 80 takes the absolute lead in the performance of the training set and test set, in the prediction of \(\theta\) (training set: R\({}^{2}\) = 0.9938, MAE = 0.0083, VAF = 0.9864, RMSE = 0.0149, test set: R\({}^{2}\) = 0.9704, MAE = 0.0174, VAF = 0.9395, RMSE = 0.0311). Further analyses of Figure 9b revealed that the models with pop size 80 and 20 occupy the highest scores for predicting \(\theta\) and \(r_{b}/r\), respectively, and the model with the highest combined score is the model with pop size 80 (n_estimators = 1000, max_depth = 1000).
# Characterization of Cardiac Fat in Atrial Fibrillation Patients Prior to Ablation Treatment
## 3 Results
### Functional Parameters and Cardiac Fat  
Table 4 summarizes the anatomical and functional characteristics of the heart in our patient population in relation to BMI groups. The LA volume increased with BMI for both patient and indexed values. In the left and right ventricles, patient EDV and ESV also increased with BMI. However, when indexed to account for BSA, ventricular volumes decreased, indicating reduced heart functionality with increased body fat. Similarly, patients' LV mass significantly increased with obesity (126.2 \(\pm\) 32.6 g); however, indexed values revealed a decrease in mass in obese patients (55.1 \(\pm\) 11.8 g/m\({}^{2}\)) compared to nor  
Figure 2: Area of EAT, PAT, and total cardiac fat stratified by age groups and BMI. Cardiac fat parameters may increase with age in normal-weight patients with paroxysmal AF. (**a**,**c**) illustrates the respective fat parameter within the end-systolic phase, and (**b**,**d**,**g**) within the end-diastolic phase. (**e**) illustrates PAT area in 4-chamber view. The box plots were generated by SPSS using the areas for EAT, PAT, and total cardiac fat with respect to five age groups (\(<\)30, 30–40, 40–50, 50–60, and \(>\)60 years) and BMI. Within each age group, box plots are colored according to BMI categories. Blue brackets on top of the box plots represent differences in EAT, PAT, and total cardiac fat within BMI categories in each age group, whereas the green brackets represent differences in cardiac fat across different ages within the same BMI group. * within the box represents \(p\)\(<\) 0.05 as measured by one-way ANOVA or Mann–Whitney U test; ** indicates \(p\)\(<\) 0.001; EAT, Epicardial adipose tissue; PAT, pericardial adipose tissue; and BMI, body mass index. mal patients (56.0 \(\pm\) 9.8 g/m\({}^{2}\)). Obese patients exhibited a significantly reduced cardiac index (2.9 \(\pm\) 0.6 L/min/m\({}^{2}\)) compared to normal patients (3.2 \(\pm\) 0.6 L/min/m\({}^{2}\)), but no significant decrease in left and right ventricular ejection fraction (EF) across BMI was noted. Table 5 illustrates the association between cardiac fat and functional parameters. A positive correlation between LV mass and PAT areas was evident, with the PAT area in 4-chamber view displaying the highest association (r = 0.279, \(p\) < 0.01). Conversely, a negative correlation was observed between EAT end-systolic area and cardiac index (r = \(-\)0.220, \(p\) < 0.01), suggesting that an increase in EAT may be associated with a decreased functional capacity of the heart. Scatter plots for these correlations are shown in Figure 3.