esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 3 Interleaver
### Proposed Interleaver
#### 3.7.5 Proposed Chaotic Interleaver  
Chaotic consequences show similar behavior to random behavior but with a deterministic origin that improves both the randomness and propagation properties of the interleaver.  
The chaotic interleaver is created based on the Duffing map by applying the following algorithm:  
Step 1: generate a sequence of integers.  
\[u=\{u_{i}\}_{i=1}^{L} \tag{29}\]  
where \(i\in\mathbb{Z}\), \(L\) is the length of the interleaver, and \(u_{i}=i\), \(\forall\;i\).  
Step 2: generate a chaotic sequence of values based on a Duffing map (Equations (19) and (20)), obtaining the two sequences \(x_{n}\) and \(y_{n}\in\mathbb{R}\), and considering the initial conditions of \(a\) and \(b\) to obtain a chaotic behavior.  
Step 3: the sequence \(x_{n}\) is sorted in ascending order.  
Step 4: the change of positions derived from the ascending sorting is stored in a \(u^{\prime}\) vector that contains whole values \(i\) in the new position derived from the order.  
Step 5: apply the circular minimum spread constraint criterion and store the values that satisfy the criterion in the output vector.  
Step 6: if after reviewing the \(L\) elements given by the chaotic permutation, the output vector has not been filled, step 1 should be repeated with a new sequence of chaotic values.  
Step 7: for the output vector filled with values that satisfy the CMSC criterion, apply the circular displacement criterion.  
Step 8: the interleaver is obtained with the chaotic permutations that satisfy the two criteria.  
The pseudocode that describes the proposed interleaver employs two functions: one for obtaining the chaotic sequence (map_chaotic) and another function (int_chaotic) for linking the chaotic sequence to an integer vector, which has the permutation associated with the chaotic sequence.  
First, the algorithm assesses the CMSC criterion. When a value satisfies the vector, it is saved in the output variable. If the value does not fill the vector, a new chaotic permutation is generated based on the last chaotic terms previously generated in the sequence; therefore, each chaotic sequence is different. The algorithm ends with the application of circular movement and generates an interleaver vector (output_interleaver), as shown in Figure 3.  
\(L\) is the length of the interleaver;  
\(term\_x/term\_y\) represent the initial values;  
\(w\) is the iteration counter;  
\(n\), \(t\), \(j\) are the iteration control variable;  
\(output\) is an auxiliary variable that stores values that satisfy CMSC criteria;  
\(pi\_int\) is an auxiliary variable;  
S is the circular extension distance;  
\(equality\_flag\) is a flag indicating if there are repeated values;  
\(d\) is the circular displacement;  
\(output\_interleaver\) is a variable that stores the indices of the interleaver.  
Figure 3: Pseudocode for obtaining the interleaver.
# A Feasibility Study for a Nonlinear Guided Wave Mixing Technique
## 3 Experimental Setup
### Experimental Setup for Guided Wave Mixing Generation
To check if primary waves are generated with a determined mode and frequency, each signal needs to be analyzed individually. Pulse and echo experiments are performed at a 100 mm total distance on plate. The arrival times of primary waves \(\mathbf{u}(7.83\,\mathrm{MHz})\) and \(\mathbf{v}(2.06\,\mathrm{MHz})\) are (39.99 and 52.37) \(\mathbf{\upmu s}\), respectively. Figures 9 and 10 show the analysis results, which verify that each primary wave is generated properly. Figure 8: Experimental setup with S1, A1 modes and two directions: (**a**) Forward direction S1, A1 modes; (**b**) Inverse direction S1, A1 modes (\(a=100\) mm and \(\left\lvert b\right\rvert=20\) mm). Figure 9: Wave velocity and frequency analysis for 7.83 MHz (S2 mode). The mixing signal is represented by four FFT functions as shown in Figure 11, and the result for number 4 in Table 3 is shown by removing the sum of a and c from b. Figure 12 shows the frequency analysis for the four experimental setups and with different mode and direction. These results show that the mixing signal is only detected in the setup with A1 mode and the forward direction (Figure 12c). Figure 13a,b are the results for the first and third frequency setup of Equation (19) with A1 mode and the forward direction. In both figures, no mixing signal is detected. This means that a mixing signal is only generated at the calculated frequency ratio (Equation (18)) that maximizes the mixing amplitude. Figure 11: FFT results: (**a**) Rectangular window; (**b**) Hanning window; (**c**) Hamming window; (**d**) Blackman window. Figure 10: Wave velocity and frequency analysis for 2.06 MHz (S0 mode). Figure 12: Frequency domain analysis results: (**a**) S1 mode, forward direction; (**b**) S1 mode, inverse direction; (**c**) A1 mode, forward direction; (**d**) A1 mode, inverse direction. Figure 13: Frequency analysis results: (**a**) A1 mode, forward direction \(u(6.83\;\mathrm{MHz})+v(2.06\;\mathrm{MHz})\to v(4.77\;\mathrm{MHz})\); (**b**) A1 mode, inverse direction \(u(8.83\;\mathrm{MHz})+v(2.06\;\mathrm{MHz})\to v(6.77\;\mathrm{MHz})\).
## 3 Interference Coordination Scheme Analysis  
### CRE and ABS  
The coverage areas of PBSs are not only limited by the transmit power of PBSs, but also limited by the cross-tier interference of MBS. So, there are only a few users near the PBSs that can be served by PBSs. Moreover, Cell range expansion (CRE) is a cell access selection strategy with a positive bias value based on the traditional cell access selection strategy of maximum reference signal receiving power (RSRP). The terminals add a positive bias value to the downlink measurement of RSRP of picocells before they choose to access BS to expand the coverage of the picocells and off-load part of the macro users to the picocells. Cell access selection strategy is given as:  
\[Cell\_ID_{access}=\underset{b}{\text{argmax}}\{RSRP_{b}+bias_{b}\}, \tag{6}\]  
where  
\[\left\{\begin{array}{l}bias_{b}=0,\text{if }b\text{ is }MBS\\ bias_{b}>0,\text{if }b\text{ is }PBS\end{array}\right.. \tag{7}\]  
Due to the positive bias value of PBS, there are more macro users off-loaded to the picocells. An important benefit of CRE is that the spectral resources can be more uniformly distributed to the network users because of the similar number of users and the same spectral resources of BSs. The users who access the PBS through CRE will suffer severe cross-tier interference from MBS; the almost blank subframe (ABS) of the time-domain inter-cell interference coordination (ICIC) needs to be applied in HetNets. ABS is a subframe where MBS only transmits cell common reference signals (CRS) and other necessary signals. Therefore, the users will experience close to zero cross-tier interference from MBS in subframes where MBS uses ABS. The basic principle of ABS is illustrated in Figure 2.  
In the networks, there is only one type of user of MBS which is called Macro_UE and is scheduled during non-ABS. Moreover, PBS has two types of users named Pico_UE and CRE_UE, respectively. The Pico_UEs are scheduled during non-ABS while the CRE_UEs are scheduled during ABS. This means that the CRE_UEs of picocells are protected from cross-tier interference from MBS.  
The CRE scheme off-loads some macro users to the picocells to achieve load balancing while the ABS scheme alleviates the severe cross-tier interference from MBS that will effectively improve the performance of the networks.
Sensors_**2014**, _14_, 15415-15433; doi:10.3390/s140815415  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Kalman Filter-Based Short Baseline RTK Algorithm for Single-Frequency Combination of GPS and BDS**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Department of Electronic Engineering, Tsinghua University, No.1 Qinghuayuan, Haidian District,  
Beijing 100084, China; E-Mails: [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (F.G.); [EMAIL_ADDRESS] (M.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-138-1198-7424.  
_Received: 25 June 2014; in revised form: 11 August 2014 / Accepted: 13 August 2014 / Published: 20 August 2014_  
**Abstract:** The emerging Global Navigation Satellite Systems (GNSS) including the BeiDou Navigation Satellite System (BDS) offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK) algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.  
**Keywords:** real time kinematic (RTK) algorithm; Global Positioning System (GPS); BeiDou navigation satellite system (BDS); Kalman filter; ambiguity resolution
# A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
## 2 Related Work
### The Difference Between RANS and LES  
LES (Large Eddy Simulation) is a method based on direct simulation of large-scale turbulent structures. In LES, large-scale turbulence structures are directly numerically simulated, while small-scale turbulence structures are approximated through models. The computational cost of LES is relatively high, especially in complex situations such as near-wall flow, but it can provide more accurate turbulence simulation results and is suitable for problems that require consideration of large-scale turbulent structures. RANS (Reynolds-averaged Navier-Stokes) is a method based on the time averaging of NS equations. RANS converts unsteady turbulence problems into steady problems for study and eliminates unsteadiness by averaging the turbulence variables over time. RANS has a relatively low computational cost and is suitable for large-scale engineering calculations and situations where faster results are required. However, the RANS method is less effective at capturing large-scale structures in turbulence and lacks the resolution of the coherent scales of fluid structures compared to LES. To sum up, LES is suitable for situations where more accurate simulation of large-scale turbulent structures is required, while RANS is suitable for large-scale engineering calculations and situations where the large-scale structure of turbulent flow is not demanding. The choice of which method to use depends on the requirements of the specific problem and the availability of computing resources.
# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
## 4 Experiments
### Datasets and Experimental Setting  
The experiments in this paper were conducted on the NYT [33] and WebNLG [34] datasets. In the NYT dataset, the training set consists of 56,195 sentences, the valid set consists of 5000 sentences, and the test set consists of 5000 sentences. There are a total of 24 relation types in this dataset. In the WebNLG dataset, the training set consists of 5019 sentences, the valid set consists of 500 sentences, and the test dataset consists of 703 sentences, for a total of 247 types of relationships between the dataset. To evaluate the efficiency of the ESGM model in extracting nested entities, the dataset was preprocessed to create a new dataset called \"new_data\". The new_data training set consisted of 5589 sentences, the valid set consisted of 1226 sentences, and the test set consisted of 627 sentences; there were a total of 360 relation types in this dataset. The statistics of the datasets are summarized in Table 1.  
In this experiment, we used Python 3.7 and related scientific computing libraries to write and execute the code of the experiment, using Pytorch v1.12 as the deep learning framework. Specifically, we used the Pytorch v1.12 GPU version to speed up the training process. All experiments were conducted on a workstation running an Ubuntu 18.04 Linux operating system. The model training in the experiment relied on an NVIDIA GeForce RTX 3090 GPU. We configured Pytorch to take full advantage of GPU acceleration using CUDA 11.3. The model was trained using mini-batches with a batch size of 3. The learning rate was set to 1 \(\times\) 10\({}^{-5}\). The maximum sentence length was set to 300. Dropout with a rate of 0.2 was applied during the subject extraction and object extraction stages. The model was trained on the BERT base case pretrained language model, which consists of twelve Transformer blocks with a hidden size of 768 and a total of 110M parameters. For the NYT dataset, the maximum number of epochs was set to 100. For the WebNLG dataset, we set the maximum epoch number to 200. To prevent overfitting, the training process was automatically stopped if the training results showed no improvement over ten consecutive  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets/Category** & **Train** & **Valid** & **Test** \\ \hline new\_data & 5589 & 1226 & 627 \\ NYT & 56,195 & 5000 & 5000 \\ WebNLG & 5019 & 500 & 703 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of datasets.  
epochs or the performance on the valid set worsened while the performance on the training set improved.  
In order to select the most appropriate span length limit, in this experiment we used span lengths of 5, 10, 15, and 20. The performance of the acquired loss was evaluated visually, as shown in Figure 3. From the figure, it is evident that the loss function converges more easily when the span length is configured as 15.
## 3 Research Methodology
### Adaptive Feature Extraction  
The learning in neural network models is significantly affected by both the initial setting values of the hyperparameters and the lack of corresponding changes in these values with increased training times. The adaptive learning method involves analyzing the dataset output results using a neural network model throughout the training process [26]. This leads to automatic adjustment of the hyperparameters, learning rates, and sometimes even the parameters of the neural network model, based on the analysis. The adaptive learning approach in this study adapts the APFL method [27]. Each client's feature extraction layer is obtained through a blend of global and client model parameters from the previous round, with the blending ratio established via adaptation. \(F_{i}^{w}\) denotes the parameter of the feature extraction layer, and \(G_{i}^{w}\) denotes the parameter of the category prediction layer, where i \(\in\{1,2...n\}\) represents the i-th client, * represents the best solution of the model, and  
Figure 1: Personalized client neural network model.  
federated learning model training is \(O(w\times e\times n\times m)\), where w is the client number, \(e\) is the number of model iterations, \(n\) is the number of datasets, and \(m\) is the number of parameters [28].  
The following algorithm is based on the division and separate training of each client model, and it then incorporates the adaptive mixing method of feature extraction layer parameters.  
```
let \(W_{f}\) be the parameters of feature extraction layers. let \(W_{g}\) be the parameters of the category predictions layer. let \(i\) be the \(i_{th}\) global round. let \(c\) be the \(c_{th}\) selected client. let \(\gamma\) be the learning rate. let \(\beta\) be the mixing ration, and the initial value is 0.5. for\(i=1,2,\ldots\),N do  set \(\beta\) to initial value if\(i=\) N then all clients do  new \(\beta=\)betaupdate (\(\beta\))  client own model \(\leftarrow\) (\((1-\beta)*\ W_{f}^{i-1}+\beta*W_{f}^{i-1,c}\)) \(\circ\)\(W_{g}^{i-1,c}\))  break else each selected clients \(C\in\{C_{1},C_{2}...C_{m}\}\) parallel do  receive \(W_{f}^{i-1}\) from server.  new \(\beta=\)betaupdate (\(\beta\))  (\(W_{f}^{i,c}\)\(\circ\)\(W_{g}^{i,c}\)) \(\leftarrow\)modelupdate ((\((1-\beta)*\ W_{f}^{i-1}+\beta*W_{f}^{i-1,c}\)) \(\circ\)\(W_{g}^{i-1,c}\), \(\gamma\))  keep the \(W_{g}^{i,c}\)  send \(W_{f}^{i,c}\) back to server  finish the federated learning
```  
**Algorithm 1**
## 4 Analytical Methods
### XGBoost  
XGBoost [24] is an ensemble algorithm that combines multiple decision trees and is a boosting-based model that improves the overfitting problems, speed, and stability of existing tree-based models. XGBoost sequentially trains a decision tree on the training data, and the objective function of XGBoost is defined as follows.  
\[Obj(\theta)=\sum_{i=1}^{m}l\left(y_{i},\hat{y}_{i}^{(t)}\right)+\sum_{k=1}^{K }\Omega(f_{k}),\ \ \ \theta=(f_{1},f_{2},\ldots,f_{K}) \tag{1}\]  
Here, \(i\) represents the \(i\)th sample in the dataset and \(m\) represents the total number of dataset inserted into the \(k\)th tree while \(K\) is the total number of trees. \(y_{i}\) is the class label, while \(\hat{y}_{i}\) is the predicted label. \(l\) is the loss function and \(\Omega\) is the regularization term.  
XGBoost adopts an additive strategy to improve the value of the objective function by adding a new decision tree to the previous one at each iteration. When the \(t\)-tree is constructed, the predicted value \(\hat{y}_{i}^{(t)}\) can be formulated as follows.  
\[\hat{y}_{i}^{(t)}=\sum_{k=1}^{t-1}f_{k}(x_{i})+f_{t}(x_{i})=\hat{y}_{i}^{(t-1) }+f_{t}(x_{i}) \tag{2}\]According to Equations (1) and (2), the objective function can be formulated as follows.  
\[Obj(\theta)^{t}=\sum_{i=1}^{m}l\left(y_{i},y_{i}^{(t-1)}+f_{t}(x_{i})\right)+ \sum_{k=1}^{t}\Omega(f_{k}) \tag{3}\]  
If the tree contains a total of \(T\) leaf nodes, the index of each leaf node is defined as \(j\) and the weight of the samples for each leaf node is \(w_{j}\). Then, the regularization term \(\Omega(f)\) is defined as follows.  
\[\Omega(f)=\gamma T+\frac{1}{2}\lambda\sum_{j=1}^{T}w_{j}^{2} \tag{4}\]  
Here, \(\gamma\) and \(\lambda\) represent penalty factors.
## 4 Results and Discussion
### Mechanical Performance  
#### 4.4.1 Tensile Test Data  
Tensile tests were conducted on ASTM D638 Type IV specimens. Table 4 details the average ultimate tensile strength (UTS), elongation at break, and Young's modulus. Additionally, Figure 3 presents representative stress-strain curves for each material, providing insights into their respective failure modes and mechanical behavior under tension.  
SLA exhibited the highest tensile strength (68.9 MPa), outperforming both PLA and ABS. However, FDM ABS demonstrated superior ductility, with an elongation at break of 8.7%, compared to SLA's 4.4%. PLA's intermediate elongation and higher stiffness (3.4 GPa) position it between ABS and SLA on the stiffness-ductility spectrum. These findings suggest that SLA is better suited for load-bearing applications, while FDM ABS is preferable for parts requiring impact resistance or flexibility.  
The stress-strain curves in Figure 3 further illustrate the differences in mechanical behavior. The SLA resin shows a steep elastic region and a sudden fracture, indicative of brittle failure. FDM ABS, in contrast, exhibits a prolonged plastic deformation phase before failure, demonstrating its superior toughness. PLA, while stronger than ABS, has a lower elongation at break and moderate plasticity before failure.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Material** & **UTS (MPa)** & **Elongation at Break (\%)** & **Young’s Modulus (GPa)** \\ \hline FDM (PLA) & \(50.2\pm 2.7\) & \(5.8\pm 0.6\) & \(3.4\pm 0.2\) \\ FDM (ABS) & \(47.5\pm 3.1\) & \(8.7\pm 0.8\) & \(2.3\pm 0.2\) \\ SLA (Resin) & \(68.9\pm 3.0\) & \(4.4\pm 0.3\) & \(2.8\pm 0.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tensile properties (\(n=5\)) for FDM (PLA, ABS) and SLA resin.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Material** & **X-Axis (mm)** & **Y-Axis (mm)** & **Z-Axis (mm)** \\ \hline FDM (PLA) & \(+0.16\pm 0.04\) & \(+0.15\pm 0.05\) & \(+0.19\pm 0.07\) \\ FDM (ABS) & \(+0.17\pm 0.05\) & \(+0.14\pm 0.05\) & \(+0.20\pm 0.06\) \\ SLA (Resin) & \(+0.05\pm 0.02\) & \(+0.05\pm 0.02\) & \(+0.06\pm 0.02\) \\ \hline \end{tabular}
\end{table}
Table 3: Dimensional deviations (\(\Delta D\)) of 50 mm cubes for FDM and SLA (\(n=5\)).  
These stress-strain curves reinforce the previous numerical findings, confirming that SLA resin offers the highest tensile strength but fractures without significant plastic deformation, limiting its ability to withstand impact forces. In contrast, FDM ABS, despite having a lower UTS, allows for substantial elongation, making it preferable for components subjected to dynamic or shock loads. PLA remains an intermediate option, combining moderate strength and ductility. This analysis suggests that material selection should be guided not only by ultimate strength but also by expected loading conditions and failure tolerance.
# Amaranth Seed Polyphenol, Fatty Acid and Amino Acid Profile
## 3 Results and Discussion
### Amino Acid Profile  
Amaranth is a protein-rich pseudo-cereal (\(\sim\)14.45%), and the sum of the essential amino acids in the seeds is approximately equal to the recommended FAO and WHO standard [35]. Table 5 presents the composition of amino acids in amaranth seed. There are nine essential amino acids, namely leucine, isoleucine, valine, phenylalanine, threonine, tryptophan, methionine, histidine and lysine [36]. Figure 4 presents a typical chromatogram for the amino acid profile of amaranth seed. As can be observed, all 9 essential amino acids known were reported: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan and valine in a total concentration of 29.67 g/100 g protein, with the major essential amino acid being lysine, followed by leucine and histidine. According to the FAO and WHO standard, the tryptophan concentration is 1.0 g/100 g protein, methionine's is 3.5 g/100 g protein, threonine's is 4.0 g/100 g protein, isoleucine's is 4.0 g/100 g, valine's is 5.0/100 g protein, lysine's is 5.5 g/100 g protein, leucine's is 7.0 g/100 g protein and cysteine's is 3.5 g/100 g protein. In our study, there were some negative differences with the FAO and WHO standard in terms of threonine, isoleucine and valine and positive differences in terms of lysine, leucine, cysteine, methionine and tryptophan. Our results are in agreement with those reported in the literature [37; 38]. The essential amino acids play an important role in the human body, promoting protein synthesis, influencing the human metabolism, regulating multiple biological processes and influencing body weight and energy balance [36; 39; 40].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Fatty Acid** & **mg/100 g Amaranth** \\ \hline Dodecanoic acid C12:0 & 0.30 \\ Myristoleic acid C14:0 & 0.52 \\ _cis_-9-Octadecenoic acid C18:1 & 0.74 \\ Pentadecanoic acid C15:0 & 0.08 \\ Palmitic acid C16:0 & 7.38 \\ _cis_-9-Palmitoleic acid C16:1 & 0.63 \\ Heptadecanoic Acid C17:0 & 0.16 \\ _cis_-10-Heptadecenoic acid C17:1 & 0.06 \\ Oleic acid C18:1 & 23.78 \\ _trans_,_trans_-9,12-linoleic acid (C18:2) & 3.18 \\ _cis_,_cis_-9,12-linoleic acid (C18:2) & 13.53 \\ Arachidic acid (C20:0) & 0.30 \\ Gamma linolenic acid (C18:3) & 3.05 \\ _cis_-11-Eicosenoic acid (C20:1) & 0.36 \\ Linolenic acid (C18:3) & 0.71 \\ Heneicosanoic acid (C21:0) & 0.08 \\ Eicosadienoic acid (C20:2, _cis_-11,14) & 0.16 \\ Behenic acid (C22:0) & 0.34 \\ Eicosadetranonic acid (20:3) & 0.05 \\ _cis_-5,8,11,14-arachidic acid (C20:4) & 0.19 \\ Tricosanoic acid (C23:0) & 0.06 \\ Nervonic acid (C24:1) & 0.08 \\ TOTAL & 55.71 \\ Saturated fatty acids (\%) & 16.54 \\ Unsaturated fatty acids (\%) & 83.45 \\ Poly-unsaturated fatty acids & 37.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fatty acid composition of amaranth seed.