esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] et al. developed a 1 DCNN-Informer model where 1 DCNN extracts local features from shield parameters and Informer captures long-term dependencies. Enhanced by DANN transfer learning, it improves cross-strata adaptability for position/attitude forecasting. The combined structure increases model complexity and resource requirements [31]. [PERSON] et al. developed the TBMformer model by modifying Informer's architecture to incorporate spatio-temporal feature fusion (time/ring encoding) and multi-attention mechanisms (LSTM and temporal/self-attention). This adaptation enhances real-time prediction of tunnel boring machine parameters under complex geological conditions while retaining Informer's efficient sequence forecasting capability [32]. [PERSON] et al. enhanced Informer with T-BiGRU (local feature extraction) and Masked MSA (global dependency capture) for railway settlement prediction. While improving long-sequence accuracy, limitations include unverified generalization to diverse geological conditions, unquantified computational complexity, and a lack of real-time deployment validation in IoT systems [23]. In the Informer algorithm, self-attention distillation is utilized to introduce convolutional and max-pooling layers between self-attention blocks to shorten input lengths and optimize performance. However, when applied to surface settlement prediction induced by shield tunneling, the Informer algorithm faces three key limitations: (1) As the network depth increases, the amount of historical information retrievable by standard convolutional layers grows only linearly, limiting the algorithm's perception range and leading to repetitive and uninformative computations. (2) Standard convolutional layers do not consider the temporal dimension, which can cause leakage of information about future events during prediction. (3) There is room for improvement in reducing computational complexity and memory consumption. Moreover, surface settlement induced by shield tunneling exhibits lag and abruptness. The maximum settlement location typically lags the shield face by 5-10 rings, and unexpected events such as machine stoppages or secondary grouting can significantly impact surface settlement. These factors are not adequately considered in the Informer algorithm. To overcome these limitations, we replaced the original standard convolutional layers with dilated causal convolutional layers and employed soil layer classification and characterization to reduce computational complexity and memory consumption. Considering the characteristics of shield tunneling, we increased the prediction accuracy through measures such as moving prediction windows and incorporating special factors. Comparative analyses with the traditional Informer algorithm, RF algorithm, and LSTM algorithm validated the proposed approach. Furthermore, the improved Informer algorithm was integrated with the Shapley additive explanations (SHAP) method to analyze the effects of key shield tunneling parameters on surface settlement. Finally, the NSGA-III multi-objective optimization algorithm was employed to optimize the shield tunneling parameters.
###### Abstract
In order to improve the concealment and effectiveness of the constructed bionic communication signal, a BC-UAC method based on the TFC of the bottlenose dolphin whistle is proposed. First, the TFC of the bottlenose dolphin whistle is extracted by the short-time Fourier transform (STFT) algorithm and smoothed by the Sage-Husa adaptive Kalman filter (SHAKF) algorithm. Second, the transmitted information bits are modulated to the TFC, where the TFC is processed as segments to improve the communication rate. Two BC-UAC schemes based on the segmented TFC of the bottlenose dolphin whistle, namely the BC-UAC scheme using the whistle signal with time-delay (BC-UAC-TD) and the BC-UAC scheme using the whistle signal with frequency-shift (BC-UAC-FS), are presented. In the proposed BC-UAC schemes, the information bits are represented by time-delay and frequency-shift to obtain the bionic modulated signal after modulation. Furthermore, an improved orthogonal matching pursuit (IOMP) algorithm is addressed to estimate the channel with unknown sparsity, and virtual time reversal mirror (VTRM) technology is used to perform channel equalization. The performance of the proposed BC-UAC method is evaluated with simulated and measured underwater channels, respectively. Numerical results show that the constructed bionic communication signals have good concealment. The two BC-UAC schemes have good performance in terms of covertness and reliability. In addition, the covertness of BC-UAC-TD is better than that of BC-UAC-FS, although the reliability of BC-UAC-TD is worse than that of BC-UAC-FS. The remainder of this paper is organized as follows. In Section 2, we analyze the TFC characteristics of the bottlenose dolphin whistle. Two BC-UAC schemes based on the segmented TFC of the bottlenose dolphin whistle are presented in Section 3. In Section 4, the performance of the proposed BC-UAC method is evaluated with simulated and measured underwater channels. Finally, the conclusion of the paper is given in Section 5.
## 5 Conclusions  
The sensitivity of the magnetorheological removal function with the normal positioning error is investigated. Treating the normal polishing force as the monitoring target, a constant force polishing magnetorheological end-effector is designed, which has a fast response speed, high force control accuracy, compact structure, and lightweight. The impedance-based controller fulfills the force control stability and tracking precision. The adaptability of the magnetorheological end-effector is enhanced by the autonomous control system, and the robotic MRF method based on constant force control is applicable to any robots with different positioning precision levels.  
The experimental results reveal that the normal force of magnetorheological polishing accurately defines the stability of removal function, and the distribution of material removal amount for the uniform polishing achieves high consistency under constant force control. The average error of constant force control reaches within 0.1N. A deterministic polishing based on constant force control is also conducted, and the results reveal that magnetorheological finishing with constant force has a strong surface convergence effect, which can satisfy the demands of nano-level surface fabrication for optical lenses.  
Conceptualization, C.Z.; writing-original draft preparation, L.Z.; writing-review and editing, L.Z., C.Z. and W.F. All authors have read and agreed to the published version of manuscript.  
The research is supported by National Natural Science Foundation of China (61605182), Innovation Development Foundation of China Academy of Engineering Physics (K1267-2022-TCF).  
Figure 14: The error map before and after deterministic polishing with the force control: (**a**) Initial surface error map; (**b**) The residual surface error map.
## 4 The Geometries of the Proposed Transmitters
### \(5\times 5\) Square-Shaped Array  
As depicted in Figure 12, a square LED array of size \(5\times 5\) with a spacing of \(s\) is proposed, where \(s\) is set to 1 cm. Each LED element, denoted as \(e_{i,j}\), has a field of view of \(\phi_{\frac{1}{2}}^{\prime}\) and is aligned along an orientation vector \(\mathbf{O}_{i,j}\), with each vector intersecting the \(y=d_{0}\) plane at a point \(e_{i,j}^{\prime}\). Similar to the other configurations, the \(y=d_{0}\) plane is defined such that the two square arrays formed by the \(e_{i,j}^{\prime}\) points from the two transmitters become tangent on this plane (illustrated in Figure 13). To meet this condition, the separation between the \(e_{i,j}^{\prime}\) points should be \(D/4\). The naming convention for the array elements is illustrated in the matrix below.  
\[\begin{bmatrix}e_{2,2}&e_{2,1}&e_{2,0}&e_{2,-1}&e_{2,-2}\\ e_{1,2}&e_{1,1}&e_{1,0}&e_{1,-1}&e_{1,-2}\\ e_{0,2}&e_{0,1}&e_{0,0}&e_{0,-1}&e_{0,-2}\\ e_{-1,2}&e_{-1,1}&e_{-1,0}&e_{-1,-1}&e_{-1,-2}\\ e_{-2,2}&e_{-2,1}  
The location of the element \(e_{i,j}\) relative to the center element \(e_{0,0}\) is given by \(\mathrm{P}(sj,0,si)\). To achieve the geometry illustrated in Figure 12, the orientation vector \(\mathbf{O}_{i,j}\) with starting point \(\mathrm{P}(sj,0,si)\) (location of \(e_{i,j}\)) should intersect the \(y=d_{0}\) plane at \(\mathrm{P}(\frac{D}{4}j,d_{0},\frac{D}{4}i)\) (location of \(e^{\prime}_{i,j}\)). Thus, the required vector can be found using  
\[\mathbf{O}_{i,j}=\langle(\frac{D}{4}-s)j,d_{0},(\frac{D}{4}-s)i\rangle \tag{34}\]  
The power of each LED in the square array transmitter is determined to be \(P_{0}/25\). Since the intensity is proportional to the power level divided by the squared tangent of the half FOV, the intensity \(I_{4}\) of each LED of the square array is set as follows:  
\[I_{4}=\frac{1}{25}\frac{\tan^{2}\phi_{\frac{1}{2}}}{\tan^{2}\phi_{\frac{1}{2}} ^{\prime}}I_{0} \tag{35}\]  
The initial values of the design parameters for the two transmitters are listed in Table 2. Using Equations (2) and (3), and the values given in Table 2, the irradiance distribution in the plane \(y=1\) m is plotted using MATLAB. The result is given in Figure 6e.  
Figure 12: Square-shaped LED array geometry.  
Figure 13: \(5\times 5\) square arrays formed by the \(e^{\prime}_{i,j}\) points on the \(y=d_{0}\) plane.
## Abbreviations  
The following abbreviations are used in this manuscript:  
ASCs Sustainable Agriculture Supply Chains  
CC Cloud Computing  
IoT Internet of Things  
AI Artificial intelligence  
ML Machine learning  
LSTM Long Short-Term Memory  
XGBoost Extreme Gradient Boosting  
SVM Support Vector Machine  
LR Linear Regression  
RNN Recurrent Neural Network  
ANN Artificial Neural Networks  
R2 Coefficient of determination  
MSE Mean Square Error  
RMSE Root Mean Square Error  
MAE Mean Absolute Error  
MAPE Mean Absolute Percentage Error  
TDS Team Data Science Process  
PLS Partial Least Squares  
SLR Single Linear Regression  
MLR Multiple Linear Regression  
OLS Ordinary Least Squares  
SVR Support Vector Regression  
RBF Radial Basis Function
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Print Quality Problems
#### 3.3.15 Vibration and Ringing  
Visual waves or rippling effect can sometimes be seen on the print surface, also known as echoes in print is usually caused by vibration due to an unsteady 3D printer, worn linear bearings, loose print heads, unsmooth axis movement, and/or print speed set too high. Solutions include the following:  
1. Placing the printer on a sturdy surface to reduce vibration.
2. Make sure all bearings are running smoothly, all the bolts and joints are secured and correctly tightened.
3. Check that all axes or rods are clear of dust and lubricated with oil for smooth movement (refer to 3.2.7: Shifted layers or leaning print).
4. Reduce the printing speed or increase the flow and extrusion temperature.
# Design and Performance Evaluation of eLoran Monitoring System
## 2 Principle and Structure
### Data Processing Center  
The BPL monitoring data processing center realizes a comprehensive processing and analysis and evaluation of monitoring data gathered by each monitoring station, evaluates and assesses the performance of the BPL time service system and the integrity and availability of current BPL signals, and realizes the functions of an abnormal alarm for time difference, abnormal alarm for time code, and abnormal alarm for power.
# A Two-Phase Evolutionary Method to Train RBF Networks
## 2 Method Description
### Fitness Evaluation  
In this step, a valid RBF network \(y(x)=\sum_{i=1}^{k}w_{i}\phi(\|x-c_{i}\|)\), is created using the chromosome \(g\) and is subsequently trained using the typical training procedure for RBF networks. The main steps to calculate the fitness \(f_{g}\) of a chromosome \(g\) are the following:  
1. **Decode** the chromosome \(g\) to the parts (centers and variances) of the RBF network as defined by the layout of Table 1. 2. **Calculate** the output vectors \(w_{1},w_{w},\ldots,w_{k}\) by solving the induced system of equations: 1. **Set** \(W=w_{kj}\) the matrix of \(k\) weights, \(\Phi=\phi_{j}(x_{i})\) and \(T=\{t_{i}\}\). 2. **Solve:** \[\Phi^{T}\Big{(}T-\Phi W^{T}\Big{)}=0\] (6) \[W^{T}=\Big{(}\Phi^{T}\Phi\Big{)}^{-1}\Phi^{T}T=\Phi^{\dagger}T\] (7) The matrix \(\Phi^{\dagger}=\big{(}\Phi^{T}\Phi\big{)}^{-1}\Phi^{T}\) is the pseudo-inverse of \(\Phi\), with \[\Phi^{\dagger}\Phi=I\] (8)
3. **Set** \(f_{g}=\)\(\sum_{i=}^{m}(y(x_{i})-t_{i})^{2}\)
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
###### Abstract  
The technical feasibility of a heavy haul freight using WDPS (Wireless Distributed Power System) was verified for the first time in Korea. That consisted of a master locomotive, 50 freights and a slave locomotive. The master and slave locomotives were equipped with WDPS to distribute traction and braking control command data. MIMO-OFDM, which is strong in multi-path, was applied to the WDPS, and a repeater was installed on the 25 th freight to secure coverage even with signal attenuation according to the terrain (curve, gradient) of the driving section. The test was carried out at an average speed of 100 km/h in the approximately 21 km from Jillye station to Busansinhang station in Korea. As a result of the test, it was confirmed that the traction function, braking function, pantograph control function, MCB function, and safety circuit function of the heavy haul freight using WDPS were perfectly controlled. Additionally, the air braking time was reduced by about 50% compared to the centralized control as the braking was controlled by two locomotives at the same time. For the first time, the operational feasibility of heavy haul freight using WDPS based on MIMO-OFDM in the 2.4 GHz/5 GHz band with a relatively short propagation distance was verified.  
heavy haul freight; WDPS; MIMO-OFDM; braking performance; safety function Article
###### Abstract
The effective modelling of such a test would therefore open doors to greater innovations, as different door designs incorporating new materials could be preliminary tested by numerical methods, resulting in significant savings in time and costs. A number of papers in the literature have indeed addressed the issue of modelling the effects of the FTP test on fire-doors, using the FEM technique [2; 9; 10]. These works aimed at deriving a simplified model for the description of the door behavior under a fire-test, despite the occurrence of complex phenomena [11]. Unfortunately, a perusal of the adopted experimental and calculation procedures reveals a lack of details in the assessment of the validity of the results, and the calculations appear strictly related to the specific door analyzed, precluding their general use and reproducibility of calculations, which has become a major issue [12]. Moreover, the choice of the employed physical parameters affects the results of the fitting [13]. In a preliminary study [14], the issue of modelling the FTP test has been addressed, aimed at introducing innovation in this field. Though thermal modelling, this preliminary work was still linked to the specific door investigated and the preliminary results highlighted the critical importance of thermal bridges originating at the door edges in determining the fire-resistance and thus allowed us to propose a design for a novel thinner door. Since a general, self-consistent, fully detailed, validated and ready-to-use FEM methodology for fire-door designs cannot be derived from the previous works, thus precluding the forecasting capability, which is the strength of computational methods, this work attempts to reach this challenge. Finally, the assessed FEM model developed is used to simulate the fire-test on the fire-door with the foam as an insulator. The results clearly show the potential of the foam as a substitute for the rock wool. This finding demonstrates that, with an appropriate door design, materials with poorer thermal insulating capability compared to the traditional insulator can also be used, allowing us to optimize other characteristics as, for example, soundproofing.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 5 Dimensionality Reduction Techniques
### Independent Component Analysis (ICA)
#### 5.3.1 Centering  
Centering is regarded as the most basic and essential preprocessing step for implementing ICA [74]. In this step, the mean value is subtracted from each data point to transform the data into a zero-mean variable and simplify the implementation of the ICA algorithm. Later, the mean can be estimated and added to the independent components.
## 6 Experimentation Results and Discussion
### Experimentation on NASA Defect Datasets  
This section presents the results obtained after conducting experiments on the NASA jm1 and KC1 defect datasets collected from the PROMISE online public data repository. There are 22 features in the dataset, of which 21 are used to represent feature vectors and 22 are used as class variables. A window size of 500 samples for jm1 and 100 samples for the KC1 dataset was used for the experiment.  
The AUC-ROC curves, as shown in Figure 2a,b, showed that the ROC-AUC scores of PL-NB and PL-RF are better than the ROC-AUC scores of base NB and base RF (Refer Table 6), respectively. That is to say, the application of PL for CD adaptation in SDP showed significant improvement in the performance of the prediction model.
## 3 Results and Discussions
### Optimal Level of DoE Factors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Desired Graphene Traits**} & **Respective** & \multicolumn{3}{c}{**Factors**} \\ \cline{3-5}  & **Responses I \& II** & **A Annealing** & **B a-C Layer** & **B a-C Layer** & **C Annealing Time** \\ \cline{3-5}  & & **Temperature** & **Thickness** & & \\ \hline Low defects only & Low Response I & Low for high B; & Low & Large \\ \hline Mono-/few-layer graphene only & High Response II & High & Low & Small \\ \hline Mono-/few-layer graphene with low defects & Low Response I and high Response II & High & Low &
\begin{tabular}{c} Need to be optimized \\ using response surface \\ method [37; 45] \\ \end{tabular} \\ \hline Multi-/many-layer graphene only & Low Response II & Large region in Figure 8, representing several combinations \\ \hline Multi-/many-layer graphene with low defects & Low Response I and low Response II & High & Low & Large \\ \cline{3-5}  & & & As indicated by Figure 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relation between the choice of levels and the quality/number of graphene layers. Figure 9: Superimposition of contour plots for Responses I and II, as shown in Figures 7 and 8, respectively; dotted ovals show the region of coincidence. However, the case for multi-layer graphene with low defects is not straight-forward. The region for multi-layer graphene represented in Figure 8 is large, but the region for low defects in Figure 7 is smaller. In order to have a clearer picture, Figures 7 and 8 are superimposed, as shown in Figure 9.  
The coinciding regions (depicted by dotted ovals) in Figure 9 indicate that a high level of the factors \"A\" and \"C\" and a low level of the factor \"B\" are the required levels for many-layer graphene with low defects. These regions suggest that a treatment combination of higher annealing temperature, thinner a-C layer and longer annealing time will result in a low \(I_{\rm 2D}/I_{\rm G}\) peak intensity ratio (close to one or less) and a low \(I_{\rm D}/I_{\rm G}\) peak intensity ratio; hence, multi-layer graphene [40; 41] with less defects will be synthesized.
## 4 Model of the Composite Deck  
Various modeling approaches for composite beams with deformable connections have been proposed in the literature that consider different levels of detail in terms of materials, elements, and stud behavior. Two general categories can be defined:  
* A detailed model using 3D solid elements for concrete and studs can be used to study the local behavior of the connection and the failure mode through the stresses and strains in the materials [17; 26; 27].
* A global model that introduces the connection between steel and concrete parts by nonlinear links with an adequate relationship between displacements (slip and lift) and forces (normal and shear) [25; 28; 29] can be used to model the steel profile and concrete slab and can be implemented with various levels of detail (frame, shell, and solid elements).  
The first approach is particularly important for studying the local behavior of studs, developing the constitutive relationship of the connection, and comparing numerical results with experimental results for push-out tests. However, this approach is not suitable for the global analysis of the entire structure, especially when the behavior of the connection has already been defined by tests or code provisions.  
The second type of model represents a good compromise between understanding the role of the connection in the global behavior of the structure and maintaining an acceptable level of computational effort.  
Therefore, in this work, the second type of global model was adopted since the aim is to better understand the effect of the main parameters of the connection on the deformability and resistance of the composite beams. A simply supported steel-concrete composite deck of a bridge, with a length ranging from 25 to 50 m, was considered. Skewness was taken into account, with angles ranging from 0 to 60 degrees, as in the literature [5; 6] and typical applications. The structure is composed of a solid concrete slab and three I-shaped beams connected by headed studs.  
The finite element (FE) model was developed using four node shell elements, providing six degrees of freedom for both the steel profiles and the concrete slab. Nonlinear links were introduced to connect these components, considering the load-slip relationships of the connectors.
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
## 4 Design and Experiment
### Experiment for a Single String 6-LED  
The experiment to evaluate the characteristics of a LCR to drive a single string 6-LED and their relative parameters during \(v_{GS}\) changes, is shown in Figure 9. The synthesis voltage \(VV_{dc}\) composed of \(v_{DN}=Nv_{D}\), \(V_{R}\), and \(v_{DS}\), under the \(v_{GS}\) control, is displayed in Figure 9a, in which all of the parameters measured meet the estimations with an acceptably low dropout voltage. The experiment shows that \(v_{DS}\) varies at around 1.5 V, from 1.77 V to 1.38 V, in all five MOSFETs; correspondingly, their \(v_{GS}\) varies from 3.72 V to 4.46 V.  
Figure 9: Characteristics of the master LCR loaded with a single string 6-LED in dimming process by \(v_{GS}\) controlled: **(a)** The synthesized \(VV_{dc}\) composed of a specified \(V_{DS1}\equiv V_{Q}\cong 1.5\) V, \(v_{R}\), and \(v_{DS}=5v_{D}\); **(b)** LED current \(i_{D}\) versus \(v_{GS}\); **(c)** LED luminance with respect to conduction loss of MOSFET during dimming.  
The LED luminance, with respect to the LED current \(i_{D}\) under the \(v_{GS}\) control, is shown in Figure 9b, in which each string 6-LED can emit a luminance from 0 to 62.1 kcd/m\({}^{2}\); correspondingly, the string LED current \(i_{D}\) varies from 0 to 357 mA. The string LED luminance is measured vertically, facing the LED display at a distance of 50 cm, by using a luminance-meter type BM-910 TOPCON with a high accuracy. The string LED luminance and the conduction loss in MOSFET are measured in Figure 9c. It is clearly seen that the maximum conduction loss in the MOSFET is only 492 mW at \(i_{DS}\) = 357 mA, due to a low dropout of \(v_{DS}\approx\) 1.38 V, which actually benefits the MOSFET, dissipating the low conduction loss.