esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Three-View Imaging Optical Coherence Tomography for Intraoperative Vascular Evaluation
## 4 Discussion  
We have demonstrated a novel cooperative three-view imaging SDOCT system with a 3 mm \(\times\) 3 mm rectangle scanning FOV. Both the phantom tube and in vivo rat femoral artery imaging clearly demonstrate the effectiveness of cooperative three-view imaging with a larger synthetic FOV in resolving tubular structures. We believe the intraoperative application of this cooperative three-view imaging system will greatly benefit the objective evaluation of vascular anastomosis procedures. As for the future work, we will develop a handheld probe based on a compact MEMS mirror that is more user-friendly for intraoperative applications. Secondly, an efficient and fast image registration algorithm to meet the real-time application demand is necessary.  
Conceptualization, Y.H.; Data curation, S.P., Y.J., C.W. and Y.H.; Funding acquisition, D.A., J.Y., Y.W. and Y.H.; Investigation, S.P., Y.J., K.Z. and Y.H.; Methodology, Y.H.; Project administration, Y.H.; Resources, D.A., J.Y. and Y.W.; Supervision, J.Y. and Y.W.; Visualization, D.A.; Writing--original draft, S.P. and Y.H.; Writing--review & editing, K.Z., C.W. and Y.H.  
This research was funded by the National Key Research & Development Program of China, grant number [2017 YFC0107900], National Natural Science Foundation of China, grant number (61505006, 61527827, 61672099, and 81627803) and Beijing Institute of Technology Science and Technology Innovation Program under Central Special Funds of China for Science and Technology Development.  
The authors acknowledge help from [PERSON] from Institute of Automation, Chinese Academy of Sciences for the animal experiment.  
The authors declare no conflict of interest.
## 2 Data Source Integrity and Parametric Space Definition  
To support the development of a reliable, transparent, and interpretable decision-support model, this study implemented a controlled, literature-informed dataset construction framework that reflects the realistic engineering constraints and environmental variability associated with steel slag use in road base applications. The primary goal was to generate a numerically stable and physically grounded dataset capable of capturing the nonlinear interactions between operational parameters and their dual impact on cost and carbon performance. Specifically, five independent variables were identified and retained based on their physical influence and empirical importance on the two model targets: unit cost (USD/m\({}^{3}\)) and CO\({}_{2}\)-equivalent emission (kg/m\({}^{3}\)). These variables--transport distance, processing energy, moisture content, gradation intensity, and electricity emission factor--were selected through literature triangulation, expert consensus, and correlation analysis. The following subsections describe the following in detail: (i) the rationale behind input variable selection, (ii) the source domains and inclusion criteria used to define input bounds, and (iii) the formulation of output variables through first-principles-based physical models.
## 3 System Model and Problem Formulations
### The Modeling of the Solution Using the Original GA
In the operation of mutation, the mutated value of \(a_{m}\) was no longer randomly selected from a random probability set instead of a certain probability. The factor \(\omega(B_{i})\) that affects probability can be calculated as \[\frac{1}{\omega(B_{i}\,)}=\frac{1}{T_{i}-T_{i-1}}\times\int_{T_{i-1}}^{T_{i}}p_ {g}(B_{i}\,,T_{i}\,,t)dt\] (19)Furthermore, the probability of the selection of scheme access to \(B_{i}\) is:  
\[ \begin{array}{rl}P(B_{i})&=\frac{\omega(B_{i})}{\omega(B_{i})+\omega \big{(}B_{j}\big{)}}\\ &=\frac{\frac{1}{T_{i-1}-1}\times\int_{I_{i-1}}^{T_{i}}p_{g}(B_{i},T_{i},t)dt}{ \frac{1}{T_{i-1}-i}[\int_{I_{i-1}}^{T_{i}}p_{g}(B_{i},T_{i},t)dt+\int_{I_{i-1}} ^{T_{i}}p_{g}(B_{j},T_{i},t)dt]}\\ &=\frac{\frac{1}{T_{i-1}}\times\int_{I_{i-1}}^{T_{i}}p_{g}(B_{i},T_{i},t)dt}{ \frac{1}{T_{i-1}-i}[\int_{I_{i-1}}^{T_{i}}p_{g}(B_{i},T_{i},t)dt+\int_{I_{i-1}} ^{T_{i}}p_{g}(B_{i},T_{i},t)dt]}\\ &=\frac{\int_{I_{i-1}}^{T_{i}}p_{g}(B_{i},T_{i},t)dt}{\int_{I_{i-1}}^{t_{i}} \big{[}p_{g}(B_{i},T_{i},t)+p_{g}(B_{j},T_{i},t)\big{]}dt}\end{array}  \tag{20}\]  
In Equation (20) we can conclude that the higher the PDF of DoU of F-Sat-Beams, the lower the probability of being selected. The overall evolution direction of the mutation operation is limited to optimization in the load balancing direction. In addition, with the observed periods and F-Ser-Beams varying, the evolution direction of the mutation can automatically adapt to real scenarios following the variances of the relative differences of PDF of adjacent F-Sat-Beams.
## Materials and Methods
### System Design
#### 2.2.5 Pathlength Multiplexing Element
Pathlength 3 contains light that is scattered at each extreme--both direct backscattering and high-angle backscattering. This simulation relative PME intensities in Figure 5. To maximize utility of the angular discrimination in Pathlengths 1 and 2, a PME aperture size was selected around 72% of the beam diameter. At this aperture size, the intensities of light in Pathlengths 1 and 2 were about equal, while the intensity of light in Pathlength 3 was about half that of Pathlengths 1 and 2. This selection is indicated by the yellow line in Figure 5, and the angular distribution of light at this aperture size is plotted in Figure 4.  
Given the 12 mm beam diameter after the reflective collimator (Figure 1) and available discrete drill bit sizes, a 7 mm diameter central aperture was selected for the PME. Figure 4: The distribution of backscattering angles in the Monte Carlo simulation for three pathlengths. Pathlength 1 contains the smallest scattering angles, and is most distinguished from Pathlength 2. Pathlength 3 contains both high- and low-angle backscattering and is thus degenerate. Permission was granted by the publisher to reprint this figure from [PERSON] et al. with minor modifications [19]. Figure 5: Monte Carlo simulation shows the relative intensity of each pathlength as a function of PME central aperture size (diameter; percent of beam diameter). The intensity of light in pathlength one increases monotonically with PME aperture size, and the intensity of light in Pathlength 3 decreases monotonically with PME aperture size. The intensity of light due to photons in Pathlength 2 increases and then decreases, with an infection point at a PME aperture size (diameter) of about 60% of the OCT beam diameter. The yellow vertical line corresponds to the angular distributions in Figure 4. Permission was granted by the publisher to reprint this figure from [PERSON] et al. with minor modifications [19].
## 1 Introduction
Based on these observations, the motivations for this study are as follows:  
1. The current methods for detecting inner bore deformation in electromagnetic launchers are inefficient, poorly integrated, and lack a comprehensive means of detecting both surface deformation and straightness. These limitations hinder the frequent application of these methods in the research, optimization, and management of electromagnetic launchers. 2. Current methods lack adequate discussion and viable solutions to the self-deflection issue of detection devices. This deficiency undermines the effective application of detection methods in actual rugged inner bore environments. 3. The current detection methods do not fully utilize the unique inner bore shape of electromagnetic launchers. Consequently, there remains much potential for optimization in the post-processing stage of point cloud data. In this article, a highly effective system for detecting the surface deformation and straightness of the bore in electromagnetic launchers is presented. The system utilizes ring-structured light to scan bore profiles, and incorporates a laser with alignment and ranging capabilities to correct the spatial coordinates of the in-bore detection device. By employing point cloud registration, the widespread issue of the self-deflection in detection devices present in existing methods is resolved, and we ultimately extract the surface deformation and straightness of the bore from the registered point cloud data. Additionally, a coarse registration method that combines circle and parabola equations to leverage rail shape features is proposed, and we use the trimmed iterative closest-point algorithm for the fine registration of non-axisymmetrically deformed bores. Compared with existing methods, the proposed method exhibits the highest detection efficiency. The remainder of this paper is organized as follows: Section 2 describes the composition and detection principles of the inspection system and analyzes the impact of the detection device's self-deflection on the detection results. Section 3 introduces the coarse and fine registration methods for laser point clouds. Section 4 presents the results and a discussion of the bore inspection experimental validation. Section 5 provides a concise conclusion of the main findings of this paper.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 4 ANN-Based Placement Scheme Selection
### Data Set Generation
It is noted that all GMTs are served by the UFBS via the A2G link, utilizing the NOMA technique, while the D2D cooperative transmission is activated to improve the overall communication quality. The A2G and D2D channel gains are generated based on expressions (9) and (14), respectively, while the urban environment parameters are given in Section 5. Concerning the dataset format, it can be expressed as \(D=\{(\mathbf{x_{i}},y_{i})\}\) with \(i=1,\ldots,d\), where \(d\) is the total number of instances. Also, \(\mathbf{x_{i}}\in R^{w}\) is the input vector of the \(i\)-th instance comprised of \(w\) features and \(y_{i}\in\{\text{k - means, k - medoids}\}\) is the class of \(\mathbf{x_{i}}\). In the following, the input features vector \(\mathbf{x_{i}}\) consists of eight parameters, i.e., \(w=8\), that affect the placement procedure of the UFBS and are presented in detail in Table 2. Moreover, for the computation of class \(y_{i}\), we evaluate the total system sum rate \(R_{s}\) in each simulated frame (see Equation (23)) for each UFBS placement procedure. Thus, the class value of the \(i\)-th instance, \(y_{i}\), is determined as the placement method that achieved the highest \(R_{s}\). To precisely train the ANN model and to prevent over-fitting and under-fitting issues, the entire data set \(D\) is divided into training, validation, and testing subsets using the data splitting approach. A popular strategy for data partitioning is to use 70-80% of the entire dataset for training, with the remaining proportion used to improve and assess the trained models. Consequently, 70% of the total samples are chosen for the training phase, 15% for validation, and the remaining 15% for testing the proposed ANN model [37]. The training set is used to train the ANN, the validation set is used to evaluate the performance of the ANN during training, and the testing set is used to evaluate the performance of the ANN after training.
## 3 Model Validation
### Comparison of Prediction Methods of Different Airflow Parameters  
We use the same dataset for prediction analysis and compare the prediction ability of different artificial intelligence models for the constructed intelligent prediction model. Root mean squared error (RMSE), mean absolute error (MAE), and mean relative error (MARE) were used to characterize the accuracy of the prediction (Table 1). By comparing these three indexes, constructing a suitable CNN model has good accuracy in solving the prediction of mine airflow parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **RMSE** & **MAE** & **MAPE** \\ \hline ANN & 0.0965 & 0.00998 & 29.576 \\ SVR & 0.0892 & 0.01065 & 30.452 \\ CNN & 0.0985 & 0.00971 & 26.531 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Error comparison of different prediction methods.  
Figure 6: The locations of the monitoring points and prediction points in ventilation network.  
As shown in Figure 7, it results from data training using the CNN method. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The model training results of the intelligent algorithm are good, and the accuracy is high, which can meet the requirements of the research and application of this project.  
Figure 8 shows the error diagram of 51 sensing points. In the training process, the error of the corresponding point is related to the position of the point in the ventilation system. Therefore, the rationality of the location should be considered in the arrangement of measuring points and sensing points.  
As shown in Figure 9, the output result is obtained through the data input of the 40 monitoring points under the CNN method with 51 sensing points. The result is static, and in practical application, it should be trained according to the rolling data of the 40 monitoring points and then the latest input value of the 40 monitoring points is loaded to realize the intelligent control of the perception and decision module of the intelligent ventilation system.  
Figure 8: Method error statistics of CNN prediction.  
Figure 7: The training results of measured points and perceived points.  
Figure 10 shows the comparison between the predicted results of the training set and the test set and the real values when CNN was used to train the predictive model. The comparison chart shows us that the CNN prediction model we trained can accurately predict and verify the correctness of the model parameter settings.
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 3 Results and Discussion
### Falling-Water-Detection Module Test of PIT  
The FWD module is used to high salinity seawater environments. We verified its hardware performance by analyzing the waveform of the detection pin using a Tektronix  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field Name** & **Contents** & **Data Type** & **Length (Bytes)** \\ \hline ID information & Personnel ID & Char & 8 \\ Functional & Base-station ID & Char & 8 \\ \multirow{2}{*}{
\begin{tabular}{c} Functional \\ information \\ \end{tabular} } & 0x10/0x11 & Char & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Response information content.  
Figure 6: Physical devices of the LR-MPIBS. (**a**) Ship-mounted base station. (**b**) Position-indicating terminal.  
digital oscilloscope. In the waveform shown in Figure 7, Channel 2 (blue waveform) and Channel 4 (green waveform) display the waveforms of the TAS pin and FWDP1 pin (as shown in Figure 3), respectively. Channel 3 (pink waveform) is the output waveform of the power management module (operating voltage 3.3 V).  
At the moment the device falls into the water, the potential of the FWDP1 pin switches to a low level. At the same time, the TAS pin produces an automatic start-up trigger voltage (less than 0.85 V). After PIT startup, the TAS pin remains high and the FWDP1 pin remains low. This waveform is consistent with the theoretical analysis.  
The electrical conductivity of water of different salinities will be different [32]. In this paper, automatic start-up performance tests of the equipment are carried out under different levels of salinity.  
The test process was as follows. A total of 100 mL of tap water was poured into a container, and its salinity was measured by a multi-parameter salinometer, which was 0.05%. The salinity value was then adjusted by adding salt. At the same time, the voltage of the difference between the FWDP1 pin and the power-supply ground, and the difference between the negative and positive terminals of the U1, were observed using an oscilloscope. The test results are shown in Figure 8; the red curve represents the voltage difference between the FWDP1 pin and the GND under different salinity values. This decreased rapidly from 2.670 to 1.4 V when the salinity value was 0-15%, and it stabilized at 1.3 to 1.4 V when the salinity value was 15-40%. As indicated by the blue curve, when the salinity value was in the range of 0.05-40.00%, the voltage difference between the negative and positive terminals of the U1 was consistently greater than 1.4 V (as shown by the black curve), meeting the condition for the U1 to output a low level. Therefore, this module can operate reliably under seawater conditions with salinity greater than 20%.  
Figure 8: Conductivity tests result.  
Figure 7: Analysis of the waveforms from the FWD module.
# Effect Mechanism of Connection Joints in Fabricated Station Structures
## 1 Introduction
Although some scholars have conducted seismic research on fabricated station structures, the existing literature [29; 30; 31; 32; 33] does not give any research results about the weak locations and joints of fabricated station structures. Based on the projects of Yuanjiadian station in Changchun, Fuchunjiang Road Station in Qingdao, and Longxing station in Shenzhen, China, three-dimensional finite element models of fabricated single-ring station structures assembled with seven, five, and four prefabricated components was proposed in the study. The plastic strain, interlayer relative deformation, and internal force were investigated. The joint damage index and residual strength index were offered based on the concrete damage index to evaluate the joints' damage degree. The dynamic time history method was used to compare and analyze the dynamic response characteristics of the fabricated station structure with different joint distribution forms. The effect of the vertical seismic component on the joint damage degree of the fabricated station was studied based on the proposed evaluation method. The research provides scientific evidence for the seismic design and reinforcement of the prefabricated subway station.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 5 Comparison of Online SOC Estimation before and after Model Parameter Optimization
### Comparison of the Estimation Accuracy between the AUKF and the UKF  
The third section explains in detail the process of establishing the AUKF. This section verifies the superiority of the AUKF and the UKF in terms of estimation accuracy in MATLAB/Simulink. The verification process is based on the model parameters before optimization. In this simulation model, the input current \(I(k)\) is integrated using the ampere-hour integral method. As there is no error in the current measurement due to outside disturbances, no accumulative error exists. Thus, the integration of the current in the simulation model can be regarded as the theoretical value of the SOC. The model simulation results and errors are shown in Figures 12 and 13, respectively.  
Figure 11: The input current waveform.  
Figure 12: The model simulation results.  
From the simulation results, the AUKF and the UKF can estimate the SOC online well under different charge and discharge rates. The UKF maximum estimation error is 2.98%, and the maximum estimated error of the AUKF is 2.28%; compared with the UKF, its estimation accuracy is significantly improved.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 5 Engineering of PLA-Based Electrospun Structures for Biomedical Applications
### Control of the Architecture of PLA-Based Nanofibrous Structures  
In a typical electrospinning process, nanofibers are deposited randomly to form a nonwoven web with no orientation [8, 36]. Besides the morphological features, the architecture and arrangement of the fibers in the structure also greatly affect the ultimate function of the nanofibrous matrices. For example, it influences adhesion, proliferation, and penetration of the cells, and also releases the behavior of the biomolecules from the matrix; furthermore, the architecture and structure of scaffolds also considerably affect the wound healing procedure. In addition, the mechanical performance of the matrices is important and can notably be regulated by the arrangement of the fibers in the structure [8, 35]; hence, many attempts have been performed to control the alignment and patterning of PLA-based-electrospun structures.  
By employing various collection devices or by manipulating the electrical field during the electrospinning procedure, fiber deposition and structure architecture can be tuned, in the form of random (Figure 7a), up to uniaxial orientation (Figure 7b). Compared to randomly oriented nonwovens, in aligned structures, cells adhere and grow in the direction of the fiber arrangement. Aligned fiber orientation in the structure can also control the release of incorporated biomolecules by tuning the network porosity [4, 8].  
In a research work performed by [PERSON] et al. [40], the effect of the fiber arrangement on the morphology and mechanical performance of PLA-based electrospun scaffolds has been investigated.  
In this work, the electrospun nanofibers were collected on a grounded rotary drum with the speed of 10 rpm and 200 rpm to form the randomly and uniaxially oriented fibrous depositions, respectively. The stress-strain curves of the PLA-based scaffolds were strongly affected by the fiber orientation. The tensile strength and elastic modulus of the randomly oriented structures were significantly lower than those of aligned matrices. In vitro biological tests confirmed that the aligned arrangement induced homogeneous colonization of the pre-osteoblastic cells. Electrospinning of the PLA-based structures with the oriented arrangement of fibers, such as aligned bundles or twisted yarns (Figure 7c), is a promising approach to develop a new generation of materials for various medical devices.