esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Pressurized Capsule Structure
### The Development Course of Pressurized Capsule Structure
The integrity of the structural components greatly improves the structural efficiency and reliability, and effectively reduces the structural quality. Compared with the traditional semi-monocoque shell structure relying on riveting and other connection methods, the panel structure has the advantages of less parts, small assembly workload, short process cycle, large overall strength and stiffness, and superior sealing performance and anti-fatigue performance. High reliability and high structural efficiency make the panel structure the best choice for the pressurized capsule structure of long-term service manned spacecraft represented by the space station. Russia and the United States have developed the welded panel pressurized capsule structure on the 'Mir' space station and the International Space Station. China, the United States, Russia and other countries have designed and developed a number of WPPCS, such as the MPLM capsule structure of International Space Station with WPPCS (as shown in Figure 4b), experimental capsule structure of Tiangong-1 with WPPCS (as shown in Figure 5b), Orion manned spacecraft crew capsule structure with WPPCS (as shown in Figure 5c) and pressurized capsule structure of Tianhe core module with WPPCS (as shown in Figure 5d).
## 3 Results  
### Evaluations of Three Models in Modeling LUE  
Figure 4 presents the regression analysis between modeled LUE and LUE obtained by EnKF for the test dataset. For VPM and VPRM, modeled LUE is the product of \(\text{LUE}_{\text{max}}\) and \(f\). The results suggest that the MLP is capable of accurately capturing the variations in LUE obtained by EnKF with an R\({}^{2}\) value of 0.60 for the test dataset. MLP also produced a low RMSE of 0.36 gC MJ\({}^{-1}\) for the test dataset. On the test dataset, MLP performed significantly better than VPM (R\({}^{2}\) = 0.38 and RMSE = 0.43 gC MJ\({}^{-1}\)) and VPRM (R\({}^{2}\) = 0.17 and RMSE = 0.69 gC MJ\({}^{-1}\)). MLP also showed better performance than VPM and VPRM on the training and validation datasets (Appendix C, Figure A1). The results suggest that MLP can reduce the uncertainties in quantifying LUE as compared to VPM and VPRM. In other words, MLP can indeed reduce the uncertainties introduced by \(\text{LUE}_{\text{max}}\) and environmental stress factors of conventional LUE models.  
Figure 3: The distribution of data for extreme environmental conditions. (**a**) The distribution of data for extremely wet and dry conditions. (**b**) The distribution of data for high-temperature and low-temperature conditions. High-P, Low-P, High-T\({}_{\text{a}}\), and Low-T\({}_{\text{a}}\) denote extremely wet, dry, high-temperature, and low-temperature conditions, respectively.
## 5 Conclusions  
ECG is an important medical detection tool. Making full use of the information contained in ECG for intelligent identification and classification can effectively improve the discovery and treatment rate of cardiovascular diseases and reduce the shortage of medical resources at this stage. Therefore, this study proposes an improved 1D residual ECG classification model (APRN) based on the ResNet-18 network. The effect of the APRN network model is more intuitively illustrated by comparison with CNN, CNN-R, and ResNet-18 for training and testing. Among them, CNN and CNN-R are constructed in this study, while ResNet-18 is the classical residual network model.  
The experimental results show that the average accuracy of the APRN network in all data can reach 97.89%. The accuracies of five single-lead datasets are 96.24%, 98.41%, 98.67%, 99.18%, and 97.88%. The one double-lead dataset shows an accuracy of 97.68% and, at the same time, has high performance in classification of various categories. The proposed APRN can provide a new direction of using the residual network for follow-up ECG research. In the network training, the optimization of the maximum pooling layer can be fully utilized to effectively learn the data segments used to distinguish specific categories in 1D ECG data. At the same time, as the pooling layer does not affect the number of data channels and the two residual connections used in the APRN do not cause an increase in parameters, the parameters of our APRN are almost the same as those of rennet, which means that the two approaches have almost no difference in the training time. From the results, the pool layer is more conducive to extracting the generation characteristics of 1D ECG data.  
In addition, all types of data used in this study are unbalanced. The main characteristic is that the normal samples are much more than the abnormal samples. The experiments reveal that the classification accuracy of each category does not have a large error. However, given that we carry out the experiment on a small dataset, we still need to train and test the model on a large dataset.  
Finally, experiments are carried out with common classification algorithms of machine learning, and the results show that the network proposed in this paper has a better effect on ECG feature extraction than common algorithms of machine learning.  
Conceptualization, J.Z. and Z.Z.; methodology, J.W.; software, Y.Z.; validation, [PERSON] and J.Z.; formal analysis, J.W.; investigation, J.W.; resources, C.X.; data curation, J.W.; writing--original draft preparation, J.W.; writing--review and editing, J.W. and J.Z.; visualization, J.Z.; supervision, Z.Z.; project administration, Y.Z.; funding acquisition, C.X. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the National Natural Science Foundation of China (No. 62001430) and Key R&D Program of Shanxi Province (No. 202102020101010).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
For underwater acoustic covert communications, biomimetic covert communications have been developed using dolphin whistles. The conventional biomimetic covert communication methods transmit slightly different signal patterns from real dolphin whistles, which results in a low degree of mimic (DoM). In this paper, we propose a novel biomimetic communication method that preserves the large DoM with a low bit error rate (BER). For the transmission, the proposed method utilizes the various contours of real dolphin whistles with the link information among consecutive whistles, and the proposed receiver uses machine learning based whistle detectors with the aid of the link information. Computer simulations and practical ocean experiments were executed to demonstrate the better BER performance of the proposed method. Ocean experiments demonstrate that the BER of the proposed method was 0.002, while the BER of the conventional Deep Neural Network (DNN) based detector showed 0.36.  
secure LPD/LPI communication; modulation; signal processing; bio-mimetic +
Footnote †: journal: Sensors
# Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service
## 2 Data Description
### ERA5 Reanalysis  
The fifth generation ECMWF reanalysis (ERA5) in pressure-level format is employed in this study. It can provide meteorological variables for 37 specific pressure levels from the earth's surface to about 80 km [34]. The specific humidity of ERA5 reanalysis will be integrated into PWV for water vapor accuracy assessment in this study. The detailed method for the integration process can be found in [35]. The ERA5 reanalysis can be downloaded from the Climate Data Store (CDS) via [[https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview)]([https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview)) (accessed on 12 June 2025).  
Figure 1: The installation environment of the antenna (**a**) and Hexagon FangZhou receiver (**b**), which are part of the water vapor monitoring system.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 3 Proposed Method
### COordinate Rotation DIgital Computer (CORDIC) Iterative Reduction Algorithm
Then we continue to compare the value 7, because the residual value 3 of the input angle is smaller than the value 7 of the fixed comparison angle, the condition is not true. Then we continue to compare the fixed angle value 3; the input angle residual value 3 is equal to the fixed input angle 3, so the angle will do the subtraction action. After the subtraction, the residual value is negative 0.1, so we continue to find a fixed input angle equal to or less than 0.1. Finding a fixed input angle of 0.111905677 will add up. The main principle of the traditional rotation angle is that the input angle must approach zero. Therefore, the last residual value is a negative value, the next search will be added, and the input angle will gradually approach the condition. When the final calculation is performed, whether the positive or negative residual value is not less than 0.001, the angle will continue to be compared until the residual value is less than 0.001 to jump out of the loop, as shown in Figure 6. In this way, the input angle Z is 30 degrees, and only five iterations are needed to find the required value. The rotated vector will be different from the original vector. To make the vector length the same, the vector needs to be multiplied by the vector length correction factor. However, if the pre-angle selection method is used, the original value needs to be set to X = 1, Y = 0, so the vector correction factor must wait for the X and Y rotations before the K total product is multiplied after each iteration. In the traditional CORDIC algorithm, it can be known that for some specific angles, a simple shift  
Figure 6: Example of angle selection. instruction can be used, so let \(\tan\alpha_{i}=2^{-i}\) and \(\mathrm{K}=\prod_{i=0}^{n-1}\cos\alpha_{i}\). Through the Pythagorean theorem in the trigonometric function, we get \(\cos\alpha_{i}=\frac{1}{\sqrt{1+2^{-2i}}}\). The total product of \(\mathrm{K}\) has an iteration coefficient \(\mathrm{i}\) for each iteration. Substituting \(\mathrm{i}\) into Equation (11), we can obtain the vector length correction factor. Finally, the total product of \(\mathrm{K}\) can be calculated as the sum of the number of iterations corrected each time. Taking the input angle of 30 degrees as an example, the selection angle selects the iteration angles of \(\mathrm{i}=1\), 4, 9, 11, and 15, respectively, so the correction length factor \(\mathrm{K}\) is \(\mathrm{K}1\times\mathrm{K}4\times\mathrm{K}9\times\mathrm{K}11\times\mathrm{ K}15\). The \(\mathrm{K}\) value of each iteration is shown in Table 4.  
\[\mathrm{K}\ =\prod\
olimits_{\mathrm{i}=0}^{n-1}\left(\frac{1}{\sqrt{1+2^{-2 \mathrm{i}}}}\right) \tag{11}\]
# A Self-Calibration Method for Robot End-Effector Using Spherical Constraints
## 5 Simulation Experiment
### Calibration of End-Effector Coordinate System  
The end-effector coordinate system refers to determining the position \(O_{t}X_{t}Y_{t}Z_{t}\) relative to the robot end flange coordinate system \(O_{b}X_{b}Y_{b}Z_{b}\). This relationship can typically be derived from the robot's 3D model design drawings or using the calibration programs provided by KUKA. In this experiment, the 4-point calibration program of the KUKA robot was used to obtain the transformation matrix between the end-effector coordinate system and the robot end flange coordinate system, as shown below.  
\[T_{t}^{6}=\begin{bmatrix}0.7773&0.6273&-0.0489&-0.0599\\ -0.6260&0.7788&0.0393&0.0505\\ 0.0627&0.0007&0.9980&0.4103\\ 0&0&0&1\end{bmatrix} \tag{40}\]
## 2 Methods
### Pinball-MAPE Loss Function  
Most existing prediction algorithms focus primarily on improving prediction accuracy while neglecting bias, which plays a crucial role in fatigue prediction. In this study, the  
Figure 2: Improved N-BEATS architecture. (**a**) Fully connected nonlinear regressor block. (**b**) Residual stacking with multiple layers. (**c**) Stack aggregation of outputs for the final prediction.  
MAPE [22] is used as the primary accuracy metric. Using it as a loss function ensures maximum consistency between the training process and performance evaluation. However, the bias generated during the prediction process cannot be determined by the MAPE. [PERSON] [23] suggested that using lower \(\tau\) can help avoid overprediction by the model during training. To minimize bias as much as possible during prediction, this paper adopts Pinball-MAPE as the loss function:  
\[\text{Pinball-MAPE}(y,\hat{y})=\frac{1}{N}\sum_{i=1}^{N}\begin{cases}200\cdot \tau(y_{i}-\hat{y}_{i})/y_{i}&\text{ if }\quad y_{i}\geq\hat{y}_{i}\\ 200\cdot(1-\tau)(\hat{y}_{i}-y_{i})/y_{i}&\text{ otherwise }\end{cases} \tag{4}\]  
In the formula, \(N\) represents the number of samples, \(y_{i}\) denotes the true value, and \(\hat{y}_{i}\) represents the predicted value. The parameter \(\tau\) in Pinball-MAPE can be tuned on the validation set to offset bias introduced during training.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 4 Results and Discussion
### Effects of Different Features  
The input of our model was composed of three types of features: pollutant concentration (\(F_{p}\)), meteorological data (\(F_{m}\)), and PM\({}_{2.5}\) concentrations of adjacent monitoring stations (\(F_{a}\)). To evaluate the effectiveness of different features in the proposed AC-LSTM model, we conducted experiments with different combinations of features and computed the errors on the multiscale prediction tasks. Because the number of monitoring stations in Taiyuan is too small, we used PM\({}_{2.5}\) concentrations from all stations rather than from adjacent stations. The effects of various features in AC-LSTM are shown in Tables 4 and 5. As can be seen, by gradually adding features, the prediction accuracy of the model could be generally improved. Except for the lowest MAE in the next 1 h and 13-24 h prediction tasks, the model with three types of features as input had the best overall performance. This shows that the past feature states and the PM\({}_{2.5}\) concentrations of adjacent monitoring stations can help predict the PM\({}_{2.5}\) concentration.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & **1 h** & **2 h** & **3 h** & **4 h–6 h** & **7 h–12 h** & **13 h–24 h** \\ \hline \(F_{p}\) & 7.61 & 8.12 & 8.21 & 8.53 & 8.57 & 9.04 \\ \(F_{p}+F_{m}\) & 7.98 & 7.99 & 7.99 & 8.51 & 8.6 & 8.89 \\ \(F_{p}+F_{m}+F_{a}\) & 7.68 & 7.97 & 7.98 & 8.38 & 8.51 & 8.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mean absolute error (MAE) of various features in AC-LSTM.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time Lag** & **2** & **4** & **6** & **8** & **10** & **12** & **14** & **16** \\ \hline MAE & 8.21 & 7.89 & 7.9 & 7.82 & 7.75 & 7.68 & 7.61 & 8.04 \\ RMSE & 13.81 & 13.2 & 13.22 & 13.08 & 13.01 & 13.06 & 13.09 & 13.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of different time lags. MAE—mean absolute error; RMSE—root-mean-square error.
## 3 Results and Discussion
### Small-Scale Problems Solved by Gurobi
Essentially, the single-task assignment gets a local optimal solution. In each task time window, the integer-linear-programming-based multi-task assignment (MTA) determines the execution order of tasks according to the relationship between tasks, which will obtain an optimal solution. We use Gap to represent the difference of sum-of-cost (SOC) or makespan (MS) obtained by single-task assignment (STA) based on the nearest distance and the ILP model. The expression of the Gap for sum-of-cost (SOC) and makespan (MS) is:  
\[Gap_{SOC}=(SOC_{\text{STA}}-SOC_{\text{ILP}})/SOC_{\text{STA}} \tag{35}\]  
\[Gap_{MS}=(MS_{\text{STA}}-MS_{\text{ILP}})/MS_{\text{STA}} \tag{36}\]  
The results are shown in Table 2. The minimum sum-of-cost (SOC) and makespan (MS) obtained by the ILP model is reduced by at least 30% and 37.6%, respectively, compared to the nearest-distance-based single-task assignment (STA), and even by 44.3% and 50.2% in some cases. The results show that the integer linear programming (ILP)-based multi-task assignment (MTA) has great advantages over the single-task assignment method based on the nearest distance for small-scale problems. Figure 10: Task sequences in experiment 4, including 5 robots and 25 tasks. Figure 9: Tasksequences in experiment 3, including 5 robots and 20 tasks. To further explore the task assignment of each robot in the four small-scale experiments, the cost of each robot's task sequence is represented by a histogram under the patterns of \((w_{1},w_{2})=(1,0)\), \((w_{1},w_{2})=(0,1)\), and single-task assignment (STA). As shown in Figure 11, when \((w_{1},w_{2})=(0,1)\), the total cost of each robot's task sequence is relatively balanced and the value of makespan (MS) is the smallest. When \((w_{1},w_{2})=(1,0)\), the sum-of-cost (SOC) is the smallest. For the single-task assignment (STA) based on the nearest distance, the values of sum-of-cost (SOC) and makespan (MS) are larger than the other two task assignment patterns. From the above analysis, we know that the two-objective integer linear programming (ILP)-based multi-robot task assignment in this paper has practical significance and can significantly improve the operating efficiency and reduce the operating cost of an intelligent warehouse.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
###### Abstract  
The advent of satellite-borne machine learning hardware accelerators has enabled the onboard processing of payload data using machine learning techniques such as convolutional neural networks (CNNs). A notable example is using a CNN to detect the presence of clouds in the multispectral data captured on Earth observation (EO) missions, whereby only clear sky data are downlinked to conserve bandwidth. However, prior to deployment, new missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missions will underperform when deployed to process the data on the new missions. This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and future missions. In this paper, we address the domain gap problem in the context of onboard multispectral cloud detection. Our main contributions lie in formulating new domain adaptation tasks that are motivated by a concrete EO mission, developing a novel algorithm for bandwidth-efficient supervised domain adaptation, and demonstrating test-time adaptation algorithms on space deployable neural network accelerators. Our contributions enable minimal data transmission to be invoked (e.g., only 1% of the weights in ResNet50) to achieve domain adaptation, thereby allowing more sophisticated CNN models to be deployed and updated on satellites without being hampered by domain gap and bandwidth limitations.  
Earth observation; satellite; multispectral; cloud detection; convolutional neural network; domain adaptation +
Footnote †: journal: _remote sensing_
# Profiling Taste and Aroma Compound Metabolism during Apricot Fruit Development and Ripening
## 2 Results
### Correlation between Enzymatic Capacities and Flavor Compound Accumulation  
To search for possible links between enzymatic activities and flavor compounds accumulation, a Spearman's correlation analysis was performed.  
As seen in Figure 8a, the contents of fructose, glucose and sucrose are significantly positively correlated with SPS activity (\(p<0.01\)). Similarly, a significant positive correlation was observed between the contents of sucrose, total sugars and Sathy activity (\(p<0.01\)). In contrast, a significant negative correlation was found between the contents of sucrose, total sugars and Ssca activity. No other significant correlations were found between the enzyme activities of sugar metabolism and sugar accumulation. Regarding organic acids (Figure 8b), the content of malate was significantly positively correlated with MS activity, but negatively with NADP-ME and NAD-ME activities (\(p<0.01\)). The content of citrate was significantly positively correlated with CS activity, but negatively with GAD activity (\(p<0.01\)). As shown in Figure 8c, ester and lactone accumulation is significantly positively correlated with LOX, HPL, ADH, AAT and ACX activities (\(p<0.01\)), however, significant negative correlations were found between the content of aldehydes and ADH activity. The contents of apocarotonoids and terpen are significantly positively correlated with CCD and TPS activities, respectively.  
Figure 8: \(Cont.\)
## 5 Discussions and Future Perspectives
### Ongoing Solutions and Advancements  
Several emerging strategies show promise in mitigating current challenges:  
* Hybrid Architectures: Combining CNNs, Transformers, and DMs leverages the strengths of each. Hybrid models like PASTA [50] integrate temporal and spatial attention mechanisms, enhancing alignment and multi-exposure fusion while reducing artifacts and improving visual quality.
* Data Augmentation Techniques: Methods like neural data augmentation [88] and HDR-LMDA [89] expand the diversity of datasets, increasing model robustness against variations in exposure and lighting. They simulate different capture scenarios, improving generalization capabilities.
* Self-Supervised and Unsupervised Models: Methods that reduce reliance on large annotated datasets are gaining attention. Self-supervised models can learn representations from unlabeled data, simplifying training in data-scar environments [50]. Approaches based on Large Language Models (LLMs), such as LLM-HDR, explore self-supervised learning for unpaired LDR-to-HDR translation [179].
* Computational Optimization: Efforts to develop lighter and more efficient models, such as RepUNet [122], aim to reduce computational costs without substantially compromising HDR image quality. Techniques like pruning, quantization, and knowledge distillation also yield more compact models suitable for real-time applications. Hardware-based solutions, such as those using FPGAs and optimized lookup tables, also represent viable paths to achieve real-time HDR video processing [178].
* Integration of Multi-modal Data: Incorporating depth, polarization, event cameras, or other modalities can enhance HDR reconstruction by providing deeper scene perception and facilitating exposure alignment and fusion.
* Better Loss Functions: New hybrid loss functions considering perceptual and structural aspects of HDR images are being introduced to guide model training more effectively. Combining adversarial, perceptual, and reconstruction losses helps re duce artifacts [41]. Losses guided by semantic information or human knowledge (e.g., LLM-based loss) are also being explored [179].