esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering
## 3 Experimental Analysis and Results
### Experimental Results
The fusion positioning, which incorporates visual odometry data, performs even better. The RMSE of the fusion positioning shows an improvement in accuracy of the most up to 61.3% compared to satellite positioning and GNSS/IMU integrated positioning. In obstructed environments, the accuracy of satellite positioning and GNSS/IMU integrated positioning deteriorates. Despite the influence of lighting conditions, the accuracy significantly improves after incorporating visual data. The RMSE of the fusion positioning shows an improvement in accuracy of the most up to 59.7% compared to satellite positioning and GNSS/IMU integrated positioning. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Experimental Sequence**} & \multirow{2}{*}{**Satellite Positioning/m**} & \multicolumn{2}{c}{**GNSS/IMU**} & \multicolumn{2}{c}{**GNSS/IMU/Visual**} & \multicolumn{2}{c}{**Accuracy**} \\  & & & **Integrated Positioning/m** & \multicolumn{2}{c}{**Fused Positioning**} & \multicolumn{2}{c}{**Improvement (Satellite & \multicolumn{2}{c}{**Improvement (Integrated Positioning)/\%**} \\  & & & **/m** & **Positioning**)/**\%** & **Positioning**)/**\%** \\ \hline \multirow{3}{*}{Unobstructed} & First & 2.8945 & 2.3597 & 1.7611 & 39.2 & 25.4 \\  & Second & 3.3586 & 1.9683 & 1.4131 & 57.9 & 28.2 \\ \cline{1-1}  & Third & 5.2161 & 3.6935 & 3.147 & 39.7 & 14.8 \\ \cline{1-1}  & Average & 3.8231 & 2.6738 & 2.1071 & 44.9 & 21.2 \\ \hline \multirow{3}{*}{Obstructed} & Fourth & 6.1739 & 5.4834 & 3.0609 & 50.4 & 44.2 \\ \cline{1-1}  & Fifth & 9.5697 & 4.7627 & 4.2512 & 55.6 & 10.7 \\ \cline{1-1}  & Sixth & 6.9074 & 5.5784 & 2.8931 & 58.1 & 48.1 \\ \cline{1-1}  & Average & 7.5503 & 5.2748 & 3.4017 & 54.9 & 35.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Navigation positioning solution STD and its accuracy improvement. Figure 4: Position trajectory solution diagram.
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 4 Results
### Implementation Details
#### 4.2.2 Evaluation Metrics  
This paper evaluates the model's segmentation performance using six metrics: Mean Intersection over Union (MIoU), F1-score, Accuracy, model Parameters (Params), Floating Point Operations (FLOPs), and Frames Per Second (FPS). These metrics provide a comprehensive assessment of segmentation accuracy, model complexity, computational efficiency, and real-time performance, offering a balanced evaluation of both effectiveness and efficiency. The calculation formulas for MIoU, F1-score, and Accuracy are as follows:  
\[\text{MIoU}=\frac{1}{N}\sum_{N=1}^{N}\frac{TP}{FN+FP+TP} \tag{8}\]\[\text{F1-score}= \frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{9}\] \[\text{Recall}= \frac{TP}{TP+FN}\] (10) \[\text{Precision}= \frac{TP}{TP+FP}\] (11) \[\text{Accuracy}= \frac{TP+TN}{TP+FP+TN+FN} \tag{12}\]  
where \(TP\) represents the number of pixels correctly predicted as positive, \(FP\) represents the number of pixels incorrectly predicted as positive, \(FN\) represents the number of pixels incorrectly predicted as negative, \(TN\) represents the number of pixels correctly predicted as negative, and \(N\) represents the total number of classes.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 3 Pipeline Overview
### Simplification via Octree-Based Vertex Clustering  
As [PERSON] et al. proposed initially [17], the mesh simplification for dual contouring is a vertex-clustering-based preprocessing step that replaces interior nodes with a leaf containing the representative sample that is determined by the quadratic error function (QEF) [59], which minimizes the sum of least squared distances of all given input planes (Section 5). This function is ill-conditioned in coplanar cases and, therefore, requires robust linear solvers or high numeric precision. In this work, we use the recently introduced probabilistic extension for QEF by [PERSON] and [PERSON] [68] for better robustness.  
Compared with their work, the conditions for our simplification are required to be more strict to preserve manifoldness and silhouette (Section 5). Like previous steps, the bottom-upsimplification is implemented in CUDA using dynamic parallelism to reduce CPU/GPU overhead by dynamically launching a kernel for all current simplification candidates.
###### Abstract  
A timely and accurate damage assessment of buildings after an earthquake is critical for the safety of people and property. Most of the existing methods based on classification and segmentation use two-dimensional information to determine the damage level of the buildings, which cannot provide the multi-view information of the damaged building, resulting in inaccurate assessment results. According to the knowledge of the authors, there is no related research using the deep-learning-based 3D reconstruction method for the evaluation of building damage. In this paper, we first applied the deep-learning-based MVS model to reconstruct the 3D model of the buildings after an earthquake using multi-view UAV images, to assist the building damage assessment task. The method contains three main steps. Firstly, the camera parameters are calculated. Then, 3D reconstruction is conducted based on CasMVSNet. Finally, a building damage assessment is performed based on the 3D reconstruction result. To evaluate the effectiveness of the proposed method, the method was tested in multi-view UAV aerial images of Yangbi County, Yunnan Province. The results indicate that: (1) the time efficiency of CasMVSNet is significantly higher than that of other deep learning models, which can meet the timeliness requirement of post-earthquake rescue and damage assessment. In addition, the memory consumption of CasMVSNet is the lowest; (2) CasMVSNet exhibits the best 3D reconstruction result in both high and small buildings; (3) the proposed method can provide detail and multi-view information of damaged buildings, which can be used to assist the building damage assessment task. The results of the building damage assessment are very similar to the results of the field survey.  
multi-view UAV images; deep learning; CasMVSNet; building damage classification Article
## 2 Materials and Methods
### Data Collection and Calculation of Surface Water Retention and Curve Numbers
#### 2.3.3 Calculation of Surface Retention and Curve Numbers  
Curve numbers were calculated using the Soil Conservation Service (SCS) method, now known as the Natural Resources Conservation Service (NRCS) method. The SCS \(CN\) method describes the relationship between rainfall received and the resultant surface runoff generated by the rainfall event [17] as:  
\[Q=(P-0.2S)/(2P+0.8S) \tag{1}\]  
where \(Q\) = runoff depth (mm), \(P\) = rainfall depth (mm), and \(S\) = maximum retention storage (mm) after runoff begins. The method assumes that the initial abstraction = 0.2S. When rainfall and runoff data are available, \(S\) can be calculated using the following equation [39]:  
\[S=5P+2Q-\sqrt{4Q^{2}+5 PQ} \tag{2}\]  
The maximum \(S\) value on the soil surface is related to the \(CN\) value by the following equation:  
\[CN=25,400/(254+S) \tag{3}\]  
The \(CN\) value is a dimensionless index and has values that range from 0 (no runoff generated) to 100 (all rainfall becomes runoff). \(CN\) = 100 represents a lower boundary of the potential retention storage, and \(CN\) = 0 denotes an upper boundary of the potential retention storage.
## 7 Results and Discussion
### Discussion  
To ensure a fair and representative evaluation, we compare RBPS with four distinct client selection strategies: FedCS, Oort, FRS, and FedProm. These methods were chosen because they reflect fundamentally different priorities in FL system design, including resource awareness, accuracy optimization, fairness, and privacy personalization. Rather than comparing against every existing approach, we focused on methods with minimal overlap to highlight meaningful trade-offs and avoid redundancy.  
Figures 34-36 illustrate the comparative results across 200 rounds of training. As shown, Oort achieves the fastest and highest accuracy convergence, but it does so at the cost of battery inefficiency and privacy leakage. FedProm demonstrates strong privacy preservation, outperforming all methods in that dimension, but with significantly lower accuracy. FRS and FedCS achieve moderate accuracy while maintaining efficient battery use, though they lack mechanisms for real-time privacy control. RBPS, in contrast, provides a robust balance: its accuracy closely approaches Oort, its privacy performance is near that of FedProm, and its battery consumption remains within sustainable bounds. These observations are summarized qualitatively in Table 7, which confirms that RBPS is the only method that performs consistently well across all three dimensions, making it a strong candidate for practical deployment in heterogeneous FL environments.  
Figure 34: Model accuracy over 200 rounds on MNIST.  
Figure 33: Privacy loss over 300 rounds (CIFAR-10).
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 4 Results
### Scalp Diagnosis Web Platform
Additionally, similar cases for follicular erythema are provided in the \"Follicular Erythema step0 Similar Cases\" section, which also shows images of the side and top of the head. The user's scalp condition is visually displayed, with an image and a graph illustrating the step-by-step scalp condition for microscaling and follicular erythema. The text description states, \"Your Scalp is that 'Microscaling' is Mild, 'Follicular Erythema' is Good\", indicating that the user's scalp condition is mild for microscaling and good for follicular erythema. The \"solution section\" offers specific advice for each condition. For mild microscaling, the advice is to \"Use exfoliation products regularly to remove dead skin cells. Maintain your scalp properly by using moisturizing shampoo and conditioner\". For good follicular  
Figure 6: Flowchart of web platform. erythema, the recommendation is, \"Regular cleansing twice a week is important. Keep your scalp healthy with gentle shampoo and conditioner and promote blood circulation through scalp massages.\" This system aims to help users easily understand their scalp condition and apply appropriate management methods. The scalp Diagnosis Web Platform offers several benefits over traditional scalp health assessment methods. First and foremost, it eliminates the need for time-consuming visits to specialists by providing instant online diagnostics. This convenience is especially beneficial for users with busy schedules or those living in remote areas with limited access to dermatological services. Furthermore, the platform enhances diagnostic consistency by leveraging a trained machine learning model, reducing the variability associated with human assessments. The provision of customized solutions based on the diagnosed condition promotes proactive scalp health management, allowing users to address issues before they escalate. Despite its advantages, the platform currently faces certain limitations. The diagnostic accuracy, although promising, could be further improved by expanding the dataset and incorporating more diverse scalp conditions. The moderate category, in particular, exhibited lower performance, indicating the need for more granular data to enhance the model's differentiation capabilities. Future enhancements should focus on augmenting the dataset with more varied and balanced samples, employing advanced data augmentation techniques, and exploring additional deep learning architectures to boost performance. Incorporating user feedback will also be vital in refining the platform's usability and effectiveness. Figure 8: Scalp diagnosis results page. Figure 7: User interface of scalp upload.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 6 Discussion
### Discrepancy in the Predictions  
Further investigation is necessary to enhance prediction in cases of disruptive changes in prices, as observed in the memory-optimized instance family. Considering the complex and multifaceted nature of the issue, it is hypothesized that several potential factors may be contributing to the observed trend. Furthermore, since the efficiency of prediction is not the same in all instance families, though the properties of instances serving as input parameters in the ML model are the same, further investigation on this issue is also useful.  
It is essential to note that the identification and correction of such discrepancies will improve the model's robustness. It is this process of continual refinement that makes machine-learning models effective. By iterating on the model based on findings from real-world data, we can improve its predictive precision, not just for the memory-optimized instance family, but also for other families demonstrating similar complexity. Potential factors to explore consist of:  
_External factors_: Our current model does not fully incorporate the impact of external market dynamics, such as industry trends, policy changes, or technological advancements. These elements, although external to AWS's pricing strategy, can significantly influence it. One could explore the inclusion of additional data sources that capture these external influences, though this is not an easy task. By integrating variables such as global techno  
Figure 5: Comparison of actual and predicted memory-optimized subset prices for 2022.  
logical shifts, regional demand patterns, or even provider policy observations, our model may provide a more comprehensive prediction of price trends, which is beyond the scope of the current study.  
_Instance family's heterogeneity:_ The GPU instance family is a coherent sample. Other larger instance families encompass a diverse range of instances that possess unique capacities and specifications. Hence, it is plausible that the observed disparity could be attributed to the inherent heterogeneity present within this particular group of instances. One potential approach to addressing this issue involves the clustering of similar instances within the family and the subsequent development of individualized models for each cluster.
# Development and Evaluation of Nano-Silica Sustainable Concrete
## 3 Results
### Modulus of Elasticity  
The modulus of elasticity test was performed after 28 days of curing. The results are shown in Figure 5. For the 70% GGBS concrete mixtures, the incorporation of 1% nano-silica increased the Young's modulus remarkably by 42.6% compared to the control mixture (M1). On the other hand, the incorporation of 2% nano-silica increased the Young's modulus by 2.7% for the control mixture (M1), which is less than M2. Similarly, the addition of 1% of nano-silica to the 30% GGBS concrete mixtures had a greater effect than the 2% dosage of nano-silica by 4.4% as illustrated in Figure 5. Nano-silica decreased the deformation of the concrete specimens by nano-filling the pores, which caused the modulus of elasticity of the mixtures to increase.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
###### Abstract  
There are a lot of interference factors in the operating environment of machinery, which makes it ineffective to use traditional detection methods to judge the fault location and type of fault of the machinery, and even misjudment of the fault location and type may occur. In order to solve these problems, this paper proposes a bearing fault diagnosis method based on wavelet denoising and machine learning. We use sensors to detect the operating conditions of rolling bearings under different working conditions to obtain datasets of different types of bearing failures. On the basis of using the wavelet denoising algorithm to reduce noise, we comprehensively evaluated five machine learning models, including K-means clustering, decision tree, random forest, and support vector machine to classify bearing faults and compare their results. By designing the fault classification evaluation prediction criteria, the following conclusions are drawn. The model proposed in this paper is significantly better than other traditional diagnostic models for bearing faults. In order to solve the problem of weak signal strength and background noise interference, this paper selects a better noise reduction algorithm under different quantitative evaluation indicators for wavelet denoising, which can better restore the true characteristics of the fault signal. Using unsupervised learning and supervised machine learning classification algorithms, the evaluation indicators before and after denoising are compared to make the classification results more accurate and reliable. This article will help researchers to intelligently diagnose the faults of rolling bearing equipment in rotating machinery.  
fault detection; wavelet denoising; K-means clustering; decision trees; random forests; support vector machines; AdaBoost algorithm  
18 April 2023  
Revised: 3 May 2023  
Accepted: 9 May 2023  
Published: 11 May 2023
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 4 Simulation Systems and Results
### Simulation Results
#### 4.2.3 The MSE Performance of Algorithm 1 Combined with the IFO Estimation  
Figure 7 shows the MSE performance of Algorithm 1 combined with the IFO estimation (Equation (4)), where \(e\) is set between \(-2\) to \(2\), \(\text{SNR}=4\) dB, \(M=60\), and \(N_{rb}=20\). As shown in Figure 7, the MSE is periodic and remains between \(5.5\times 10^{-4}\) and \(7.5\times 10^{-4}\). Figure 7 indicates that the proposed Algorithm 1 combined with the IFO estimation can achieve the large frequency offset estimation range. In particular, Algorithm 1 can cover \((-0.5,0.5]\) estimation range, independent of the symbol gap between two pilot OFDM symbols.  
Figure 6: The MSE performance with different \(N_{rb}\).  
Figure 7: The MSE performance of Algorithm 1 combined with the IFO estimation.
# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 3 Simulation Model--Practical Verification
#### Setting Parameters and Creating Schedule Variants  
This section describes the process of performing simulation experiments to create variant solutions for a sequential problem in custom production. The proposed system envisages the possibility of the manual or automatic setting of the input parameters for the simulation model. Thus, it is also possible to select a specific decision-making rule for the creation of a timetable or run all available options at once, where, after the verification of one variant is completed, the next variant is automatically launched, and then the evaluation and comparison of the generated variants takes place. Based on the variants evaluated in this way, the final variant is selected and sent directly to production. The sequence of steps used to create the production plan is as follows.  
1. The arrival of a notification in the client interface with a request to change the schedule.
2. The choice of automated or manual control. In the case of automated settings, step 5 follows.
3. The opening of the control menu of the scheduling system.
4. The setting of boundary parameters for a simulation experiment.
5. Connection to the database and the updating of the necessary parameters in the simulation model.
6. The creation of variant solutions for the production schedules in a simulation environment.
7. The final evaluation of the timetable variants.
8. The selection and approval of the final variant in an automatic manner or after confirmation by the planner.
9. The sending of the timetable to a central database to which the company's management system is connected.  
Figure 8: Connection of model and database.  
The implementation of production based on the schedule. In case of deviations, it is necessary to return to step 1.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Substrate**} & **Average Value of** & **Average Value of** & **Statistical** & **Difference** \\  & **I\({}_{2956/2925}\) (HUKE)** & **I\({}_{2956/2925}\) (SHSY-SY)** & **Significance** & **Average Values** \\ \hline CaF\({}_{2}\) & 0.73 \(\pm\) 0.01 & 0.79 \(\pm\) 0.05 & \(p\) \(<\) 0.001 & 0.06 \(\pm\) 0.06 \\ low-e & 0.77 \(\pm\) 0.01 & 0.85 \(\pm\) 0.01 & \(p\) \(<\) 0.001 & 0.08 \(\pm\) 0.02 \\ glass & 0.73 \(\pm\) 0.01 & 0.78 \(\pm\) 0.01 & \(p\) \(<\) 0.001 & 0.05 \(\pm\) 0.02 \\ coverslip & 0.75 \(\pm\) 0.03 & 0.81 \(\pm\) 0.020 & \(p\) \(<\) 0.001 & 0.06 \(\pm\) 0.05 \\ \hline \end{tabular}
\end{table} 
Table 2: Average values of I\({}_{2956/2924}\) for HUKE (second column) and SH-SYSY (third column) cells grown on different substrates (first column). Statistical significance of separation of I\({}_{2956/2924}\) values for the two types of cells is reported in the fourth column, whereas the fifth column shows the difference of average I\({}_{2956/2924}\) values. Figure 3: Average and standard deviation values of the ratio I\({}_{2956/2924}\) between the intensity of the peak at 2956 cm\({}^{-1}\) and the intensity of the peak at 2924 cm\({}^{-1}\) for HUKE (red circles) and SH-SYSY (black circles) cells grown on CaF\({}_{2}\) (**a**), low-e (**b**), glass (**c**), and coverslip (**d**) substrates. not the normal and cancerous counterpart of the same type of cells. This feature is a promising result in view of diagnostic application of FTIR for cancer detection, because we have found that the absorption spectra are able to discriminate cells with different amount of protein/lipid ratio. Overall, this result suggests that, in addition to PCA technique, also the univariate ratiometric analysis, limited to the high wavenumber range, is able to discriminate different cells according to the relative content of cellular components. An important issue to be addressed concerns the choice of suitable substrates to perform FTIR spectra. By performing PCA and ratiometric analysis, we deduce that low-e and glass slides can give a suitable discrimination of HUKE and SH-SYSY cells. Furthermore, CaF\({}_{2}\) and coverslip substrates are also able to provide a sufficient discrimination; however, they are characterized by a broad distribution of I\({}_{2956/2924}\) values for SH-SYSY cells (as for the former) and by significant overlapping of PC1 values related to the two cell types (as for the latter). The obtained results are promising for the perspective of diagnostic application of FTIR technique, because they show that different types of cells can be discriminated also when they are deposited on low-cost slides, such as glass and coverslip, which are widely used in clinical diagnostics.
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
## 1 Introduction
Additionally, this technique offers the advantage of reducing the costs associated with the use of nonstandard lenses for image capture, thereby increasing the accessibility of the technique for various applications.