esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Researchers [17] presented an approach which used dual-frequency data from multiple low Earth orbit (LEO) satellites to generate a global topside ionospheric map, calculating the ionospheric electron content more accurately. [PERSON] et al. [18] concluded that the smartphones showed potential as a useful course of TEC for ionospheric observations by analyzing the quality of smartphone-based TEC measurements. [PERSON] et al. [19] proposed two approaches which can significantly improve the accuracy of the ionospheric model to combine GNSS and LEO observation data for ionosphere modeling. At present, most ionospheric models use the method of segmented estimation. This method uses all epoch observations in a period to calculate a set of model parameters, which can obtain higher accuracy during the ionospheric quiet period. However, during the active period of the ionosphere, large errors will be caused by ignoring part of the local high-frequency information. Dual-frequency observations can monitor changes in the inversion area or global ionospheric distribution in real-time, but the amount of dual-frequency data on smartphones is small and the availability is low. Based on the above analysis, this article uses CORS dual-frequency GNSS observations to invert the ionospheric model, and transmit the results to the smartphone to achieve real-time ionospheric correction. The experimental setup and raw observations analysis are described in Section 2. In Section 3, the positioning algorithm of smartphone is described in detail. The experimental results are presented in Section 4. The conclusion and discussion of this work are in Section 5.
## 4 Experiments and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Ours**} & \multicolumn{2}{c}{**[PERSON] [18]**} & \multicolumn{2}{c}{**[PERSON] [20]**} \\ \cline{2-7}  & **TPR (\%)** & **TNR (\%)** & **TPR (\%)** & **TNR (\%)** & **TPR (\%)** & **TNR (\%)** \\ \hline
1 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline
2 & 99.8 & 100 & 100 & 97.3 & 100 & 100 \\ \hline
3 & 100 & 100 & 95.5 & 100 & 100 & 100 \\ \hline
4 & 99.3 & 100 & 100 & 98.7 & 100 & 93.2 \\ \hline
5 & 99.6 & 100 & 98.8 & 100 & 99.7 & 100 \\ \hline
6 & 98.5 & 100 & 100 & 99.6 & 100 & 100 \\ \hline
7 & 100 & 100 & - & - & - & - \\ \hline
8 & 100 & 100 & - & - & - & - \\ \hline
9 & 97.3 & 100 & - & - & - & - \\ \hline
10 & 100 & 99.3 & - & - & - & - \\ \hline mean & **99.45** & **99.93** & 99.05 & 99.27 & 99.95 & 98.87 \\ \hline \end{tabular}
\end{table} 
Table 4: True negative rate (TNR) and true positive rate (TPR) performance comparison. Figure 6: ROC curve and AUC-ROC for 10 classes of data in DAGM dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **[PERSON]’s [21]** & **[PERSON]’s [18]** & **[PERSON]’s [20]** & **Our** \\ \hline Parameters & 6.07M & 6.98M & 135.59M & **2.12M** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of different networks.
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 2 Materials and Methods
### Apple Grading Overall Design  
This study developed a simplified apple grading pipeline apparatus, primarily comprising seven components: a conveyance control system, an image acquisition unit, stem detection, fruit body image preprocessing, multi-feature extraction, grading model evaluation, and automatic sorting. By effectively integrating traditional machine learning techniques with cutting-edge deep learning technologies, a new architecture was designed specifically for the apple grading task, achieving efficient grading of apples. The detailed structure is illustrated in Figure 2.  
Figure 1: Dataset samples and visualization. (**a**) Complex background + multiple targets + small size. (**b**) Simple background + multiple targets + normal size. (**c**) Complex background + single target + normal size. (**d**) Simple background + single target + normal size. (**e**) Detailed distribution of annotation boxes.  
Figure 2: Schematic diagram of apple grading.  
The conveyance control system consists of a standard conveyor belt and a drive motor, which can dynamically adjust the speed of the pipeline according to requirements. The image acquisition unit includes a high-performance computer, an image acquisition device, and high-definition cameras. Inside the image acquisition unit, three high-definition wide-angle distortion-free industrial cameras are placed. During image capture, a ping-pong-ball-assisted calibration method is used first. By calculating the error between the diameter of the ping-pong ball in the captured image and its actual diameter, the distance between the camera and the apple is determined. When an apple is detected within the top camera's field of view, the camera in the overhead image capture area checks for the presence of a stem. Apples without stems will not trigger the capture of frontal and rear views and will not proceed to the subsequent grading process. This enhances the efficiency of the apple grading pipeline and reduces resource consumption to some extent. The stem is also used for auxiliary positioning, and once the stem detection box fully enters a designated area, apples with stems trigger the computer to control the other two cameras to capture the front and rear views in sequence. The front and rear image capture focuses on the apple's maximum cross-sectional area, approximating the apple as a sphere. High-definition cameras use an equidistant view method to capture the entire side view of the apple from the front and rear angles. Image processing operations are then applied to extract key feature information of the apples, followed by model comparison, training, and evaluation. The model with the optimal performance is selected to determine the apple's grade. Finally, a robotic arm sorts the apples into specified grade areas.
# Bonding Behaviors of GFRP/Steel Bonded Joints after Wet-Dry Cyclic and Hydrothermal Curing
## 1 Introduction  
Subjected to various loading and working conditions, engineering structures showed different types of deterioration during their service life [1; 2; 3]. To mitigate such structural decreases, various repairing/retrofitting methods have been studied [4; 5]. Among them, research on composite materials, especially, fiber-reinforced polymer (FRP), for steel structures has significantly increased during the past few decades [6; 7; 8; 9], and the outstanding performance of FRP retrofitting/strengthening steel structures has been determined [6; 10; 11]. The most widely used composite materials are carbon fiber-reinforced polymer (CFRP) and glass fiber-reinforced polymer (GFRP). CFRP is commonly used where high elastic modulus is required [12]; even so, the much lower cost of GFRP is very important for a broader use of GFRP in engineering applications where low modulus of GFRP structures will not restrict the performance of composite systems, such as in steel-FRP hybrid bridges [13]. As a result, a lot of studies have been conducted on the behaviors of GFRP/steel strengthening systems [6; 14; 15].  
The design and short-term mechanical behavior of GFRP/steel strengthening systems in constant environments have been researched and discussed extensively [10; 16; 17]. For engineering structures such as bridges, they are exposed to highly variable environments; for example, there are daily or seasonal changes in humidity and ambient temperature, which could affect the long-term behavior of adhesively bonded joints [18; 19]. However, in-depth study on the long-term behaviors of the adhesively bonded joints considering moisture and thermal effect is still very limited.  
To fill this research gap, this paper investigates the influence of different curing conditions on the mechanical behaviors of GFRP/steel bonded joints. Within this study, two series of curing program were introduced (wet-dry curing and hygothermal pretreatment). The wet-dry curing experimental program included the characterization of cyclic moisture diffusion and thermal effects of the GFRP/steel bonded joints cured for different durations, whereas the hygothermal pretreatment included different curing conditions of hygothermal environments and durations. Based on the experimental study, the failure modes, tensile capacities, and strain curves were investigated, from which the interfacial fracture energy \(G_{f}\) of the bonded joints were calculated and discussed.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 2 Related Work
This can result in null values of the RSSI in position determination and can lead to negative effects on performance, computational complexity and indoor localization system scalability [36]. This paper presents a secure indoor location algorithm based on the trusted location fingerprint. The proposed algorithm can be applied to many scenarios, including earthquakes, explosions and floods. The algorithm can identify the impaired AP after an accident and select the safe and trusted AP to locate. For example, in the case of a magnitude 5.0 earthquake, it may lead to the collapse of a few tunnels and the breakdown of some pipes. In this case, the algorithm can exclude the impaired APs and select safe APs to locate the target and carry out rescue work. In the event of a major accident, the algorithm identifies that all the APs have been impaired, and then, the positioning system may fail. If the positioning system can operate, the reference value of the positioning result will be determined according to the number of impaired APs. The main contributions of this paper are as follows:
* A fingerprint database update model based on error analysis is introduced. According to the characteristics' analysis of RSSI and error verification, the fingerprint database is updated partially. It can reduce the update overhead and avoid the introduction of unreliable fingerprints when updating the fingerprint database. * We propose a trust factor extraction mechanism based on the voting algorithm. APs are voted through the test of RSSI based on reference points. According to the vote, results extract APs' trust factor, reducing the impact of untrusted APs on localization. * We designed a fingerprint matching algorithm based on weight distribution. By comparing the RSSI value received on the online phase with the offline phase, we can filter out the broken APs and the new APs. The trust factor of APs is used in the calculation of fingerprint weight, to reduce the impact of untrusted fingerprints on the location algorithm.
# A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network
## 3 Numerical Experiments
### The One-Quarter Plane-Symmetric Forging  
The CNN models were trained by utilizing 3D data arrays; thus, the trained models could derive the 3D preform shapes for the 3D inputted forging geometries. However, the training dataset used in this study was defined by considering axis-symmetric forgings. Therefore, to investigate the capability of the CNN sub-models trained with axisymmetric 2D images, and to accurately derive the preform shape for the case of non-axisymmetric shapes, two strategies were implemented for a \(\frac{1}{4}\) plane-symmetric simple forging, as shown in Figure 11. The first consisted of directly inputting the 3D images into the CNN sub-models. The second consisted of inputting separately the axisymmetric shapes of three cross-sections along \(0^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\).  
Considering the first approach, the six shapes of the preform candidates are shown in Figure 12a and Table 3 and the preform shape derived by CNN#2 ensured the highest forging load reduction of 11.1% compared with that of a cylindrical bitlet (Figure 12b). The FEM results presented in this section were acquired through a 3D thermo-mechanical FEM model, as described in Appendix B.  
Figure 11: The1/4 plane-symmetric forging with cross-sections along \(0^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\) directions.  
Figure 12: (a) Preform geometries for the symmetric forging and (b) forging force–punch patch comparison.  
Regarding the second approach, the three preforms estimated for each of the three considered directions (\(0^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\)) were combined into a single image by a Boolean union and smoothed in the transition regions between the three angles.  
Considering the results reported in Figure 12b, the preform shape derived by CNN#2 resulted in a maximum forging load reduction. For this reason, the preform shapes by inputting the 3D arrays to CNN#2 and by inputting the cross-sections at \(0^{\circ}\), \(45^{\circ}\), and \(90^{\circ}\) to CNN#2 were utilized in two forging simulations, and the relevant forging force versus stroke results are shown in Figure 13b.  
The comparison between the \(0^{\circ}\) and \(90^{\circ}\) cross-section dimensions of the two preforms derived through these two methods shows that they are remarkably similar in both the overall shape and dimensions. This indicates that although the training dataset was defined by considering axis-symmetric forgings, the proposed method can be applied to forging geometries in which at least one symmetry plane can be identified.
## 2 Grid Forming and Small-Signal Modeling
### Summary  
In order to increase the damping provided by the GFM-based IBR, there are two possibilities: reduce the droop constant (increasing the emulated inertia or not), which increases the damping, or implement an additional POD control loop.  
This second option is more complex--especially when a POD-P structure is used. The general dynamics and properties of the GFM can be significantly altered, potentially reducing the provided inertia or even leading to system instability. Therefore, this option should not be implemented lightly. A thorough analysis of the interaction between the POD-P structure and the specific GFM control is essential. This paper proposes the use of the NFP visual method as a tool for this analysis.  
GFM control also supports the implementation of POD-V and POD-Q loops. These two methods use voltage variation to damp low-frequency oscillations (LFOs) but they present some limitations compared to active power-based methods. For instance, the voltage at the point of common coupling (PCC) must remain within certain limits--typically \(\pm\)10% of the rated voltage--restricting the variation range. As a result, the reactive power output from the POD-Q or POD-V algorithms must be kept within these bounds. Additionally, the effectiveness of reactive power injection depends on the short-circuit ratio (SCR) of the grid: the higher the SCR, the lower the impact of injected reactive power on PCC voltage. In strong grids, this effectiveness can be very limited.  
In summary, while POD-V and POD-Q introduce fewer control compatibility issues than POD-P, their damping effectiveness may be reduced under specific conditions. It is important to highlight that the use of POD-Q or POD-V does not exclude the implementation of POD-P [2].  
Since POD-P/V algorithms generally present low compatibility issues with GFM control, their structure is very similar to those used in STATCOMs or HVDC inverters. As a result, current research primarily focuses on POD-P controls.
# CMS: A Continuous Machine-Learning and Serving Platform for Industrial Big Data
## 4 Orchestrator
### Model Validation  
The newly generated model might not always be feasible for production-level usage, due to the disruptions from inconsistent data, broken data, and failures in the underlying execution environments. The broken data might result in generating inaccurate models which cannot be deployed into production environments. Although some data-cleaning techniques can detect and remove errors and inconsistencies from data and improve the data quality, it is difficult to predict whether a learning algorithm will behave reasonably on new data [35]. Thus, the newly generated models should be validated before their usage in production. This is vital to the reliability and robustness of the industrial machine-learning platform, as a bad model might lead to catastrophic consequences.  
The model validation contains two different stages: the offline validation and the online validation. The offline validation is performed by checking the prediction quality with the test set when training a new model. Generally, we retain 20% of the data as test data to calculate the MAE (Mean Absolute Error) of the new and old model (the current adopted model) separately. When the new model's MAE is less than the old model, it means that the model verification is passed. The online validation is performed when the model has passed offline validation and is performed on live traffic rather than by the objective function on the training data. In the current prototype, a model would pass the online validation only if running MAE below a certain threshold during an arbitrarily defined period.
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 2 Materials and Methods
### Experimental Procedure  
In this study, five different ML algorithms were applied using the MATLAB software in the prediction process. The aim of this study is to determine the model's estimation efficiency for suggesting the appropriate raster angle of a new part. Five performance criteria, including mean squared error, mean absolute error, root mean squared error, R-squared, and explained variance score, were employed to test the performance of the proposed model using the cross-validation technique. Algorithms must be trained using experimental data to develop the ML-based process shown in Figure 2.  
Models with different structures, some of which are shown in Figure 1, were used to generate experimental data. Twenty-one data points were calculated for each model. A total of 50 model designs were made, and as a result, a dataset of 1050 lines was created. The material weight, length of wire, and process time values were recorded for all datasets. Ten sample datasets are given in Table 1. When the data are collected, ML algorithms are trained to estimate the best raster angle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Process Time** & **Weight Material** & **Length of Material** & **Infill Density** & **Raster Angle** \\ (**min**) & **(g)** & **Wire (m)** & **(\%)** & **(\%)** \\ \hline
95 & 25 & 8.25 & 30 & 45 \\
112 & 38 & 12.54 & 60 & 60 \\
150 & 52 & 17.16 & 90 & 15 \\
189 & 41 & 13.53 & 30 & 45 \\
147 & 55 & 13.53 & 30 & 90 \\
130 & 28 & 9.24 & 60 & 0 \\
133 & 32 & 10.56 & 90 & 30 \\
106 & 23 & 7.59 & 30 & 75 \\
175 & 45 & 14.85 & 60 & 15 \\
121 & 24 & 7.92 & 30 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis of variance for a one-factor experiment.  
Figure 1: Data specimen employed to train the ML model.  
Figure 2: Proposed structure for estimation of the raster angle.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 2 Methodology
### Classification Model for Exercise Intensity Assessment  
Appropriate exercise intensity is essential for achieving effective outcomes during daily physical activity. In this study, we developed an exercise intensity assessment model that integrates heart rate metrics based on target heart rate (THR) theory with heart rate variability (HRV) indicators to reflect autonomic nervous system activity. The model utilizes high-quality ECG signals collected by the CEMS. By simulating varying exercise intensities through different activity durations across users, we evaluated exercise intensity using temporal and frequency domain features extracted from the ECG signals, achieving accurate classification and assessment.
## 2 Materials and Methods
### Production Process  
For the preparation of the different types of mortar used in this research, the recommendations of the UNE-EN 196-1 standard [74] were followed. The same techniques and methods outlined in Figure 3 have always been used, using an IBERTEST planetary mixer model IB32-040V01 (Madrid, Spain).  
As can be seen in Figure 3, all the samples were cured under the same conditions prior to the tests (temperature 22 \(\pm\) 1 \({}^{\circ}\)C and ambient humidity 95 \(\pm\) 2%) and for a period of 28 days.  
Regarding dosages used for the preparation of the samples, Table 7 shows the mass proportions used for each type of mortar.  
As seen in Table 7, two proportions of cement/aggregate by mass (1:3 and 1:4) commonly used for manufacturing of mortars have been used. A percentage of natural fiber addition corresponding to 1.5% by weight of cement has been selected, based on the results obtained by other researchers and analyzed in the literature review.  
Additionally, with this added fiber proportion it was possible to obtain a plastic and workable consistency corresponding to a mortar diameter of 175 \(\pm\) 10 mm in the shaking table test, following the UNE-EN 1015-3 standard [75]. It should be noted that in no case  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Cement [g]** & **Sand [g]** & **Water [g]** & **Water/Cement Ratio** & \multicolumn{3}{c}{**Natural Fiber Reinforcement [g]**} \\  & & & & & **Abaca** & **Coconut** & **Touquilla Straw** \\ \hline NA-1:3 & 450.0 & 1350 & 234.0 & 0.52 & — & — & — \\ RCA–1:3 & 450.0 & 1350 & 328.5 & 0.73 & — & — & — \\ RCA–1:3–1.5\%(A) & 450.0 & 1350 & 328.5 & 0.73 & 6.75 & — & — \\ RCA–1:3–1.5\%(C) & 450.0 & 1350 & 328.5 & 0.73 & — & 6.75 & — \\ RCA–1:3–1.5\%(T) & 450.0 & 1350 & 328.5 & 0.73 & — & — & 6.75 \\ NA–1:4 & 337.5 & 1350 & 183.0 & 0.54 & — & — & — \\ RCA–1:4–1.5\%(A) & 337.5 & 1350 & 283.5 & 0.84 & 6.75 & — & — \\ RCA–1:4–1.5\%(C) & 337.5 & 1350 & 283.5 & 0.84 & — & 6.75 & — \\ RCA–1:4–1.5\%(T) & 337.5 & 1350 & 283.5 & 0.84 & — & — & 6.75 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mass dosages used for the manufacture of mortars.  
Figure 3: Manufacturing process diagram of the mortar samples used in this research.  
superplasticizers were used, to eliminate environmentally harmful chemical compounds and taking into consideration that the main application of these mortars would be as a non-structural lining or bonding material [14]. Consequently, the water/cement ratio had to be increased compared to mortars made with natural aggregate, since otherwise, as highlighted in the research by [PERSON] et al. there is a significant difficulty to obtain an adequate workability of the mixture [76]. Finally, it should be noted that two reference mixes were prepared with standardized sand for both cement/aggregate ratios used in this research.