esa-sceva/satcom-chunk-collection huggingface dataset:
# Diabetic Retinopathy Improved Detection Using Deep Learning
## 4 Experimental Results
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**DS** & **Class** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{APTOS} & No DR & **0.96** & **0.99** & **0.97** \\  & Slight NPDR & **0.73** & **0.49** & **0.59** \\  & Medium NPDR & **0.70** & **0.84** & **0.77** \\  & Severe NPDR & 0.40 & **0.26** & **0.31** \\  & PDR & **0.46** & **0.36** & **0.40** \\ \hline \multirow{4}{*}{Messidor} & No DR & 0.73 & 0.88 & 0.80 \\  & Slight NPDR & 0.43 & 0.11 & 0.18 \\ \cline{1-1}  & Medium NPDR & 0.44 & 0.54 & 0.48 \\ \cline{1-1}  & Severe NPDR & **0.60** & 0.20 & 0.30 \\ \cline{1-1}  & PDR & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Metrics obtained during validation. APTOS dataset proved to perform better during validation in all presented metrics. Bolder values indicate best performance for a specific class between both datasets. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**DS** & **Class** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{APTOSMess} & No DR & 0.60 & **1.00** & **0.75** \\  & Slight NPDR & 0.00 & 0.00 & 0.00 \\  & Medium NPDR & 0.18 & 0.02 & 0.04 \\  & Severe NPDR & 0.00 & 0.00 & 0.00 \\  & PDR & **0.25** & **0.09** & **0.13** \\ \hline \multirow{4}{*}{MessAPTOS} & No DR & **0.69** & 0.39 & 0.50 \\  & Slight NPDR & **0.07** & **0.03** & **0.04** \\ \cline{1-1}  & Medium NPDR & **0.22** & **0.36** & **0.27** \\ \cline{1-1}  & Severe NPDR & **0.15** & **0.65** & **0.24** \\ \cline{1-1}  & PDR & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metrics obtained during testing. DS columns indicate datasets used for training and testing, where Mess means Messidor. Bolder values indicate best performance for a specific class between both datasets. Figure 2: These ROC curves represent the feature acquisition level for the model using the cross-testing method. The MessAPTOS and APTOSMess lines specify the model trained over Messidor and tested against APTOS and vice versa, respectively. The Slight and Medium NPDR are the most challenging differentiable classes in terms of features. Nevertheless, the APTOS dataset comprises the most diverse images to optimize the parameters for the Medium NPDR class. The Severe NPDR and PDR classes were the most simple to differentiate using both datasets for training in terms of features. In general terms, the model trained over APTOS acquired better features for all the classes at a low false-positive rate. **(a)** The ROC curve for the No DR class. **(b)** The ROC curve for the Slight NPDR class. **(c)** The ROC curve for the Medium NPDR class. **(d)** The ROC curve for the Severe NPDR class. **(e)** The ROC curve for the PDR class. Figure 3: Dataset image samples of Messidor (**left**) and APTOS (**right**). Both datasets were used in this work as a cross-testing method. From top to bottom are sample for each class. First one is a healthy eye. In middle are nonproliferative diabetic retinopathy subcategories, and last presents a proliferative diabetic retinopathy sample.
## 3 Results  
The numerical model of the described MIFA was validated by laboratory tests of the undisturbed (in free space) reflection coefficient S11 parameter. The results of an experimental validation showed sufficient agreement of the obtained centre (resonance) frequency (RF module matching frequency),  
Figure 6: Exposure scenarios with the RF module located (**a**) on the headband; (**b**) attached to a helmet (the RF module is circled).  
with a difference of 1.3% between the measurement and simulations (2.400 GHz--measurements vs. 2.432 GHz--simulations) (Figure 7).  
In the cases with the MIFA close to the body (on the arm, forearm and at the head), it was experimentally observed that the dielectric parameters of the tissues had a significant influence on the S11 parameter (Figure 8). The resonance frequency of the considered antenna and the antenna performance varied significantly near the surface of the body. Our study showed that resonance frequency varied among 1.545 GHz, 1.595 GHz and 2.159 GHz for the MIFA located on the arm, forearm and at the head respectively. The reason for this is that the human body acts as a new layer of substrate with a complex structure and surface, as has also been observed in other studies [31; 40]. The results obtained show that locating the MIFA at a very short distance (a few millimetres) from the human body causes a high mismatch with the desired operating frequency and the antenna's performance.  
Figure 8: The S11 parameter, measured in the laboratory and determined using numerical simulations of the developed numerical model of the wireless communication module (RF module) used in IoT wearable devices located near various parts of the worker’s body.  
Figure 7: The S11 parameter, measured from the wireless communication module (the ESP32-WROOM-32 RF module) in the laboratory and determined using numerical simulations of the developed numerical model of the MIFA, used in the considered IoT wearable devices.  
Table 2 shows the results of the numerical simulations of SAR values related to EMF exposure near the ESP32-WROOM-32 RF module evaluated at an input power to the MIFA of 100 mW at a frequency of 2.45 GHz (analysed with respect to continuous exposure), i.e., evaluated at the worst-case exposure scenario with respect to the level of EMF emission from the considered RF module and the rules of evaluating thermal load from human exposure to EMF.  
The obtained results show SAR values in the case of the RF module located in the headband up to 10 times higher in comparison to its location on the helmet. Furthermore, in this exposure case, for an input power to the antenna over 50 mW, SAR10g values may exceed the limits for the general public (2 W/kg), while for an input power to the antenna over 250 mW, SAR10g values may also exceed the limits for occupational exposure (10 W/kg). It was observed in the results of numerical simulations that antenna performance drops dramatically when it is located very close to tissues (only 6.1 mW EIRP @ 100 mW of input power to antenna, compared to 350 mW when used on the helmet).
# GTMesh: A Highly Efficient C++ Template Library for Numerical Schemes on General Topology Meshes
## 4 The GTMesh Library
### Example of Use  
By using the concept of class traits, it is possible to generalize complex computational methods to be independent of the data structures utilized by the solved problem, which simplifies their development and debugging. We demonstrate an implementation of a generic version of the fourth-order Runge-Kutta-Merson (RKM) solver, with adaptive time stepping for numerical integration of ordinary differential equation (ODE) systems [38].  
Considering the ODE system in the form  
\[\dot{\mathbf{x}}=f(t,\mathbf{x}), \tag{17}\]  
where the \(\mathbf{x}\in\mathbb{R}^{N}\), \(N\in\mathbb{N}\) and \(f:\mathcal{J}\times\mathbb{R}^{N}\rightarrow\mathbb{R}^{N}\), the RKM solver works as presented in Algorithm 1:  
```
1\(\tau=\tau_{\text{test}}\); \(x^{\prime}=x^{\prime}_{\text{test}}\); \(t=T_{\text{test}}\);
2 while(\(|T_{\text{test}}-t|<|T_{\text{test}}-T|\)) {
3 if ( \(|T-t|<|\tau|\)) {
4\(\tau=T-t\);
5}
6\(\mathbf{K}_{1}=f(t,x^{\prime})\);
7\(\mathbf{K}_{2}=f(t+\frac{\tau}{5},x^{\prime}+\frac{\tau}{5}\mathbf{K}_{1})\);
8\(\mathbf{K}_{3}=f(t+\frac{\tau}{5},x^{\prime}+\frac{\tau}{5}(\mathbf{K}_{1}+\mathbf{K}_{2}))\);
9\(\mathbf{K}_{4}=f(t+\frac{\tau}{5},x^{\prime}+\frac{\tau}{5}(\mathbf{K}_{1}+\mathbf{3}\mathbf{K }_{3}))\);
10\(\mathbf{K}_{5}=f(t+\frac{\tau}{5},x^{\prime}+(\mathbf{K}_{1}-\frac{\tau}{5}\mathbf{K}_{3}+ 2\mathbf{K}_{4}))\);
11\(\mathbf{\epsilon}=\text{max\_element}\frac{\tau}{5}|0.2\mathbf{K}_{1}-0.9\mathbf{K}_{3}+0.8 \mathbf{K}_{4}-0.1\mathbf{K}_{5}|\)
12 if ( \(\mathbf{\epsilon}<\tau\)) {
13\(\mathbf{x}^{\prime}=x^{\prime}+\tau\left(\frac{1}{5}(\mathbf{K}_{1}+\mathbf{K}_{5})+\frac{ \tau}{5}\mathbf{K}_{4}\right)\);
14\(t=t+\tau\);
15 if (\(\mathbf{\epsilon}==0\)) continue;
16}
17\(\tau=(f/t)^{2}\cdot\omega\tau\);
18}
```  
**Algorithm 1** Pseudo-code of the Runge-Kutta-Merson ODE solver [38].  
For more information, see [38; 39]. This algorithm is utilized in the example application presented in Section 6 below. Finally, the implementation of the algorithm is illustrated in Listing 6.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(t\) & current time level \\ \(T\) & final time \\ \(T_{\text{ini}}\) & initial time \\ \(\tau\) & time step \\ \(\tau_{\text{ini}}\) & initial time step \\ \(x^{\text{T}}\) & numerical solution \\ \(\mathbf{x}^{\text{T}}_{\text{ini}}\) & initial condition for the numerical solution \(x^{\text{T}}\) \\ \(\delta\) & tolerance parameter \\ \(\omega\) & time step adjustment parameter (\(\omega=0.8\) is recommended) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols used in the Runge-Kutta-Merson pseudo-code (Algorithm 1).  
to avoid race conditions (Section 6.3); and OpenMP utilizing auxiliary computational data values to avoid race conditions (Section 4.2).
# Image Error Concealment Based on Deep Neural Network
## 4 Experiments  
In this section, comparative experiments verify the performance of the proposed algorithm. We firstly analyzed the influence of the proposed correction and adaptive scan order to the final EC performance. Then, we compared the proposed method with other state-of-the-art EC methods [4; 6; 7; 9; 10; 27; 31; 32] to evaluate our method.  
Similar to the work of others, three kinds of block loss were considered in our experiments: a \(16\times 16\) regular isolate block losses (\(\approx 22\%\) loss rate), \(16\times 16\) regular consecutive block losses (\(\approx 50\%\) loss rate), and \(16\times 16\) random consecutive block losses (\(\approx 25\%\) loss rate). This three-loss mode is shown in Figure 11.  
For convincing comparisons, 13 widely used images were used as test sets in this paper. Note that we only considered grayscale images; since the color images contain multi-channels, we could reconstruct the color images by reconstructing the channels separately. All of the test images were \(256\times 256\). in size, as illustrated in Figure 12.  
Figure 11: Typical block loss mode. (**a**) Isolate block loss. (**b**) Consecutive block loss. (**c**) Random block loss.  
Figure 12: The 13 test images used in our experiments. From left to right and top to bottom: Baboon, Barbara, Boat, Butterfly, Columbia, Cornfield, Couple, Goldhill, Hat, Man, Peppers, Tower.  
In this paper, the size of the designed templates was set to \(7\times 7+1\) (that is a combination of a \(7\times 7\) square and one pixel to be predicted). The training samples were collected through matching the templates on the test images. Each sample was normalized to the range of [0,1] to serve the active range of 'tanh'. The two parts of the AE-P network were both 11-layer fully connected networks. We used the 'elu' activation function after each layer except for the last layer, and we used the 'tanh' activation function after the last layer in order to normalize the output to [0,255], which is the grayscale range. The overview of the AE-P network can be seen in Table 1. We used Adam [33] for optimization with the learning rate of the two networks set to 0.001. The batch size was set to 600. In addition, our work is implemented on the Python-Tensorflow platform on Windows 10. The hardware platform used Intel i5 7300H CPU, 8 GB of RAM, and NVIDIA GTX 1060 GPU.  
In order to compare the quality of reconstruction, the widely used measurement peak signal-to-noise ratio (PSNR) is chosen serve as an objective metric to measure the image quality in our experiments. For a better comparison of structural similarities, the structural similarity (SSIM) index [33] is also used in this paper.
## 4 Optimization Process
### Fitness Functions  
A fitness function is needed in the optimization in order to convert the contour error from a vector into a singular arithmetic value that can be used for optimization purposes. In this paper, three different fitness functions are used. Two of them, ISE and IAE, are existing and widely used fitness functions in controller tuning via optimization. The third one, MSMAE, was introduced by the authors in order to investigate its practicality as an alternative to the existing fitness functions.  
\[IAE = \int_{0}^{\infty}|e_{c}|dt \tag{17}\] \[ISE = \int_{0}^{\infty}{e_{c}}^{2}\ dt\] (18) \[MSMAE = \overline{|e_{c}|}+\sigma(|e_{c}|)+\max(|e_{c}|) \tag{19}\]  
where \(e_{c}\) is the contour error, \(\overline{|e_{c}|}\), \(\sigma(|e_{c}|)\), and \(\max(|e_{c}|)\) are the mean, standard derivation, and maximum value of the absolute contour error that will be defined in the next section. The rationale to propose MSMAE is to consider the statistics of the contour error distributions.
## 7 Conclusions  
To address the limitations of storage space and computational resources in space environments, this paper proposes a lightweight satellite component detection algorithm called LTSCD-YOLO. The backbone network of this algorithm is based on EfficientNet_B0; it introduces a cross-scale feature fusion module in the Neck part and upgrades the C2 fmodule to the F-C2f module. The Head part employs RepHead, and the loss function uses Focal-EloU. By adopting an efficient network structure design and advanced model optimization techniques, this algorithm significantly reduces the number of model parameters and computational load while ensuring the reliability and accuracy of detection performance. To further enhance the model's training effectiveness, we systematically collected, organized, precisely annotated, and preprocessed a large amount of satellite imagery data to construct a comprehensive and diverse Typical Satellite Components Dataset (TSC-Dataset). The dataset was expanded to 16,400 images using data augmentation techniques, providing strong support for future research in this field. Experimental results on the TSC-Dataset show that, compared to YOLOv8s, LTSCD-YOLO improves the mean Average Precision (mAP50) by 1.50%, reduces the number of model parameters by 78.46%, decreases computational load by 65.97%, and increases detection speed by 17.66%. The algorithm's generalization was also verified on real satellite images. These results demonstrate that the LTSCD-YOLO model can quickly and accurately identify satellite components, validating the effectiveness of the algorithm.  
The above results indicate that LTSCD-YOLO can provide reliable technical support for satellite component detection tasks. It has broad application prospects in the field of remote sensing and is expected to promote further development in this area.  
Conceptualization, Z.T. and W.Z.; methodology, Z.T. and J.L.; software, Z.T. and R.L.; validation, Z.T. and S.C.; formal analysis, Z.T. and Z.F.; investigation, Z.T. and R.L.; resources, W.Z. and J.L.; data curation, Z.T. and R.L.; writing--original draft preparation, Z.T. and Y.X.; writing--review and editing, Z.T. and Y.X.; visualization, Z.T. and F.Z.; supervision, W.Z. and J.L.; project administration, J.L.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Key R&D Program of China under Grant No. 2022 YFE0204600, and the Fundamental Research Project of SIA under Grant No. 2022 JC3K03.  
The data of the experimental images used to support the findings of this research are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy restrictions.  
The authors would like to thank the anonymous reviewers and members of the editorial team for their comments and contributions.  
The authors declare no conflicts of interest.
# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 3 Overview of the Study Area and Data Processing  
### Research Area Introduction  
For the experimental analysis in this study, data from five geomagnetic stations in the European region--BEL, ESK, FUR, HAD, and HLP--between 10 November and 27 December 2018 were sourced from the INTERMAGNET website. The daily variation data from these selected stations were utilized. The geographical distribution of these stations is illustrated in Figure 4.  
The impact of the station network structure on the prediction of the SVM-RF algorithm was further analyzed based on Figure 4, where it is evident that the station distribution is relatively dispersed. This dispersion enables the construction of diverse station network structures according to the geographical locations of multiple stations. Additionally, Table 1 presents the basic information of each station along with the inter-station distances for reference.  
The data in Table 1 show that the altitude of each station is relatively low, ranging from 335 km to 3090 km. Additionally, the maximum longitude difference among stations is 25.273\({}^{\circ}\), and the maximum latitude difference is 7.149\({}^{\circ}\).
# Estimating and Analyzing Long-Term Multi-GNSS Inter-System Bias Based on Uncombined PPP
## 3 Experimental Data and Processing Strategies
### Positioning Bias  
Figure 7 shows that the coordinate bias of each station of the four-system model on the 150 th day of 2018. The positioning biases in the E, N and U directions of all stations were at millimeter level, among which the positioning biases in the three directions of the Ihaz station were all less than 1 mm. To further analyze the positioning of the uncombined PPP model, Figure 8 shows the coordinate bias statistics of all stations for 28 days, where Figure 8 denote the E, N and U directions, respectively, wherein the y axis indicates positioning bias in millimeters, and the ordinate indicates the number of stations. As can be seen, the RMS (Root Mean Square) of the positioning bias in the E, N and U directions were 4.6 mm, 3.4 mm and 8.5 mm, respectively.  
Therefore, we can conclude that more available satellites and better satellite constellation distribution (low DOP) can effectively improve the accuracy of PPP location solution. Moreover, a smaller RMS means a higher positioning accuracy and a more centralized positioning deviation distribution. Due to the correlation in PPP parameter estimation, the ultra-high precision position solution can better ensure the high accuracy and reliability of ISB parameter solution, which can better support the reliability and accuracy of the conclusion in the paper.  
Figure 6: DOP values of single GPS and G + C + R + E systems.  
Figure 7: PPP results of day 150.
## 3 Analysis and Design of DICAUVACOOP Software
### Grape Quality Evaluation
#### 3.2.5 Calculation of Grape Quality  
The grape quality calculation process commences with the selection of a specific campaign. For every grape variety participating in the chosen campaign, a designated quality criterion is assigned (the user will only be able to choose criteria according to the quality group and varieties specified in it). Within each quality criterion, the grape analysis of the respective entry is scrutinized for adherence to predefined ranges corresponding to each parameter, resulting in the determination of the initial score. This score is further adjusted based on the weight assigned to both the parameter and its encompassing group, yielding the final weighted score for each parameter. The summation of individual scores across all parameters culminates in the comprehensive quality score for the given grape entry. The formula for calculating the group score (GroupScore\({}_{\text{x}}\)) is expressed as follows:  
\[GroupScore_{x}=Gw_{x}\times(Score_{x_{1}}\times Weight_{x_{1}}+\ \ldots\ +\ Score_{x_{n}}\times Weight_{x_{n}}) \tag{1}\]  
Here, Gw\({}_{\text{x}}\), Score\({}_{\text{x}}\), and Weight\({}_{\text{x}}\) are assigned by the cooperative, with Gw\({}_{\text{x}}\) representing the group weight, Score\({}_{\text{x}}\) being the score resulting from the assigned minimum and maximum values within the defined range for the i-th parameter, and Weight\({}_{\text{x}}\) being the weight of the i-th parameter. This formulation captures the significance of individual parameters and the overall contribution of groups in the final calculation while allowing cooperatives to leverage their viticultural expertise to reflect regional priorities and market demands.  
To determine the final quality score, the group scores for maturity and health are combined, as expressed in the following formula:  
\[FinalScore\ =\ GroupScore_{Maturity}+GroupScore_{Health} \tag{2}\]  
where  
\[GroupScore_{Maturity}=Gw_{Maturity}\times\big{(}S_{M_{1}}\times W_{M_{1}}+ \cdots+S_{M_{x}}\times W_{M_{x}}\big{)} \tag{3}\]  
\[GroupScore_{Health}=Gw_{Health}\times\big{(}S_{H_{1}}\times W_{H_{1}}+\cdots+S_ {H_{x}}\times W_{H_{x}}\big{)} \tag{4}\]  
This approach ensures that both critical factors, maturity and health, are weighted according to their respective contributions, resulting in a comprehensive and balanced evaluation of grape quality.  
To summarize this calculation, Figure 5 shows how the quality is calculated for each parameter.  
This example illustrates a real-world application of the calculations used by our stakeholders and the database structure. Input tickets store raw data, while another table defines parameter scoring ranges. Individual parameter scores are then weighted and combined to calculate the group scores for maturity and health, which are summed to determine the final quality score.  
\[GroupScore_{Maturity}=\ 1\times(7\times 0.7+7.5\times 0.15+9\times 15)=9.475\]  
\[GroupScoreHealth=\ -0.2\times(0.5\times 1)=-0.1\]  
\[FinalScore\ =\ 9.475-0.1=9.375\]
# A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals
## 4 Solution Methodology
### Prioritized Search
#### 4.2.3 Scalability and Parallelization  
The prioritized search algorithm, while reducing the solution space, can increase computational costs due to the need to form trees and perform computationally expensive operations, such as those in Algorithm 1, for each vessel individually. This computational cost grows with the number of vessels and berths, making single priority evaluations significantly more expensive. This issue is partially addressed by reducing the redundancy of examined branches (Algorithm 3, Lines 25-26), where only the top \(k\) priority vectors per branch are retained. In our experiments, we used \(k=10\) to balance performance with computational efficiency. Scalability concerns related to increased vessel and berth numbers are further addressed through the evaluation in Section 5.2.  
Another way to speed up computation, unlike MOEAs, which rely on iterative learning across generations, is through the parallelizable nature of Algorithm 4, which offers a practical advantage in large-scale problem settings. We propose a simple parallelization approach where multiple processes start from different priority strategies close to FCFSand independently follow the steps outlined in the previous section related to the search algorithm. This allows multiple processes to concurrently evaluate different priorities, significantly increasing the search capacity of the algorithm. However, without guidance some processes may get stuck in suboptimal solutions while others progress more effectively. To address this, we ensure that the best current observation from any individual process, with respect to a specific agent, is used to update the ledger (as defined in Algorithm 4) across all processes. The ledger helps guide processes stuck in poor regions of the solution space back toward the current optimum by recording and exploiting promising solutions. Since the ledger serves as the minimal form of communication across processes and can be updated asynchronously, the communication overhead remains negligible. The search process terminates once all processes complete their final iterations, ensuring that the prioritized search strategy maintains a comparable number of iterations across processes to MOEAs, even as the number of vessels or berths increases.