esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Summary of the Literature Review  
The literature review (see Sections 2.1-2.8) highlights significant advancements in photopolymer resin systems for 3D printing, focusing on material innovation, process-induced defects, environmental effects, and standardization. Hybrid and reinforced resins, such as ceramic-filled and nanoparticle-enhanced formulations, have demonstrated improved mechanical properties, particularly in dental and structural applications [13; 14; 15]. Biocompatible and biodegradable resins address both environmental and medical needs, with studies showing reduced cytotoxicity and enhanced durability [18; 19; 22]. Smart resins, including self-healing and conductive variants, enable advanced applications in electronics and adaptive structures [24; 25; 26].  
Process-related challenges, such as curing shrinkage, microvoid formation, and residual stress, significantly impact part performance. Research by [PERSON] et al. [16; 30] and [PERSON] et al. [29] explores mitigation strategies, while micro-CT and SEM analyses provide insights into microstructural defects [27; 31; 32]. Environmental factors, such as hygroscopic behavior and UV degradation, further impact material longevity, necessitating the development of UV-stabilized and moisture-resistant formulations [28; 33; 44]. Standardizationefforts, as outlined in the ASTM and ISO guidelines, ensure reliability in aerospace and medical applications [29; 32; 34].  
Advancements in in situ monitoring, such as optical imaging and closed-loop control systems, enhance print quality and precision [63; 70; 72]. Multi-scale modeling and topology optimization enhance mechanical efficiency and lightweight design [36; 57; 62]. Certification frameworks and mechanical testing protocols further validate resin performance for industrial adoption [14; 28; 54]. Collectively, these developments underscore the potential of photopolymer resins in additive manufacturing, while addressing key challenges in material science and process engineering.
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 2 The Motivation
### Multicast Delivery Mechanism and Its Weaknesses
Public key cryptography is used to solve several issues mentioned above. It provides identity (through digital certification), authentication and admission control. A Public-key Infrastructure (PKI)consists of a set of rules, policies, and procedures on how to create and distribute public-keys in a network. Key-management is an important component of a PKI. In a PKI, a Certificate Authority (CA) plays a major role. A PKI may not function without a CA. There must be secure communication channel established between every host in the network and the CA in order to obtain public keys. Man-in-the-middle attack is possible without this secure channel. In a PKI, CA is a single point of failure and is frequently targeted by attackers from both inside and outside networks.
## 5 Corrections for Off-Reference Conditions
### Correction of Maximum Thickness-Chord Ratio  
The correction of the maximum thickness-chord ratio is more complicated than that of the orientation parameter because of the coupled effects of the maximum thickness and the chord of SVs on the blade passage throat. In view of this fact, it is believed that the CR and the maximum thickness-chord ratio are both supposed to be elementary factors in the correction. Therefore, a series of CFD calculations with CR from 0.45 to 0.85 and maximum thickness-chord ratio from 0.04 to 0.12, which are typical values for the design of blades with SVs, were carried out for the correction of the maximum thickness-chord ratio.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Ranges** \\ \hline inlet metal angle, \(\kappa_{1}\) (\({}^{\circ}\)) & 43, 53, 73 \\ camber angle, \(\phi_{\text{pb}}\) (\({}^{\circ}\)) & 30, 50 \\ equivalent solidity, \(\sigma_{\text{eq}}\) & 1.4, 1.8 \\ chord ratio(CR) & 0.45, 0.65, 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter ranges for the correction of orientation parameter.  
Figure 14: Comparisons between the results obtained from basic and modified correlations of (**a**) incidence and (**b**) deviation angles for orientation parameter correction.  
Figure 13: Averaged modifications for (**a**) incidence and (**b**) deviation angle correlations.  
Figure 15 gives the correction curves as a function of the maximum thickness-chord ratio and the CR, linearly fitted using the CFD calculation results. The correction curves match the scatter CFD results well, with a slight error of less than 0.5\({}^{\circ}\).  
Figure 16 shows the comparisons between CFD results and the results obtained by using the modified basic correlations combined with the corrections of both orientation parameter and maximum thickness-chord ratio, where basic correlations without the corrections are also presented for evaluating the effect of the improvement of the corrections. It can be seen that the correlations with corrections perform better to match the CFD results compared to the basic correlations, especially for the incidence angles of CR = 0.45 and the deviation angles of CR = 0.85. Moreover, the correlations coupled with these geometry corrections are able to achieve good agreement with the CFD results within the error band of \(\pm\)1.0\({}^{\circ}\) for both incidence and deviation angles.  
Figure 16: Comparisons between CFD results and modified basic correlations for (**a**) incidence and (**b**) deviation angles.  
Figure 15: Correction curves of (**a**) incidence and (**b**) deviation angle correlations as a function of the maximum thickness-chord ratio and the CR.
# Development Status of Key Technologies for Optoelectronic Integrated Circuit Manufacturing
## 3 Manufacturing of Optoelectronic Integrated Circuits
### Optical Interconnect
The chip-level optical interconnection technology of optical waveguides is typically focused on the improvement of the waveguide and the improvement of the fabrication process.
## 5 Evaluation Protocol and Results
### Ablation Study
#### 5.2.3 Trial Length and Stride  
The length of each raw signal in the dataset was 60 s, with a sampling rate of 128 Hz. For data augmentation, each signal was divided into patterns using a fixed temporal window.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Optimizer** & **Avg-Accuracy** & **Avg-Sensitivity** & **Avg-Specificity** \\ \hline RMSProb & 97.62\% \(\pm\) 3.12 & 98.67\% \(\pm\) 2.17 & 96.41\% \(\pm\) 3.48 \\ \hline SGD & 73.69\% \(\pm\) 7.61 & 71.19\% \(\pm\) 9.80 & 67.44\% \(\pm\) 11.86 \\ \hline Adam & 99.88\% \(\pm\) 0.13 & 100\% \(\pm\) 0 & 99.74\% \(\pm\) 0.768 \\ \hline \end{tabular}
\end{table}
Table 4: Results of different optimizers with the selected model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Channels** & **Avg-Accuracy** & **Avg-Sensitivity** & **Avg-Specificity** \\ \hline Posterior (P3, P4, Pz) & 73.09\% & 74\% & 72.05\% \\ \hline Central (C3, C4, Cz) & 65.12\% & 66.89\% & 63.08\% \\ \hline Frontal (F3, F4, F7, F8) & 74.52\% & 77.55\% & 71.02\% \\ \hline
**Temporal (T3, T4, T5, T6)** & 80.59\% & 83.77\% & 76.92\% \\ \hline Occipital (O1, O2) & 78.92\% & 82.44\% & 74.87\% \\ \hline Temporal + Frontal & 94.64\% & 95.11\% & 94.10\% \\ \hline Temporal + Posterior & 83.93\% & 87.33\% & 80\% \\ \hline Temporal + Occipital & 92.38\% & 94.44\% & 90\% \\ \hline Temporal + Central & 80.12\% & 81.56\% & 78.46\% \\ \hline Right side & 88.81\% & 91.56\% & 85.64\% \\ \hline Left side & 75.24\% & 78.89\% & 71.03\% \\ \hline \end{tabular}
\end{table}
Table 5: Results of testing different brain regions with the best model.  
To examine the best length of the trials, we tested different choices: 1 s, 2 s, and 3 s. We also tested two choices for stride: 0.125 s and 0.25 s. Table 6 shows the results of examining different choices of trial length and stride on the best model using 10-fold cross-validation.  
As shown in Table 6, the scores are improved by increasing both the pattern length and stride. The trials of 3 s length and 0.125 s stride gave the best result, so it was chosen to train the proposed model.
## 3 Design of IDPGMFO
### Adaptive Nonlinear Decreasing Strategy for Weight Coefficient  
The weight coefficient is a significant parameter for MFO. The moth position update formula with the weight coefficient is detailed as follows.  
\[\left\{\begin{aligned} M_{i}&=S(M_{i},F_{j})=D_{ij}e^{ \tau l}\cos(2\pi l)+\omega F_{j}\\ D_{ij}&=|M_{i}i-F_{j}|\end{aligned}\right. \tag{19}\]  
where \(\omega\) is the inertia weight.  
An effective decreasing strategy for the weight coefficient should be proposed, so as to make the algorithm search range smaller, which will make the optimization ability for the whole optimization process more obvious [19]. The whole optimization process for MFO is nonlinear, and an adaptive nonlinear decreasing strategy for the weight coefficient should be adopted, so as to really reflect the real situation effectively. This paper presents a nonlinear decreasing strategy for weight coefficient \(\omega\) with strong plasticity based on the cosine form decreasing. The specific calculation formula of weight coefficient \(\omega\) is as follows.  
\[\omega=\omega_{\min}+\omega_{d}\cdot\cos\left((1-t_{r})\cdot\frac{\pi}{2} \right)^{\beta} \tag{20}\]  
where \(\omega_{d}\) is the decreasing amount of the weight coefficient, \(\omega_{d}=\omega_{\max}-\omega_{\min}\), \(\omega_{\min}\) and \(\omega_{\max}\) are the minimum and maximum value for the weight coefficient, respectively; \(t_{r}\) is the iteration progress, \(t_{r}=t\cdot t_{\max}{}^{-1}\); \(\beta\) is the optimization factor [20].  
The weight coefficient is nonlinearly decreasing, and the weight coefficient will continue nonlinearly decreasing with the evolution generation throughout the whole iterative calculation process. The relation curves of specific weight coefficients and evolution generations are shown in Figure 2.  
As can be seen from Figure 2, the above nonlinear decline strategy in the form of cosine can optimize and adjust the nonlinear decline trend by selecting the most suitable optimization factor \(\beta\). This strategy is characterized by strong plasticity and strong adjustment ability. This strategy can adjust the decline rate of the weight coefficient in real time during the iterative process, which is conducive to the improvement of the balance effect between global exploration and local development, so as to improve the global optimization ability of the algorithm. In addition, the decline speed related to the weight coefficient in the final stage [0.8, 1] of the optimization process is very slow in this paper, so as to retain the global exploration ability to a certain extent.  
Figure 2: The relationship between weight coefficient and evolutionary algebra.
# Portfolio Learning Based on Deep Learning
## 3 Method
### Construction Portfolio and Backtesting  
In this section, we first introduce how to construct a portfolio every ten days based on clustering structure. We then introduce several important indicators in backtesting to evaluate the whole strategy. Finally, the complete backtesting process is introduced.  
Sharpe ratio: Sharpe ratio is the most important index to evaluate the quality of an asset. It is defined as follows:  
\[\text{Sharpe ratio}=\frac{\mathbb{E}(R_{p})-R_{f}}{\sigma_{p}} \tag{9}\]  
where \(\mathbb{E}(R_{p})\) and \(R_{f}\) are the portfolio's expected return and risk-free rate, respectively, and \(\sigma_{p}\) is the portfolio's standard deviation. The Sharpe ratio is an indicator used to measure the returns that can be obtained by measuring unit risk. The Sharpe ratio implies a trade-off between return and anti-risk ability.  
Construction Portfolio: Let \(r_{k}^{*}\) be the asset with the highest Sharpe ratio in the \(k\)-th cluster; we construct the portfolio by choosing \(r_{k}^{*}\) in each cluster:  
\[r_{k}^{*}=\arg\max_{p}\frac{\mathbb{E}(R_{p})-R_{f}}{\sigma_{p}},p\in c_{k},k =1,\cdots,K \tag{10}\]  
Total Return: The total return \(r_{t}\) is defined as follows:  
\[r_{t}=(V_{f}-V_{i})/V_{i} \tag{11}\]  
where \(V_{i}\) and \(V_{f}\) are the initial and final values, respectively. For example, when calculating daily return, \(V_{i}\) and \(V_{f}\) represent yesterday's closing price and today's closing price, respectively.  
Maximum Drawdown: Maximum drawdown (MDD) is defined as the peak-to-trough decline of an investment during a specific period:  
\[\text{MDD}=\frac{V_{p}-V_{t}}{V_{p}} \tag{12}\]where \(V_{t}\) and \(V_{p}\) represent the lowest value and the highest value, respectively.  
Backtesting: We first extract the high-level features of a candlestick chart of 20 days. The K-means algorithm is then applied to obtain stock market segmentation. We select the asset with the largest Sharpe ratio in each cluster to form a portfolio. This portfolio will be held for one period, which is defined as 10 days in our experiment. After 10 days, we sell this portfolio and obtain the return of this period. The whole process is shown in Figure 3. The total return of the 10 days is the sum of the every stock return in the portfolio.  
Asset Allocation: Each stock in the portfolio obtained through our model is allocated \(1/K\) of the total investment.
# Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography
## 3 Results and Discussion
### Comparing State-of-the-Art Windows with Tukey and Gaussian Windows
Results for inclusions as deep as 3 cm are very similar to the deconvolution approach, but for inclusion deeper or equal to 3.5 cm the localisation accuracy is lost, see Figure 17b. Moreover, the contrast is lower than previous reconstructions; although, slight differences are seen between Mellin-Laplace and Tukey windows even using cross-Born equation, see Figure 17c. The relative recovered volume increases considerably for both methods (Figure 17d. These results show that when the cross-Born approach is used, some information in depth is lost for inclusions deeper than 3 cm and to take full advantage of not overlapping datatypes (e.g., Tukey or Gaussian windows), experimental data must be deconvoluted before performing a reconstruction. From a different point of view, this theoretical analysis can also be viewed as an explanation of full time-resolved tomography limits. That is, Tukey windows-based reconstruction can be seen as an intermediate step between correlated windows-based reconstruction and full time-resolved reconstruction (i.e., reconstruction based on using each time bin as a datatype). In fact, in the last years several labs [32; 33], have developed photon propagation models for Graphics Processing Units (GPU). One of the goals of using GPU is to compute the full DTOF curve and to fit it entirely as described in Equation (3). To fit the entire DTOF curve is the limiting case of very narrow Tukey windows; if windows are shrinked to time bin size it will converge to DTOF curve fitting. Therefore, some of the questions that arise from this theoretical work is, how much benefit can be obtained by using full time-resolved reconstructions? Does the computational cost of computing the full curve outweighs the gain of some information? Moreover, since the computation of decorrelated windows, such as Tukey or Gaussian, are easily parallelizable and adaptable to GPU hardware, the authors believe that this computation method is an efficient substitute to full time-resolved reconstructions. Figure 16: (**a**) Number of iterations until convergence for Mellin–Laplace and Gaussian windows. (**b**) Seconds per iteration for Mellin–Laplace windows, novel approach (with Gaussian) and full–time resolved approach.
## 4 Experimental Design
### Datasets  
To evaluate the performance of the method proposed in this work, we used five real high-dimensional datasets: (1) Cardio; (2) Creditcard; (3) Satellite; (4) MUSK; (5) Arrhythmia.  
The Creditcard fraud dataset comes from Kaggle, and the other four datasets come from the ODDS library ([[http://odds.cs.stonybrook.edu/](http://odds.cs.stonybrook.edu/)]([http://odds.cs.stonybrook.edu/](http://odds.cs.stonybrook.edu/)) accessed date: 20 October 2021). The detailed information about the datasets is presented as follows. Some key statistics of the datasets are also given in Table 1.  
Cardio: The Cardio dataset is a 21-dimensional measurement dataset containing fetal heart rate signature (FHR) and maternal uterine contraction signature (UC) in the medical field ECG, which has been labeled by obstetric experts. Among them, the pathological class is used as the abnormal class of the dataset, with 176 data points. Except for the suspicious data class, which is removed, other data classes in the dataset are used as normal samples for training.  
Creditcard: Kaggle provided two-day credit card transaction information for European cardholders in September 2013. The data set contains 32 attributes, of which 492 out of 284,807 transactions were stolen. The data set is very unbalanced, with outliers accounting for 0.172% of all transaction data.  
Satellite: The Satellite dataset is an Earth resource satellite dataset containing 36 attributes. Since the data proportions of the three categories 2, 3, and 4 are the smallest,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **N** & **Dim** & **Outliers (\%)** \\ \hline Cardio & 1831 & 21 & 176 (9.6\%) \\ Creditcard & 284,807 & 32 & 492 (0.17\%) \\ Satellite & 5100 & 36 & 77 (1.5\%) \\ Arrhythmia & 452 & 279 & 66 (15\%) \\ MUSK & 3062 & 166 & 97 (3.2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset information.  
they can be combined into anomalous categories, and other categories are used as normal samples for training.  
Arrhythmia: The dataset is a multi-class classification dataset with a dimension of 279. The smallest categories, namely 3, 4, 5, 7, 8, 9, 14, 15, are merged to form an outlier category, and the remaining categories are merged to form a normal category.  
MUSK: There are 166 dimensions in the Musk dataset, and there are both Musk datasets and non-Musk datasets. Inliers consist of the non-musk classes j146, j147, and j252, while outliers consist of the musk classes 213 and 211.  
After we preprocessed the data, there were no missing values in all the datasets, and all the datasets were scaled to [0, 1].
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 4 Proposed Method
### Mobility-Enhanced Differential Privacy
The major updates are summarized below:  
```
1:procedureTraining(\(m\), \(\gamma\), \(\eta_{c}\), \(\eta_{s}\), \(\eta_{C}\), \(z_{\Delta}\), \(\sigma_{b}\), \(\beta\))
2:for each round \(t\) = 0, 1, 2,... do
3:\(\mathcal{Q}^{t}\leftarrow\) a set of vehicles sampled in round \(t\)
4:for each vehicle \(i\in\mathcal{Q}^{t}\)do
5:\((\Delta_{i}^{t},b_{i}^{t})\leftarrow\textsc{FedAvg}(i,\theta^{t},\eta_{C},C^{t})\)
6:endfor
7:\(\sigma_{\Delta}\gets z_{\Delta}C^{t}\)
8:\(\tilde{\Delta}^{t}=\frac{1}{m}(\sum_{i\in Q^{t}}\Delta_{i}^{t}+\mathcal{N}( 0,I\sigma_{\Delta}^{2}))\)
9:\(\tilde{\Delta}^{t}=\beta\tilde{\Delta}^{t-1}+(1-\beta)\tilde{\Delta}^{t}\)
10:\(\theta^{t+1}\leftarrow\theta^{t}+\eta_{s}\tilde{\Delta}^{t}\)
11:\(\tilde{b}^{t}=\frac{1}{m}(\sum_{i\in Q^{t}}b_{i}^{t}+\mathcal{N}(O,\sigma_{b}^ {2}))\)
12:\(C^{t+1}\gets C^{t}\cdot exp(-\eta_{C}(\tilde{b}^{t}-\gamma))\)
13:endfor
14:endprocedure
15:procedureFedAvg(\(i,\theta^{0},\eta\), \(C\))
16:\(\theta\leftarrow\theta^{0}\)
17:\(\mathcal{S}\leftarrow\) vehicle \(i\)'s local data split into batches
18:for batch \(s\in\mathcal{S}\)do
19:\(\theta\leftarrow\theta-\eta\
abla\mathcal{L}(\theta;s)\)
20:endfor
21:\(\Delta\gets\theta-\theta^{0}\)
22:\(b\leftarrow\mathbb{I}_{||\Delta||\leq C}\)
23:\(\Delta^{\prime}\leftarrow\Delta\cdot min(1,\frac{C}{||\Delta||})\)
24:return\((\Delta^{\prime},b)\)
25:endprocedure
```  
**Algorithm 1** Mobility-based differentially private federated learning with adaptive clipping for Internet of Vehicles* The model update values of vehicles participating in the training are clipped before transmission to the federated learning coordinator to limit the individual effects of each vehicle. * To obscure the effects of individual vehicles, it adds sufficient noise values to the total values updated before using the combined averaging algorithm. * To determine the value to be clipped, adaptive clipping is applied and Gaussian is used for noise.
## 3 The Establishment and Analysis of BBD Experimental Design and Prediction Model
### Establishment and Analysis of Grey Relational Degree Prediction Model
At the same friction coefficient, the grey relational degree response exhibits a decrease followed by an increase as the pressing speed rises, and the friction factor has an inconspicuous impact on the GRD response value. From Figure 6c, it is clear that with a smaller pressing speed and higher temperature, the GRD value is larger. At the same speed, the GRD response value continuously increases with temperature. Model evaluation and verification are important components of modeling. Through regression analysis, this study uses R-squared (R\({}^{2}\)) and the root mean squared error (RMSE) as  
Figure 6: Response surface plots: (**a**) response surface diagram illustrating friction coefficient and bullet temperature on GRD; (**b**) response surface diagram illustrating friction coefficient and downward pressing speed on GRD; (**c**) response surface plot of downward pressing speed and bullet temperature on GRD. evaluation indicators to test the reliability of the GRD model. The mathematical expressions are as follows:  
\[R^{2}=\frac{\sum\limits_{i=1}^{m}{(\hat{y}_{i}-\overline{y})}^{2}}{\sum\limits_{ i=1}^{m}{(y_{i}-\overline{y})}^{2}} \tag{18}\]  
\[RMSE=\sqrt{\frac{1}{m}{\sum\limits_{i=1}^{m}{(y_{i}-\hat{y}_{i})}^{2}}} \tag{19}\]  
where \(m\) is the number of the test samples, \(y_{i}\) denotes the response value corresponding to the i-th group, \(\hat{y}_{i}\) represents the estimated value for the i-th group, and \(\overline{y}\) represents the averaged experimental response value. R\({}^{2}\) serves as a measure of the model's fit to the data, where a value nearing 1 implies a high degree of fit. The RMSE represents the average root of the square error between predicted and actual values; a lower RMSE value indicates a model with reduced error [34]. The results in Table 7 show that R\({}^{2}\) is 97.98% and the RMSE is 94.35%, both of which are close to 1. These figures fully demonstrate that the GRA model's fitting accuracy is within acceptable limits and can be used as an approximate model for GRD prediction. To facilitate the observation of the model's prediction results, the correlation between GRD model predictions and experimental data is illustrated through a scatter plot. As shown in Figure 7, the data points are uniformly dispersed on both sides of the line. Prediction and simulation values are in good agreement with each other, further indicating that the grey relational grade model has a good fit. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DOF** & **Sum of Squares** & **Mean Squares** & **f-Value** & \(\boldsymbol{p}\)**-Value** \\ \hline Model & 9 & 0.2274 & 0.0253 & 26.98 & 0.0010 \\ Residual & 5 & 0.0047 & 0.0009 & & \\ Total & 14 & 0.2321 & R\({}^{2}\) = 97.98\% & & R\({}^{2}\)\({}_{\text{adj}}\) = 94.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The GRD model variance analysis results. Figure 7: Comparison of predicted and experimental values of GRD.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 2 Data Sources and Preprocessing
### Data Sources
#### 2.1.3 SAR Imagery  
SAR images provide geographic information on sea ice with high spatial resolution and will be used to verify the results of the sea ice edges in localized areas [29; 30]. The polar view website (www.polarview.aq, accessed on 10 March 2023) provides full-resolution SAR image data observed by Sentinel-1 and RADARSAT-2 over the previous 30 days. In addition, the SAR images are used as a polar stereographic projection, which is consistent with the projection of sea ice maps. The Sentinel-1 SAR images with 5 m resolution will be used to verify the boundary of open seawater and ice.