esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spaceflight Environment and Ocular Risks
### Radiation Exposure and Corneal Damage  
Galactic cosmic rays (GCR) and solar energetic particles (SEP) pose significant threats to ocular health during deep-space missions. These high-energy particles penetrate biological tissues, causing DNA damage, oxidative stress, and apoptosis in corneal and retinal cells. Such damage can lead to radiation keratitis and stromal scarring, resulting in permanent visual impairment [42]. Experimental evidence in mice shows that radiation exposure induces oxidative stress and apoptotic cell death, particularly under low-dose space radiation conditions, as observed in retinal endothelial cells, with potential implications for corneal health [43].  
Radiation levels depend on mission distance from Earth and duration. For instance, astronauts on the International Space Station (ISS) are exposed to approximately 200 mSv annually, while missions beyond Earth's magnetosphere, such as those to Mars, will expose astronauts to significantly higher doses. Prolonged exposure to GCRs and SEPs amplifies risks of cumulative tissue damage, including radiation-induced cataracts and retinal degeneration [44]. Galactic cosmic rays (GCR) consist mainly of protons and helium ions, with approximately 1% heavy ions. These heavy ions, known as high atomic number and energy ions (HZEs), include elements such as oxygen and iron, which generate dense ionization tracks that cause clusters of irreparable DNA damage [45].  
To address these risks, current protective measures, such as polyethylene shielding, provide partial mitigation but remain inadequate against high-energy GCRs. Advanced materials like hydrogen-rich composites and novel protective visors are under development to enhance shielding efficacy for ocular tissues [44]. Additionally, biomimetic solutions, including GelMA hydrogel constructs, are being investigated for their resilience to radiation and potential as bioprinted corneal replacements. These constructs mimic the biomechanical and structural properties of the cornea while providing enhanced resistance to oxidative damage [24].  
Further research into oxidative damage mechanisms and countermeasures is essential to safeguard astronaut vision during long-duration missions. Continued innovation in shielding technologies, coupled with regenerative medicine approaches, will be critical for ensuring mission success and preserving astronaut health.
## 4 Conclusions  
The proposed REU-Net(2 EEAM) network demonstrates significant advantages in the high-resolution remote sensing image building segmentation task, especially in complex scenarios, where it can effectively distinguish the boundaries between buildings, background, and vegetation. By incorporating the Residual Structure, Edge Enhancement Attention Module, and optimizing the network with a hybrid loss combining edge consistency loss and BCE loss, the model can focus more on building edge information, thus improving segmentation accuracy. Compared to other classical networks, REU-Net(2 EEAM) exhibits better robustness and segmentation capability in various scenarios, particularly when buildings are small, sparsely distributed, and overlap with vegetation. Additionally, the ablation experiment results validate the significant performance improvement brought by the edge enhancement attention module. Overall, the proposed improved algorithm has strong practicality and broad application prospects, providing new ideas and methods for building segmentation research in the field of remote sensing images.
# Multi-Scale Audio Spectrogram Transformer for Classroom Teaching Interaction Recognition
## 3 Methods
### Multi-Channel Spectrogram Feature Construction  
Time and frequency domain analysis are both of the most important methods for speech signals. The Log-Mel spectrogram combines the advantages of these two analysis methods and uses a deep model to learn its advanced features. Log-Mel spectrogram contains a variety of acoustic information such as formant, fundamental frequency, energy, amplitude, etc., which is more intuitive to understand and easier to extract features with  
Figure 1: Overview of the MAST framework.  
the neural network than with the original speech signal. Log-Mel spectrogram contains a variety of acoustic information such as formant, fundamental frequency, energy, amplitude, etc., which is more intuitive to understand and easier to extract features by the neural network than the original signal. Therefore, Log-Mel spectrograms are used as model input data in this paper. However, existing audio transformer [25; 40; 41] methods use individualized features as input and audio classification performs differently. To overcome this problem, we used Log-Mels with deltas and delta-delta as input to the MAST model.  
The Log-Mel Spectrogram was constructed through the process in Figure 2. First, the deltas feature \(\mathrm{m_{i}^{d}}\) of the Log-Mel spectra was calculated and the Log-Mel feature \(\mathrm{m_{i}^{d}}\) was operated on using Formula (1). Similarly, the delta-delta feature \(\mathrm{m_{i}^{d}}\) was calculated by taking the time derivative of delta, as shown in Formula (2). Concatenate the obtained Log-Mel, delta and delta-delta features in the channel dimension, and finally obtained a three-dimensional feature representation \(\mathrm{X}\in\mathbb{R}^{\mathrm{t}\times\mathrm{f}\times\mathrm{c}}\) where \(\mathrm{t}\) represents the length of the time frame, \(\mathrm{f}\) represents the number of Mel-filter banks and \(\mathrm{c}\) represents the feature number of channels. The extracted multichannel Log-Mels features are shown in Figure 3.  
\[\mathrm{m_{i}^{d}}=\frac{\sum_{n=1}^{N}n(\mathrm{m_{i+n}}-\mathrm{m_{i-n}})}{2 \sum_{n=1}^{N}n^{2}} \tag{1}\]  
\[\mathrm{m_{i}^{dd}}=\frac{\sum_{n=1}^{N}n(\mathrm{m_{i+n}^{d}}-\mathrm{m_{i-n }^{d}})}{2\sum_{n=1}^{N}n^{2}} \tag{2}\]
# Attention-Guided Wireless Channel Modeling and Generating
## 3 Modeling and Generating
### Wgan-Gp
WGAN-GP forces the discriminator to adhere to the 1-Lipschitz constraint by introducing a gradient penalty term, which is defined as follows:  
\[GP=\lambda\mathbb{E}_{\overline{\mathbf{H}}\sim p_{z}}\Big{[}\big{(}\parallel \
abla_{\overline{H}}D\big{(}\overline{H}\big{)}\parallel_{2}-1\big{)}^{2} \Big{]}, \tag{6}\]  
where \(\lambda\) is the weight parameter of the gradient penalty, which is used to adjust the proportion of the gradient penalty term in the total loss. \(\mathbb{E}_{\overline{\mathbf{H}}\sim p_{z}}\) represents the expectation of the interpolated sample \(\overline{\mathbf{H}}\), where \(\overline{\mathbf{H}}=\epsilon\mathbf{H}+(1-\epsilon)G(z)\), and \(\epsilon\) represents a weight sampled from the uniform distribution \(U[0,1]\), employed to interpolate between the real sample \(\mathbf{H}\) and the generated sample \(G(z)\). \(\parallel\
abla_{\overline{H}}D\big{(}\overline{H}\big{)}\parallel_{2}\) is the 2-norm (Euclidean norm) of the gradient of the discriminator output with respect to \(\overline{\mathbf{H}}\). \(\big{(}\parallel\
abla_{\overline{H}}D\big{(}\overline{H}\big{)}\parallel_{2 }-1\big{)}^{2}\) ensures that the gradient norm of the discriminator is 1 almost everywhere in its entire input space, satisfying the 1-Lipschitz constraint [35]. In summary, the overall objective function of WGAN-GP is as follows:  
\[ \begin{array}{ll}min&max\\ G&D\in\mathcal{D}\end{array} V(D,G)=E_{\mathbf{H}\sim p_{z}}[D(\mathbf{H})]-E_{ z\sim p_{z}}[D(G(z))]+\lambda E_{\overline{\mathbf{H}}\sim p_{z}}\Big{[}\big{(} \parallel\
abla_{\overline{H}}D\big{(}\overline{\mathbf{H}}\big{)}\parallel_ {2}-1\big{)}^{2}\Big{]}. \tag{7}\]  
This objective function aims to train the generator and discriminator by minimizing the Wasserstein distance and ensuring 1-Lipschitz constraints, thereby improving the stability of the training and the quality of the generated data. This is especially important for wireless channel modeling and generation, as such applications require highly accurate and realistic generated data to simulate real-world wireless channel conditions.
## 3 Results
### Corrosion Studies
#### 3.4.4 AFM Analysis  
An AFM analysis was performed to study the surface morphology of corroded copper metal surfaces with and without inhibitor in 1 M HCl solution. Figure 6 shows the two-dimensional AFM images of copper surfaces which were exposed in 1 M HCl either without inhibitor or with 1000 ppm of _P. ginseng_ inhibitor. Figure 6a depicts the image of the unprotected copper specimen without inhibitor and Figure 6b reflects the image of the protected specimen with the ginseng plant extract as a corrosion inhibitor. In the absence of the inhibitor, due to speedy corrosion and deformation, high roughness is attributed. AFM is not only used for providing high-resolution images but can also be used to calculate surface roughness of a metal surface. Data about copper surface morphology derived from AFM images are helpful for understanding the corrosion process.
## 7 Experiments
### Experimental Setup  
Our campaign involves the deployment of a single MQTT broker and a variable number of MQTT clients.  
The MQTT broker was deployed on a standard laptop equipped with an Intel i7-8850 CPU at 1.8 GHz and 16 GB of RAM. We implemented the broker via a customized version of the HiveMQ broker, including the specific functionalities required by our solution.  
To implement an MQTT client we employed a Raspberry Pi Pico W device. The latter is equipped with a dual-core ARM Cortex-M0+ processor running at 133 MHz, 256 KB of RAM, and 2 MB of flash memory. The Raspberry Pi Pico is suited to emulate typical MQTT clients due to its limited computational and networking capabilities. Then to observe the scalability of the system, given the unavailability of a high number of physical Raspberry Pi Pico units, we deployed simulated clients (publishers and subscribers) on a standard laptop. The execution times of these simulated clients were calibrated to match those of the actual Raspberry Pi Pico devices.  
We considered publishers and subscribers connected to the broker through an internet connection. To simulate realistic network conditions, we incorporated network delays based on real-world data [8].  
To simulate the transmission of actual data by publishers, we used a dataset reporting temperature sensor data collected from a building (Accessible at: [[https://www.kaggle.com/code/koheimuramatsu/iot-temperature-forecasting/input](https://www.kaggle.com/code/koheimuramatsu/iot-temperature-forecasting/input)]([https://www.kaggle.com/code/koheimuramatsu/iot-temperature-forecasting/input](https://www.kaggle.com/code/koheimuramatsu/iot-temperature-forecasting/input)) (accessed on 10 July 2024)). In this dataset, each datum was associated with the timings in which it was generated by the sensor. This allowed us to compute a realistic rate of data generation.  
We used the Sepolia testnet as the blockchain network for our experiments. Specifically, we used the testnet to deploy the smart contract required by our solution. Then, we used this testnet to measure the timing required to invoke the smart contract functions. Similarly, we used the client from IPFS Desktop App ([[https://docs.ipfs.tech/install/ipfs-desktop/](https://docs.ipfs.tech/install/ipfs-desktop/)]([https://docs.ipfs.tech/install/ipfs-desktop/](https://docs.ipfs.tech/install/ipfs-desktop/)) (accessed on 10 July 2024)) to measure the timing required to store the MHT on IPFS.  
To conclude this section, we summarize the times we obtained in our setup both client-side and broker-side in Table 2.
## 3 Results and Discussion
### Contour Plots (2D) and Response Surface Plots (3D)
#### 3.4.4 Phosphate Removal  
As shown in Figure 8a,b, the interaction between PSE and pH plays a significant role in phosphate removal. At the central point of the parameters tested, the treated wastewater exhibited a reduction in phosphate concentration to 0.012 mg/L when using a chemical coagulant dosage of 5.5 mL/L. This behavior was mainly attributed to the deprotonation of functional groups in the PSE, which enhances the number of available adsorption sites, thereby improving phosphate removal efficiency.  
Figure 7: Contour plots and their corresponding surface plots for aromatic matter (UV 254) of wastewater as a function of (**a**,**b**) pH and PSE dosage at constant FeCl\({}_{3}\) dosage; (**c**,**d**) pH and FeCl\({}_{3}\) dosage at constant PSE dosage; (**e**,**f**) PSE dosage and FeCl\({}_{3}\) dosage at constant pH.  
Figure 8c,d further demonstrates that the most effective phosphate reduction occurred at a neutral pH (7) with an optimal coagulant dosage (FeCl\({}_{3}\)) of 5.5 mL/L, combined with 11 mL/L of PSE. However, at pH levels above 9, phosphate removal efficiency slightly declined due to competition between phosphate and hydroxide ions for adsorption sites. Additionally, at pH values exceeding 9, measurement biases may occur due to the high concentration of hydroxide ions (OH\({}^{-}\)). Conversely, at an acidic pH of 4, phosphate removal  
Figure 8: Contour plots and their corresponding surface plots for phosphate of wastewater as a function of (**a,b**) pH and PSE dosage at constant FeCl\({}_{3}\) dosage; (**c,d**) pH and FeCl\({}_{3}\) dosage at constant PSE dosage; (**e,f**) PSE dosage and FeCl\({}_{3}\) dosage at constant pH.  
dropped significantly to an unsatisfactory level. This reduction is likely due to the presence of hydronium ions (H\({}_{3}\)O\({}^{+}\)), which compete with phosphate ions for electrostatic interactions with the positively charged metal species, such as iron. As a result, the neutralization of negatively charged phosphate is hindered, reducing the overall coagulation efficiency.  
The impact of the interaction between the chemical and bio-coagulants on phosphate removal at pH 7.0 is shown in Figure 8e,f. The results indicate that phosphate removal improves when both coagulants are used within a moderate dosage range. This suggests a positive interaction between the coagulants, enhancing the coagulation process. Furthermore, the statistical analysis (Equation (5)) confirms the significance of this interaction, reinforcing the reliability of the observed trend.
## 2 Materials and Methods
### Statistical Analysis  
The results are expressed as mean \(\pm\) SD. Data analysis was conducted using Jamovi statistical software version 2.3.28. The normality of the distributions and homogeneity of variances were assessed with the Shapiro-Wilk test. Logarithmic transformations were applied when assumptions were violated. A two-way ANOVA was performed to examine the main effects of training condition (multi-feeding (MF) vs. match play (SMP)) and gender (males vs. females) as well as interaction effects. Post hoc comparisons were conducted with Bonferroni correction. The significance level was set at \(p\) < 0.05. Effect sizes were calculated and interpreted using Cohen's d, based on previously proposed thresholds for highly trained athletes, and interpreted as small (>0.2 and <0.6), moderate (\(\geq\)0.6 and <1.2), and large (\(\geq\)1.2 and <2) or very large (\(\geq\)2.0) [26]
# Local Differential Privacy-Based Federated Learning under Personalized Settings
## 3 System Model  
The notations used in this article are listed in Table 1.  
In this paper, we consider a horizontal federated learning architecture. As shown in Figure 1, there are \(N\) clients in total. The server initializes the initial federated model parameter \(W^{(0)}\), and sends \(W^{(0)}\) to each client \(i\). The parameter \(W^{(0)}\) is a \(d\)-dimensional vector. Each element in the vector is 0. In the \(r\)-th round, each client \(i\), trains the local model \(G_{i}^{(r)}\), based on the federated model parameter of previous round \(W^{(r-1)}\) and local training dataset \(D_{i}\). Then, each client \(i\), generates the clipped local model parameter \(\widehat{G}_{i}^{(r)}\), by the model clipping process. Next, each client \(i\), generates the perturbed local model parameter \(\widetilde{G}_{i}^{(r)}=M(\widehat{G}_{i}^{(r)})\), by the model perturbation mechanism \(M\), and sends \(\widetilde{G}_{i}^{(r)}\) to the server. In the end of the \(r\)-th round, the server generates the federated model parameter of \(r\)-th round \(W^{(r)}=f(\widetilde{G}_{1}^{(r)},\widetilde{G}_{2}^{(r)},\ldots,\widetilde{ G}_{N}^{(r)})\), by the model aggregation rule \(f\), and sends \(W^{(r)}\) to each client. Then the \(r+1\)-th round begins.
## 3 Results
### The Microstructure of Alloy after MDF  
The microstructure was analyzed after three and six circles of multidirectional forging (Figure 4). During the forging process, the Mg2 Si particles were fragmentized. The particle parameters are presented in Table 1. The particle mean size and aspect ratio were insignificantly influenced by temperature and cumulative strain. However, the decrease in standard deviation of interparticle distance confirmed the increasing particle distribution homogeneity with increasing MDF temperature. The bright Sc-bearing particles which were observed after homogenization were also found for the sample subjected to MDF. The analysis of the EDS maps revealed the particles (marked as type-A) with bright contrast enriched with Cu, Sc, Si, and a minor amount of Zr (Figure 5a-c). However, the localization of Cu was not found in the particles marked as type-B. Higher magnification BSE images and EDS analysis also revealed fine precipitates enriched with Cu in the samples as-forged at 250 \({}^{\circ}\)C and 350 \({}^{\circ}\)C. The precipitates size increased with an increase in forging temperature and cumulative strain.  
The EBSD IPF mapping of the studied alloy was carried out after six circles of forging (Figure 6). The grain and subgrain size increased with increasing temperature. The subgrain/grain sizes were 0.9 \(\pm\) 0.1/1.2 \(\pm\) 0.1 \(\upmu\)m, 1.7 \(\pm\) 0.1/2.0 \(\pm\) 0.1 \(\upmu\)m, and 1.8 \(\pm\) 0.1/2.1 \(\pm\) 0.1 \(\upmu\)m for 150, 250, and 350 \({}^{\circ}\)C, respectively. The grain boundary misorientation histograms revealed that the value of LAGBs decreased with increasing temperature from 150 to 250 \({}^{\circ}\)C. The further increase in temperature to 350 \({}^{\circ}\)C insignificantly changed the fraction of LAGBs and HAGBs.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Particle Parameter**} & \multirow{2}{*}{**Number of Circles**} & \multicolumn{3}{c}{**Temperature of MDF**} \\ \cline{3-5}  & & **150 \({}^{\circ}\)C** & **250 \({}^{\circ}\)C** & **350 \({}^{\circ}\)C** \\ \hline \multirow{2}{*}{Mean particle size, \(\upmu\)m} & 3 & 1.3 \(\pm\) 0.1 & 1.3 \(\pm\) 0.2 & 1.5 \(\pm\) 0.2 \\ \cline{2-5}  & 6 & 1.3 \(\pm\) 0.1 & 1.4 \(\pm\) 0.1 & 1.5 \(\pm\) 0.2 \\ \hline \multirow{2}{*}{Aspect ratio} & 3 & 0.62 \(\pm\) 0.04 & 0.68 \(\pm\) 0.03 & 0.65 \(\pm\) 0.04 \\ \cline{2-5}  & 6 & 0.63 \(\pm\) 0.03 & 0.67 \(\pm\) 0.03 & 0.66 \(\pm\) 0.03 \\ \hline \multirow{2}{*}{Mean interparticle distance, \(\upmu\)m} & 3 & 53 \(\pm\) 11 & 47 \(\pm\) 10 & 53 \(\pm\) 8 \\ \cline{2-5}  & 6 & 49 \(\pm\) 12 & 44 \(\pm\) 8 & 43 \(\pm\) 8 \\ \hline \multirow{2}{*}{Standard deviation of interparticle distance} & 3 & 42.9 & 36.8 & 34.9 \\ \cline{2-5}  & 6 & 49.2 & 35.1 & 36.7 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the Mg\({}_{2}\)Si particles after MDF.  
Figure 4: SEM-BSE micrographs of the studied alloy after (**a–c**) three and (**d–f**) six circles of MDF at (**a,d**) 150 \({}^{\circ}\)C, (**b,e**) 250 \({}^{\circ}\)C, and (**c,f**) 350 \({}^{\circ}\)C.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 3 Results  
This section characterizes the iGNSS-R altimeter SSH and quantifies its measurement accuracy by comparison with Jason-3 and Sentinel-6 reference data. The evaluation additionally encompasses the accuracy assessment of critical error correction components: ocean tide, polar tide, solid Earth tide, DAC, and dry/wet tropospheric corrections.
# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 3 Materials and Methods  
### Dataset  
Our research utilizes the HAM10000 (Human Against Machine with 10,000 training images) dataset [7] to train and test the model. This dataset contains 10,015 dermatoscopic images of seven different types of skin lesions, making it ideal for evaluating the performance of our classification model. The seven types of skin lesions are represented in Figure 1 and described below:  
* Akiec: Actinic Keratoses and Intraepithelial Carcinoma can be considered precursor cancer types of non-melanoma skin cancer, rather than actual cancer species [35].
* Bcc: Basal cell carcinoma is the most common form of skin cancer, which grows slowly and rarely spreads [36].
* Bkl: Benign keratosis is a variant of seborrheic keratosis, and it is difficult to classify because there are many biologically similar lesions and many lesions with similar morphological characteristics [37].
* Df: Dermatofibroma is a benign skin lesion considered an inflammatory response [38].
* Mel: Melanoma is a malignant tumor derived from melanocytes and can be cured with simple resection if found early [39].
* Nv: Melanocytic nevi exhibit many changes in the benign forms of melanocytes [40].
* Vasc: Vascular skin lesions include cherry hemangiomas, angiokeratomas, and pyogenic granulomas [41].  
The number and percentage of images in each class are shown in Table 1. Approximately 66% of the images belong to the 'nv' class. Notably, the 'vasc' and 'df' categories are highly imbalanced due to a relative lack of images. To address the imbalance among class images, the dataset sizes for classes with fewer images were increased through oversampling, making their numbers similar to the 'nv' class, which has the most images, as shown in Figure 2. This ensured an even balance of images across all classes in the dataset. Basic  
Figure 1: Images representing the seven types of skin lesions from the HAM10000 dataset: Actinic Keratoses (akiec), Basal Cell Carcinoma (bcc), Benign Keratosis (bkl), Dermatofibroma (df), Melanoma (mel), Melanocytic Nevi (nv), and Vascular Lesions (vasc).  
augmentation methods, including rotation, zoom, horizontal flip, and vertical flip, were applied for oversampling.