esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Attention Mechanism  
Due to the harsh production environment for ship components, with numerous interference information such as manual markings, scratches, and stacking on their surfaces, leading to substantial redundant information in spatial dimensions, a spatial separation-reconstruction approach was adopted to divide feature map data into two categories: information-rich and information-sparse, separated using thresholds. The Spatial and Channel Reconstruction Convolution (SCConv) module proposed by [PERSON] et al. effectively handles spatial and channel redundancy through Spatial Reconstruction Unit (SRU) and Channel Reconstruction Unit (CRU) units [14], providing effective ideas for spatial reconstruction, but the overall architecture has high computational complexity and large parameter count. Coordinate Attention (CA) performs cross-dimensional fusion embedding of spatial channels, showing excellent performance in feature interaction, but its identification and filtering effectiveness for redundant feature information is not ideal when facing multi-scale feature fusion [15].  
Inspired by the design concepts of SCConv spatial reconstruction strategy and CA cross-dimensional fusion, addressing the problems of high computational complexity in the former and insufficient redundant information identification capability in the latter, TSA is constructed as a triplet spatial reconstruction attention mechanism. TSA achieves a balance between performance enhancement and computational efficiency in small-sample data scenarios through innovative triplet parallel processing and spatial separation-reconstruction strategies.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
For this, we use topographic indices like the geomorphic flood index (GFI) [27] or height above nearest drainage (HAND) [28]. [PERSON] et al. [29] used the HAND index to define preferred samples for nonsupervised classification. The ever-increasing volume of radar data and the sometimes very wide extent of floods justify this need to bound processing, and radar images could take a long time to download depending on available data rates. Cloud computing services offer a way around these issues. Since Google Earth Engine (GEE) has come on stream [30], a growing number of applications have been developed to use this service for remote sensing and geographic information systems. GEE is a cloud-based geospatial analysis platform that allows users to visualise and analyse Earth observation (EO) data. GEE's database includes all freely accessible satellite imagery and is refreshed daily. Imagery can be processed directly in the cloud without the need to download it [31], which is very useful in countries like Madagascar, where bandwidth can be low and downloading heavy data complicated. GEE is used in a wide range of applications like vegetation mapping and monitoring, land cover mapping, agriculture, and hazard management [32; 33], including for floods [34]. GEE is accessible via two client libraries. The first one is based on the JavaScript (JS) programming language and the second on Python. While both libraries provide access to the same services, Python makes it easy to automate the execution of laborious and multiple tasks. This advantage afforded by Python enables the parallel processing of several images. Google Colab, an online Python interpreter, is the tool used with GEE's Python application programming interface (API). In this paper, we report on the development of an image-processing chain for identifying floods from several Sentinel-1 scenes. The approach is designed to address the challenges of monitoring extensive areas using synthetic aperture radar (SAR) and its automatisation. This chain is implemented in Google Colab using the Python API. It was first tested and calibrated on the immediate outskirts of Antananarivo (Madagascar) using optical and radar images acquired before and after a series of floods in 2018. It is then applied to the whole of Madagascar after the passage of Cyclone Batsirai on 5 and 6 February 2022. The next section of the paper covers the data we used, where we calibrated the method and how we carried out the testing. We also explain the steps in our process. Then, we share our results and compare them to other flood maps from when Cyclone Batsirai occurred. We also check our results against another method called S1 Chain [34]. Lastly, we dive into discussing our results in detail.
# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 5 Proposed System
### Involved Technologies
#### 5.1.1 Internet of Things (IoT)
The healthcare sector is enabled to take advantage of real-time data insights, improve patient outcomes, and improve overall healthcare service delivery by fusing IoT devices with a secure monitoring system in the cloud.
# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data  
This section describes models and datasets used to forecast foF2. An LSTM model using the LSTM algorithm is developed based on hourly foF2 values and related parameters to forecast ionospheric parameter foF2 by 1 h in advance. To evaluate and compare the forecasting performance and accuracy of the LSTM model with other prediction models, two neural network models are established and illustrated for comparison.
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 5 Experiments and Results  
### ASVspoof 2017 Challenge and Dataset  
The ASVspoof 2017 Version 2.0 dataset [3; 20] includes three parts: training set, development set, and evaluation set. Each dataset contains both genuine and spoof utterances as shown in Table 1.
# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 2 Materials and Methods
### Datasets
#### 2.1.2 Dataset Statistics  
Table 1 is an overview of the publicly available datasets. They are datasets that first appeared in [8,10] and were created with pollen from four different countries, Serbia (SAU-SRB) [8], Switzerland (SAU-CH) [8], Lithuania (SAU-LI) [8] and Romania (MARS) [10]. In the case of the MARS dataset, the measurements were made at Magurele center for Atmosphere and Radiation Studies (MARS) (44.34N, 26.01E), south of Bucharest, Romania, by some of the authors of this paper. They have different numbers of classes, or genera of pollen, and vastly different numbers of samples per class. This class imbalance has to be accounted for during training and evaluating the results of a classification model.  
The datasets are varied in the number of classes and also the minimum, maximum and average number of samples per class. The pollen species present in each dataset also differ and are representative for the flora of each specific country.  
Table 2 shows the pollen species present in each of the datasets. The species present in the datasets differ based on the biodiversity of the respective region. The presence of different species of pollen in the datasets makes a comparison among the results across all of them difficult, because some species of plants have very similar pollen in size, shape and fluorescence response. Therefore, for each of the datasets, the importance of each of the feature type differs.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Number of Classes** & **Number of Samples** & **Minimum Number of Samples per Class** \\ \hline SAU-SRB \({}^{a}\) & 14 & 85 k & 985 \\ \hline SAU-LI \({}^{a}\) & 11 & 399 k & 16,114 \\ \hline SAU-CH \({}^{a}\) & 10 & 50 k & 1075 \\ \hline MARS \({}^{b}\) & 13 & 105 k & 3020 \\ \hline \end{tabular} \({}^{a}\) Dataset from [8]. \({}^{b}\) dataset from [10].  
\end{table}
Table 1: Datasets overview.
# Dual-View Integral Imaging 3D Display Based on Multiplexed Lens-Array Holographic Optical Element
## 2 Structure and Principle
### Design of Projection Part for the Dual-View 3D Display  
The projection part for the MHOE has to satisfy the Bragg conditions. Projection beam I must satisfy the projection angles \(\theta_{2}\) and \(\theta_{3}\) for reconstructed 3D images I in the left viewing zone, and projection beam II also must satisfy the projection angles \(\theta_{5}\) and \(\theta_{6}\) for reconstructed 3D image II in the right viewing zone. In this section, the projection part is realized based on two projectors, so that 3D image I and II are displayed simultaneously, as depicted in Figure 1.  
The principle and design of the projection part for the dual-view 3D display are shown in Figure 5. When reference beam I is projected onto the MHOE with a pair of angles \(\theta_{2}\) and \(\theta_{3}\), spherical wavefront array I is reconstructed with the angle \(\theta_{1}\). When reference beam II is projected onto the MHOE with a pair of angles \(\theta_{5}\) and \(\theta_{6}\), spherical wavefront array II is reconstructed with the angle \(\theta_{4}\). The best view distance of the dual-view 3D display system is \(L\), the focal length of the MHOE is \(f\) and the pitch is \(p\). According to the geometric optics, the viewing angle of the left viewing zone can be expressed as:  
\[\Omega_{1}=\arctan\frac{2f\tan\theta_{1}}{2f-p\tan\theta_{1}}-\arctan\frac{2 f\tan\theta_{1}}{2f+p\tan\theta_{1}} \tag{3}\]  
and the viewing angle of the right viewing zone can be expressed as:  
\[\Omega_{2}=\arctan\frac{2f\tan\theta_{4}}{2f-p\tan\theta_{4}}-\arctan\frac{2 f\tan\theta_{4}}{2f+p\tan\theta_{4}} \tag{4}\]  
The projection part is designed to make sure that the left and right view zone are completely separated, and reconstructed 3D images I and II cannot cause crosstalk, which is shown in Figure 5.  
Figure 5: Principle and design of the projection part for the dual-view 3D display.
## 7 Discussion and Results
### Sensitivity vs. Specificity Testing  
Sensitivity measures how many true positives remain in \(S\) set, and it is described as a sudden dip as \(Sc\) increases. Specificity measures how many true negatives remain in the removed \(R\) set and increases as \(Sc\) increases. Figure 22 presents the diagnostic testing yielding a recommended \(Sc\) range in normal cleaning and ANN applications, as demonstrated in Equations (25) and (26), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **ANN** & **ANN** & **\%** \\  & **Only** & **with SRP** & **Increase** \\ \hline Heart Disease & 76.19 & 79.89 & 3.7 \\ Gender Voice & 90.6 & 93.29 & 2.69 \\ Recognition & 90.49 & 92.36 & 1.86 \\ Cancer Patients & 79.82 & 83.27 & 3.45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A curacy of datasets with noisy samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **ANN** & **ANN** & **\%** \\  & **Only** & **with SRP** & **Increase** \\ \hline Heart Disease & 73.69 & 81.76 & 8.07 \\ Gender Voice & 85.72 & 91.2 & 5.42 \\ Recognition & 83.3 & 86.46 & 3.12 \\ Breast Cancer Patients & 80.90 & 88.81 & 7.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy of datasets with random samples.  
Figure 22: Sensitivity vs. specificity diagram.  
Table 8 shows the relation between \(Sc_{min}\) and \(Sc_{test}\), but in practicality, \(Sc_{test}\) is hard to determine due to unknown \(R\) sets, and whether it consists of noise or random samples. In general, for the normal cleaning process, \(Sc\) is close to or above the \(Sc_{min}\) or \(PC_{1}\) rate. For the given datasets, it is suggested that \(Sc_{normal}\) is as follows:  
\[80\%\leq Sc_{normal}\leq 96\% \tag{27}\]  
However, ANN applications need highly predictive samples [12], so it is suggested that selectivity (\(Sc\)) should be almost near 100%. Table 8 shows \(Sc_{ANN}\) at the following range:  
\[90\%\leq Sc_{ANN}\leq 99\% \tag{28}\]  
A high \(Sc\) value loses information and true positives in the \(S\) set but increases the specificity of the dataset; however, a low value of \(Sc\) adds noise and random samples that yield fewer true negatives and increases sensitivity. Careful adjustment of \(Sc\) vouches for a good result as a cleaning agent in the system.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Mathematical Foundations of DDPM
#### 3.2.3 Reversible Diffusion  
The denoising diffusion process in DDPM is designed to be reversible, meaning it has both a forward and an inverse transformation [49]. The reversibility ensures that the model can be trained to denoise images by learning to invert the diffusion process. Mathematically, the reversibility requirement is expressed as:  
\[X_{t-1}=f_{\theta_{t}}^{-1}(X_{t})-\sqrt{2\eta_{t}}\varepsilon \tag{3}\]  
This property is crucial for training the model to accurately denoise images during the learning process.
## 3 Results
* 0.75: a conservative model with higher precision but lower recall. Table 3 shows the confusion matrices for the three models obtained using the above-mentioned thresholds on the training dataset models' predictions. We can see in Table 3 that model 3 obtains the best overall results on the training data, although models 1 and 2 detect more overtopping using the 0.25 threshold. Model 3 performs better at detecting positive and negative cases for higher thresholds. Models 1 and 2 cannot detect overtopping events when using a 0.5 threshold in model 1 and 0.75 in model 2. This indicates that the positive predictions of models 1 and 2 have low values (the network outputs a low value), i.e., they predict values up to a point (less than 0.5 for model 2). In contrast, model 3 outputs values closer to 1 when it predicts an overtopping. Figure 3: PR curves for the wave overtopping models using the training datasets. Table 4 shows that, as previously explained, models 1 and 2 detect more overtopping using the 0.25 threshold, i.e., they have a higher recall. For higher thresholds, model 3 outperforms models 1 and 2. Model 3 has a higher precision and F1 score than models 1 and 2 at any threshold. We can also see that the precision can be misleading in an imbalanced problem. Due to the high prevalence of negative examples, all models achieve high precision (0.99) at every threshold, even when models 1 and 2 cannot detect any overtopping event for higher thresholds. Now that we have observed how the models performed on the training data and concluded, given the validation performance of the three models shown in Table 2, that model 3 is the best among the three, it can be chosen as the final model. Now, we will see how the models behave on the test data without using these data as a deciding factor, as we already determined model 3 to be the best at solving the wave overtopping prediction. Figure 4 shows the PR curves for the models obtained in the three testing datasets. Figure 4 shows that model 3 obtains the best results, with an AP of 0.86 over the testing data, performing even better than on the training data. Models 1 and 2 perform worse than model 3, achieving lower APs (0.67 and 0.48, respectively), as shown in their curves decaying more rapidly than model 3's curve. These models perform worse in testing than in training. Like in training, surprisingly, using the hmax variable in model 2 made it perform worse than model 1 in testing.
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 7 Discussion and Future Research
### Selection and Combination of Different Emulation Approaches  
With the rise of firmware emulation approaches, various solutions offer distinct advantages for different peripherals and application scenarios, affecting their performance and stability across test sets. Selecting or combining emulators to test target firmware can achieve higher fidelity and efficiency. For instance, while \"hardware-in-the-loop\" solutions have hardware dependence, it provides a realistic debugging environment through real hardware connections to accomplish high-fidelity emulation. If developers test a device with an unknown peripheral that current tools cannot emulate, they can forward this peripheral to the real hardware while keeping other peripherals in emulation. This approach facilitates testing the device efficiently compared to complete \"hardware-in-the-loop\" solutions.  
_HALucinator_ bypasses peripheral emulation by replacing high-level hardware abstraction layer (HAL) functions with host implementations. However, building a comprehensive database requires HAL source codes from major MCU vendors, limiting support for SoCs with proprietary SDKs. Combining HAL replacement with peripheral modeling approach could create an advanced firmware emulator with higher execution and data fidelity at the same time. Developers can use _HALucinator_ to match HAL functions and hook them to host implementations, while peripheral modeling tools can emulate unknown peripherals during runtime.  
For applications like baseband or automotive, specialized firmware emulators such as _FirmWire_ and _MetaEmu_ are preferable over general ones, as they offer more features tailored to these specific uses.
# Numerical Simulation Study on Welding Process of Upper Frame of Hydropower Unit
## 2 Experiments  
The structure of the T-shaped test plate used in welding test is identical to that of the upper frame; neither side is beveled, and the welding gap is 0. The dimension of the flange is 200 mm \(\times\) 200 mm \(\times\) 20 mm, the dimension of the web is 200 mm \(\times\) 100 mm \(\times\) 20 mm. When welding the T-shaped test plate, three weld seams were welded on each side of the web, totaling six weld seams. Figure 2 illustrates the size of the test plates, the distribution of weld passes, and the welding sequence.  
The material of the test plate is the same as that of the welding base material of the upper frame of the hydropower unit, which is Q345 structural steel. The filler wire used is ER50-6 solid core wire with a diameter of 1.2 mm. The chemical composition of the Q345 steel and ER50-6 welding wire is detailed in Table 1.  
Semi-automatic MAG shielded welding technology was used, with a shielding gas mixture of 80% argon and 20% carbon dioxide. The welding test was conducted using a YD-500 FR2 welding machine. The specific welding process parameters are shown in Table 2.  
Five K-type thermocouples were arranged on the surface of the welded flange plate to acquire temperature data. The size of the welding foot of the T-shaped joint on the upper  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{8}{c}{**Chemical Composition (\%)**} \\ \cline{2-11}  & **C** & **Mn** & **Si** & **P** & **S** & **N** & **Cu** & **Cr** & **Ni** & **V** \\ \hline Q345 & \(\leq\)0.20 & \(\leq\)1.60 & \(\leq\)0.55 & \(\leq\)0.025 & \(\leq\)0.010 & \(\leq\)0.010 & \(\leq\)0.55 & \(\leq\)0.30 & \(\leq\)0.012 & \(\leq\)0.15 \\ ER50-6 & 0.06–0.15 & 1.40–1.85 & 0.80–1.15 & \(\leq\)0.025 & \(\leq\)0.025 & - & \(\leq\)0.020 & \(\leq\)0.15 & \(\leq\)0.015 & \(\leq\)0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of the Q345 steel and ER50-6 welding wire.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Welding Sequence** & **Current/A** & **Voltage/V** & **Welding Speed/(mm\(\cdot\)s\({}^{-1}\))** & **Gas Flow Rate/(L-min\({}^{-1}\))** \\ \hline
1, 2 & 260–270 & 28–29 & 6.67 & 20–25 \\
3, 4, 5, 6 & 260–270 & 28–29 & 4.50 & 20–25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specific welding process parameters.  
Figure 2: T-shaped test plate size, weld distribution, and welding sequence diagram.  
frame is approximately 12 to 14 mm. To avoid interfering with the welding process, to prevent the arc from damaging the thermocouple's temperature measurement junction, and to ensure the temperature did not exceed the measuring range, the thermocouples were placed 20 mm and 30 mm away from the web's edge, as depicted in Figure 3.  
The residual stress was measured by blind hole method. Five points near the weld and parallel to the welding direction were selected for residual stress measurement, with each point 20 mm apart, as shown in Figure 4.
## 3 Results
### Lattice Strain, Crystalline Size, and Dislocation Density
Figure 13a,b shows how the size of nanometric grains changes as a result of ball milling time. These graphs illustrate that as the milling process advances, the crystallite size has a significant tendency to decrease in a parallel way. The crystallite size and lattice  
Figure 13: Crystallite size, dislocation density and lattice strain variation of (**a**) Al-5 Cr and (**b**) Al7.5 powder mixtures as a function of processing periods and types. strain were determined using the Williamson-Hall method [36; 37; 38]. The mean value of nanosized grains was computed after obtaining the breadth of diffraction peaks at half their height sizes. Figure 13a shows a considerable decrease in nanosized grains from about 85 to 35 nm when the powder blend was processed during 0 to 20 h, respectively. However, after sintering, crystallite size slightly diminished, this behavior can be due the nucleation and growth of new grains in the highly deformed Al matrix. Moreover, Figure 13b shows that when the milling period increased from 0 to 20 h, the lattice strain increased by approximately 0.0016 to 0.0027. Lattice strain is related to the deformation of reticular parameters caused by crystal defects or imperfections, such as lattice dislocations. The continuous rise in lattice strain can be attributed to the transfer of mechanical energy provided by the milling balls' repeated impacts on the metallic powders. As a result, the derived energy storage takes the form of crystallographic imperfections in the milled elements [39]. Strain formation is facilitated by the grain boundary triple junction, stacking faults, sinter stresses, coherency stresses, and other crystal defects [37]. Dislocation distribution is investigated using X-ray line broadening. In addition, Figure 13a,b indicates that the lattice strain was decreased after the recrystallization process. This behavior could be ascribed to the thermal removal of crystalline punctual defects induced by the thermal energy provided during the heat treatment. However, the dislocation multiplication process could be associated by the generation of dislocations at local regions of stress concentration particles promoted by thermal energy provided during the sintering process.
# COMS Visible Channel Calibration Using Moon Observation Data
## 3 Results  
### Temporal Trends  
Figure 2 shows the irradiance ratio (%) between instrument observations and the ROLO model of the COMS visible channel for a period of more than six years from April 2011 to March 2017. The long-term temporal trend clearly showed sensor degradation of about 8% throughout the total operational period. Annual analysis results of the degradation rate show approximate nonlinearity that can be categorized into two groups. First, during the first two years (2011-2012), there was a comparatively slight degradation rate of approximately 1.19% per year. Second, during the remaining years (2013-2017), the degradation rate was relatively larger but constant, at approximately 1.41% per year, with differences between years within 0.1%.  
The degradation rate based on Equation (8) was 1.49 \(\pm\) 0.11% per year, and the rate based on irradiance in Equation (9) was 1.34 \(\pm\) 0.12% per year, with 3-sigma values of 0.11 and 0.12, respectively. Equation (8) was also used to compare the performance with other sensors; results show a degradation value of 1.49 \(\pm\) 0.11% per year between MSG/VIS06 (0.48 \(\pm\) 0.02% per year) [26] and the MTSAT-2 visible channel (2.71 \(\pm\) 0.15% per year) [30]. The uncertainties of these results are discussed in Section 3.4, where it is considered how the results can be interpreted with respect to previous studies and the working hypotheses. It is necessary to discuss these findings and their implications in the broadest context possible.