esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Experimental Results
#### 5.4.3 Effect of Initial Rotation Angles on Registration Results  
To further analyze and validate the effectiveness and robustness of our algorithm, experiments with different initial rotation angles were conducted on the noisy dataset with a fixed overlap rate of 60%. In the experiments, the initial translation vector was kept constant at \([-5,5]\), while the initial rotation angles were systematically varied from \([-0^{\circ},0^{\circ}]\) to \([-180^{\circ},180^{\circ}]\) to explore the relationship between registration results and initial rotation angles. The experimental results are shown in Figure 5.  
It can be observed that our algorithm exhibits high stability when the initial rotation angles are within the range of \([0^{\circ},30^{\circ}]\). The rotation and translation errors significantly increase when the initial rotation angle reaches \(30^{\circ}\) or larger. However, after the initial rotation angle exceeds \(90^{\circ}\), the errors of all algorithms begin to decrease. This phenomenon can be attributed to the fact that larger rotation angles make the features more distinct, facilitating the registration process.  
Additionally, we found that the time required for algorithm output is not strongly correlated with the initial rotation angle. In contrast, the NIGCN algorithm shows insufficient stability and struggles to work effectively under large initial rotation angles, taking the longest time. This indicates that the NIGCN algorithm has difficulty achieving accurate registration when dealing with complex structures. Compared with SGC, GDC, and NIGCN  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RMSE(R)** & **RMSE(t)** & **MAE(R)** & **MAE(t)** & **Times(s)** \\ \hline GDC [53] & 0.013 & 0.105 & 0.007 & 0.059 & 21.2 \\ GAT [44] & 0.051 & 0.451 & 0.012 & 0.092 & 165.5 \\ GCN [21] & 0.017 & 0.133 & 0.009 & 0.075 & 17.7 \\ GraphKan [50] & 0.008 & 0.060 & 0.004 & 0.033 & 23.5 \\ SGC [54] & 0.110 & 0.880 & 0.040 & 0.299 & 21.5 \\ NIGCN [55] & 0.117 & 0.943 & 0.043 & 0.320 & 26.1 \\ Ours & 0.003 & 0.021 & 0.002 & 0.014 & 18.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Registration performance at 60% overlap.  
algorithms, our algorithm demonstrates higher stability and robustness. Even under large initial rotation angles, our algorithm still provides stable and accurate registration results, with lower rotation and translation errors than other methods.
# A Coupling System for Prediction of Physiological Parameters in an Immersed Condition
## 3 Results
### Mean Skin Temperature  
The coupling system can predict the mean skin temperature more accurately in both air exposure and water immersion environments, and the majority of the predicted mean values are within SD of the measured values, according to a comparison of the predicted mean skin temperature of the thermal manikin and human thermal model coupling system with the experimental test results while wearing two life preservers of LP1 and LP2 (see Figure 7). There was no discernible difference between the predicted and measured mean skin temperatures, according to a paired-samples t-test (\(p>0.05\)). When wearing LP1, the maximum difference between measured and predicted values under air exposure  
Figure 5: Predicted results for heat production and measured data.  
Figure 6: Predicted results for core temperatures and measured data.  
and water immersion conditions did not exceed 0.28 \({}^{\circ}\)C and 0.57 \({}^{\circ}\)C, respectively. When wearing LP2, the maximum difference did not exceed 0.31 \({}^{\circ}\)C and 0.47 \({}^{\circ}\)C, respectively. The skin temperature was more sensitive than the core temperature, and the overall fluctuation range actually outperformed the core temperature, with a safe minimum temperature of 15 \({}^{\circ}\)C for the mean skin temperature (54% lower than the normal mean skin temperature of 33 \({}^{\circ}\)C) [32], and the safe minimum core temperature is 34 \({}^{\circ}\)C (only 8% below the normal core temperature of 37 \({}^{\circ}\)C) [33]. Although the deviation of the predicted and actual mean skin temperature values of the coupling system exceeded the good agreement criterion of 0.5 \({}^{\circ}\)C, the predicted deviation of the mean skin temperature is within 1 \({}^{\circ}\)C, demonstrating that the expected value is still within the range where the measured value is acceptable [31]. Moreover, the RMSD and SD for wearing LP1 and LP2 were, respectively, 0.42 \({}^{\circ}\)C and 0.65 \({}^{\circ}\)C, and 0.35 \({}^{\circ}\)C and 0.48 \({}^{\circ}\)C, and the \(R^{2}\) for wearing LP1 and LP2 was 0.974 and 0.985; it can be shown that the predicted and measured values are highly correlated. In conclusion, the coupling system shows good agreement for mean skin temperature between the predicted and measured values.
## 2 Materials and Methods
### Tested Model and Training Procedure  
In our study we adopted different base models (MobileNetV2 [17], VGG16 [18], InceptionV3 [19], ResNet50V2 [20], and InceptionResNetV2 [21]) without top layers, loaded with 'ImageNet' weights and added layers (Figure 3). Added layers included the following: a global average pooling layer (GA) or depthwise convolution layer (DW) with a \(4\times 4\) or a \(2\times 2\) filter (depending on the base model), two dropout layers with a \(0.35\) dropout parameter, a dense layer with \(128\) filters, and a regression layer with \(1\) filter. The ReLU activation function was used in both the convolution layers and the linear activation function was used in the depthwise convolution layer. The number of convolutional and dropout layers, as well as the number of filters and dropout parameters, were selected after multiple experiments. The loss function was defined as the mean square error, and the metric as the mean absolute error.  
The first experiment (Experiment I) consisted of a five-fold cross-validation using different base models with added layers and the DXA dataset. In the second experiment (Experiment II), the influence of data augmentation on the selected model (MobileNetV2 DW) was evaluated. In both experiments, the number of epochs was set to \(200\), and the base learning rate was set to \(0.0001\). All layers in the base models were frozen. The number of parameters in each model is presented in Table 1. In the third experiment (Experiment III), we evaluated the one selected model on the RSNA 2017 [22] bone age dataset. The depthwise convolution layer was adjusted to larger images (\(12\times 12\) filter), and the number of epochs was reduced to \(50\). Additionally, we evaluated the model presented in [13] (the attention-based model) using the DXA and augDXA datasets. The images were padded to \(224\times 224\) pixels and the model was adapted to the same size. In the fourth experiment (Experiment IV), we investigated whether the usage of the DXA images improved prediction of age in comparison to single-energy X-ray images. For this purpose, the five-fold cross-validation (the MobileNetV2 DW model) was conducted using the images obtained by concatenating the low- and high-energy arrays separately. Similarly, the images consisting only of the copies of arrays corresponding to low/high energy passing one of the filters were tested. All calculations were performed at the NVIDIA DGX-1 station available at the AI Living Lab, Gdansk University of Technology.  
Figure 3: Scheme of proposed models used in the study. A preprocessed input image was first processed by the base model. Five different base models were investigated as indicated by the blue block. Features extracted by the base model were provided as an input for the head model. The first layer of the head model was either depthwise convolution or global average pooling. Both methods were investigated together with the fixed set of remaining layers of the head model presented in the figure.
## 2 Basic Principle of SAR Imaging Simulation
### Wave Spectrum Simulation  
In this paper, the Pierson Moskowitz (PM) spectrum was used to complete the wave spectrum simulations. The PM spectrum belongs to the gravity spectra and is also a kind of steady-state sea spectrum. The one-dimensional (1-D) PM wave spectrum model is as follows:  
\[S(\omega)=\frac{\text{a}g^{2}}{\omega^{5}}\exp[-b(\frac{g}{U_{19.5\omega}})^{ 4}] \tag{1}\]  
where a = 0.0081, b = 0.74, g is the acceleration due to gravity, \(U_{19.5}\) represents the wind speed at a height of 19.5 m above the sea surface, \(\omega\) is the frequency and S(\(\omega\)) is the value of the PM spectrum. However, in the calculation of ocean waves, the wind speed at a height of 10 m (\(U_{10}\)) above the sea surface is usually selected, and the conversion relationship between the wind speeds [24] is as follows:  
\[U_{h}=U_{10}\cdot(1+2.5\log_{10}^{0.1+h}(\sqrt{0.0015/[1+\exp(-\frac{U_{10}-1. 25}{1.56})}]+0.00104)) \tag{2}\]  
where h is the sea surface height and \(U_{h}\) represents the wind speed at different sea surface heights, except 10 m.  
It is necessary to describe the waves in the different propagation directions by means of a direction function. The 1D wave spectrum is multiplied by the normalized direction function to obtain the two-dimensional (2D) wave direction spectrum. The direction function selected in this paper is as follows:  
\[G_{PM}(\phi)=\frac{2}{\pi}\cos^{2}(\frac{\phi-\phi_{m}}{2}) \tag{3}\]  
where \(\phi\) denotes the wave propagation direction, \(\phi_{m}\) is the wave direction of the main wave, usually taken as the wind direction angle, so \(\phi-\phi_{m}\) represents the angle between \(\phi\) and \(\phi_{m}\), which is usually called the azimuth angle. According to the relationship to gravity dispersion, the 2D wave direction spectrum is converted into a 2D wavenumber direction spectrum, and the expression is as follows:  
\[S_{PM}(\text{K},\phi)=\frac{a}{2K^{4}}\exp(-\frac{bg^{2}}{K^{2}U_{19.5}^{4}}) \cdot\frac{2}{\pi}\cos^{2}(\frac{\phi-\phi_{m}}{2}) \tag{4}\]In the equation, the values of \(a,b,\) and \(g\) are the same as those in Equation (1), K denotes the wavenumber, and \(S_{PM}(K,\phi)\) represents the wavenumber direction spectrum.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 5 Experiment and Analysis
### Comparative Methods  
In order to verify the effectiveness of the method proposed in this paper, we compared it with two classical methods based on physical models and six recent methods based on neural networks. The methods for comparison include DCP [16], UDCP [54], DeepSesr [10], Funie-GAN [28], MLFcGAN [8], UWCNN-SD [9], SUSIR [55], and SCNET [22].  
We also conducted experiments on multiple low-quality image datasets, comparing these with existing classical and state-of-the-art deep learning-based methods. For the dehazing task, the comparative methods included PFDN [56], FFA-Net [57], AECR-Net [58], D4 [59], and All-in-one [60]. For the rainy dataset, the comparative methods included PreNet [61], MSPFN [62], DualGCN [63], JRGR [64], and All-in-one [60]. For the desnow task, the comparative methods included DesnowNet [65], JSTASR [66], HDCW-Net [49], and All-in-one [60].
## 2 General Flow and Basic Scenario
### Assumptions and Notations  
From the perspective of mechanism analysis, based on model construction, scenario analysis, and simulation iteration, the following assumptions need to be made in this paper:  
1. The research scenario in this paper can be reduced to a \"three-body\" problem that neglected the BDS-3 satellite as it has a negligible gravitational force on the targets;
2. The correction equation of the pseudocode ranging equation between Beidou-3 and targets in cislunar space will focus on large error factors, including antenna phase center offset and relativistic effects, while neglecting others;
3. The correction equation of the pseudocode ranging equation between the reflected signals and the ground-based CEI will focus on large error factors, including atmospheric delay errors, phase center offset of the station antenna, relativistic effects, and tidal effects, while neglecting others.  
The main symbols used in this paper are illustrated in Table 1.  
Figure 4: Overview of the non-cooperative target tracking and measurement scenario in cislunar space based on the CEI and BDS-3 inter-satellite link.
# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 2 Medical Question Answering Systems
### Types of Medical QAS  
There are different classification standards for QASs. For example, the authors in [27] identified eight key criteria that researchers usually follow to classify QASs, namely, application domains, types of data, types of questions, characteristics of data sources, types of techniques used to retrieve answers, types of analyses performed on questions and source documents, and forms of answers. The authors in [34] divided the QASs into different categories based on five criteria, namely system domain, question type, system type, information source, and information source type.  
In this section, we categorize the medical QAS by the paradigm each one implements. According to [48], there are four types of MQAS: Based on Knowledge Base (KB), Based on IR, Based on NLP, and Based on Hybrid paradigm:  
1. KB-based MQA: Usage of structured data source, rather than unstructured text. For example, Ontologies are formal representations of a set of concepts and their relationships in a given domain;
2. IR-based MQA: Retrieving answers using search engines and, when the recovered passage appears, filters and ranking are applied to them;
3. NLP-based MQA: The use of linguistic insights and machine learning procedures to extract answers from the retrieved snippet;
4. Hybrid-based MQA: A hybrid paradigm is the combination of all three types (IR MQA, KB MQA, and NLP MQA). It uses modern search engines and enriching community contributing knowledge on the web. An example of this paradigm is IBM Watson.
# Effect of Ultrasonic Treatment on the Quality of Pumpkin Juice Fermented by Yeast
## 3 Results
### Influence of Ultrasonic-Assisted Yeast Fermentation on the Antioxidant Activity of Pumpkin Juice
During the propagation of ultrasonic waves, the phenomenon of cavity collapse occurs due to the unique cavitation effect [36]. This cavity collapse will instantly trigger the formation of a high-temperature and high-pressure environment in the local area [37]. The synergistic effect of these two extreme factors, namely high temperature and high pressure, directly targets the structure of the cell walls and cell membranes of microorganisms, causing their structures to disintegrate. Consequently, it undermines the integrity and physiological functions of microbial cells, ultimately achieving the goal of sterilization. As can be seen from Figure 3A, the degree of agglomeration of the particles without ultrasonic treatment is slightly higher, which is also evident when the ultrasonic treatment is carried out at relatively low powers of 100 W and 200 W (Figure 3B,C). This indicates that lower power does not cause obvious changes in the particles in the pumpkin juice. However, when the ultrasonic power reaches 300 W and above, the particles are significantly crushed (Figure 3D,E), resulting in the rupture of the cells in the pumpkin juice due to cavitation. It is very likely that at a higher amplitude, the collapsing bubbles become more intense, and that the size of the bubbles is proportional to the amplitude of the ultrasonic waves [38]. This is consistent with the findings of [PERSON] et al. [39], who discovered that low power does not cause significant changes in the morphology of the juice, but that the juice particles are crushed under high-power treatment. The study by [PERSON] et al. [40]found that the orange-carrot blended juice treated with ultrasound showed an increase in the number of smaller particles, which is also in line with the results of this experiment. [PERSON] et al. [41] found through their research that with the increase in ultrasonic time and power (>300 W) for mango juice, there were more particles with smaller particle sizes (decreasing from approximately 100 \(\upmu\)m to 2.0 \(\upmu\)m), which is also consistent with the results of this experiment.
###### Abstract  
This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (R\({}^{2}\) score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of \(10^{-4}\); this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 \"acceptable\" and 4/5 \"unacceptable\" instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan.  
references: 1  
[PERSON], [PERSON], [PERSON] Real-Time Signal Quality Assessment and Power Adaptation of FSO Links Operating Under All-Weather Conditions Using Deep Learning Exploiting Eye Diagrams. 2025 12 789 1
# A Survey on Hardware Accelerators for Large Language Models
## 2 Computational and Energy Requirements
### Energy Consumption  
The immense computational requirements of LLMs translate into significant energy consumption. Training a state-of-the-art LLM can consume as much energy as the lifetime consumption of multiple cars, highlighting the environmental impact of these models. This energy consumption primarily stems from the use of high-performance GPUs or TPUs that are necessary for the parallel processing capabilities required in training these models.  
The energy-intensive nature of LLMs raises concerns regarding their carbon footprint, especially when trained and operated in data centers powered by non-renewable energy sources. The continuous operation of these models for inference tasks also contributes to ongoing energy usage, although it is typically less than that required for training. The trade-off between the benefits offered by LLMs and their environmental impact is an ongoing area of research and debate, emphasizing the need for more energy-efficient model architectures and the adoption of green computing practices in the AI field.  
Efforts to mitigate the energy consumption of LLMs include optimizing model architecture for efficiency, implementing more energy-efficient hardware, and utilizing renewable energy sources for data centers. These measures aim to reduce the environmental impact and maintain the advanced capabilities of LLMs.
## 3 Materials and Methods
### EEG Image Processing
#### 3.3.1 Preprocessing of EEG Data  
A notch filter (45-55 Hz) was applied to the EEG signals to eliminate interference caused by power lines. The EEG images were then divided into fragments corresponding in time to periods of correct and incorrect password entry.  
Visual analysis of the fragments showed that the subjects' EEGs generally have distinctive features. For some subjects, the moments between the end of the next phrase input and the beginning of the next one are clearly visible (Figure 5). For each subject, a different level of correlation is observed between the fragments of signals from different electrodes (cross-correlation). Autocorrelation functions also have features. In particular, the average number of zero crossings (sign change frequency) and extrema for autocorrelation functions differ for different subjects. Individual features of human EEGs are quite unstable, but they can be observed in large samples by constructing empirical probability densities of the values of the parameters under study.  
The EEG records were divided into fragments corresponding to the periods of phrase input. The average fragment duration was 7.5 s (1875 reports). Then, from each fragment, a signal spectrogram and a spectrogram of its autocorrelation function were calculated for each channel, Fp1, Fp2, Fz, F3, F4, and Cz (a total of 6 channels), with a window width of 64 and a step of 4. In this case, 4 types of window functions were used when calculating the spectrograms--rectangular, Hamming, Bartlett and Gauss. The output was 48 spectrograms (2 types of function--original and autocorrelation, 4 types of windows and 6 channels). This technique with several types of window functions was used to augment the training sample of the autoencoder, which was then used to transform the spectrogram into a feature vector.  
All spectrograms after their construction were interpolated along the time scale to bring them to a fixed dimension of 32 \(\times\) 256 (where the first dimension is associated with the frequency scale of the spectrogram; the second is associated with the time scale).  
Figure 5: EEG during the entry of the password phrase by the subject