esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Modified 1D-ResNet34  
We modified the ResNet34 model to a 1D version to extract features from the raw RRIs [58]. The selection of the architecture plays an important role in the development of deep learning models. ResNet was chosen as the base architecture for our proposed model owing to its successful performance. A previous review article reported that ResNetoutperformed other deep learning models in classifying time-series data [59]. In addition, ResNet was employed with RRI data to classify arrhythmias [34].  
Figure 3a depicts the overall architecture of the model. Figure 3b depicts the conv block. The conv block contains one 1D convolutional layer, a batch normalization layer, and a max pooling layer. Figure 3c depicts the residual block. The residual block contains three 1D convolutional layers and two batch normalization layers. Two 1D convolutional layers were used for the stacked layers and the other 1D convolutional layer (kernel size = 1) was used for a shortcut connection. ReLU activation was used for each layer, and a batch normalization layer was placed before the vectors were fed into the activation function. Table 3 presents details of the model architecture. In every convolutional layer, the input vectors were zero-padded for seven, except in the shortcut connections. In the linear block, dropout layers (50%) were added after each activation function, and SoftMax was used for the activation function on the output layer.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Block** & **Output Shape** & **Number of Layers** & **1D-ResNet34** \\ \hline \multirow{2}{*}{Conv} & (64, 1200) & 1 & 1 \(\times\) 15, 64, stride 1 \\  & (64, 598) & 1 & 1 \(\times\) 5 max pool, stride 2 \\ \hline \multirow{2}{*}{Residual 1} & (64, 598) & 4 & \(\begin{bmatrix}1\,\times\,\,15,\,64\\ 1\,\times\,\,15,\,64\end{bmatrix}\), stride 1 \\ \hline \multirow{2}{*}{Residual 2} & (128, 299) & 1 & \(\begin{bmatrix}1\,\times\,\,15,\,128\\ 1\,\times\,\,15,\,128\end{bmatrix}\), stride 2 \\  & & 3 & \(\begin{bmatrix}1\,\times\,\,15,\,128\\ 1\,\times\,\,15,\,128\end{bmatrix}\), stride 1 \\ \hline \multirow{2}{*}{Residual 3} & (192, 150) & 1 & \(\begin{bmatrix}1\,\times\,\,15,\,192\\ 1\,\times\,\,15,\,192\\ 1\,\times\,\,15,\,192\end{bmatrix}\), stride 2 \\  & & 5 & \(\begin{bmatrix}1\,\times\,\,15,\,192\\ 1\,\times\,\,15,\,192\\ 1\,\times\,\,15,\,192\end{bmatrix}\), stride 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Architecture of modified 1D-ResNet34.  
Figure 3: Modified 1D-ResNet34. (**a**) Overall architecture; (**b**) Conv block; (**c**) Residual block.
# Three-Dimensional Broad Learning Gravity Data Inversion Using Single-Anomaly Training Samples
## 1 Introduction
### Implementation Procedure of Broad Learning Gravity Data Inversion  
The implementation steps of BL gravity data inversion can be summarized as:  
1. Generate 33,000 random single-anomaly training sample pairs, as described in Section 2.3;
2. Select 30,000 samples to form the training dataset and 3000 samples for the validation dataset;
3. Use the training dataset to train a series of BL networks with varying network complexities;
4. Apply the validation dataset to identify the BL network with the most appropriate complexity;
5. Input the measured gravity data into the selected trained network to predict the optimal density model.  
In particular, the computer for inversion tasks in this study is equipped with an Intel 11 th Gen Core i7-11700 KF @ 3.60 GHz and 64.00 GB (3200 MHz) RAM. Once the network training is complete, the gravity anomaly data inversion is completed within 0.2 s.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Accuracy Improvement in Body Sensors
In this approach, data are categorizedinto classes such as hypoglycemia, non-diabetic, prediabetes, diabetes, severe diabetes, and critical diabetes. A patient-specific personalized model is then trained, with MLP used in the final stage for accurate prediction of blood glucose concentration [45]. Moreover, DL-based hybrid approaches have been applied for improving the accuracy of EMG by combining CNN and long short-term memory (LSTM) models [46]. Multiple DL models can be combined in an ensembling approach to increase the predictive accuracy of ECG-based sensor data [47].
# Wave Orbital Velocity Effects on Radar Doppler
## 3 Simulation and Results  
The consequences of R-effect on DDA measurement can only be assessed numerically; for this work, a conceptual model was used for the reconstruction of sea surface height from DDA. The examples provided here are relative to the CryoSat-2 satellite altimeter, with the parameters as reported in Table 1 above.  
A computational procedure was thus implemented on an XY grid, whose side Lx is equal to the across-track resolution, and Ly is sufficiently long to include all the points whose apparent positions are located within the along-track resolution \(\Delta_{\text{dy}}\). The computational steps are called DX and DY, respectively.  
The sea water instantaneous height \(\eta\)(X,Y), as well as its vertical velocity component V\({}_{\text{Z}}\)(X,Y), was computed on all the points in the grid. With reference to Figure 4, the Doppler frequency shift f\({}_{\text{t}}\) of a given point is given by Equation (5), so that its apparent position Y\({}_{\text{ai}}\) is:  
\[\text{Y}_{\text{ai}}=\text{Y}_{\text{i}}+(\text{f}_{\text{R}}+\text{f}_{\text {d}})\frac{\lambda\text{h}}{2\text{V}_{\text{S}}}. \tag{6}\]  
The strip will thus belong, or not, to the Doppler resolution strip \(\Delta_{\text{dy}}\), according to whether \(\Delta_{\text{dy}}/2<\text{Y}_{\text{ai}}<\Delta_{\text{dy}}/2\) or not.  
The influence of wave induced Doppler shift can be first observed by considering the formation of waveforms; as stated above, R-effect changes the apparent position of reflected spots of the sea surface, so that the effective borders of the resolution zone are no longer simply given by Equation (2); this in turn will affect the waveform shape. In order to investigate this aspect, the points on the sea geometrically positioned in and around the nominal beam \(\Delta_{\text{dy}}\) were relocated by taking the R-effect into account. By numerical computation, the distribution of distances between the antenna and the instantaneous simulated waveform (in the following: WF) can thus be constructed. This algorithm is simplified, since it neglects purely electromagnetic (e/m) effects, such as the antenna miss-pointing and the measurements noise, and it does not take into accounts the effects of the curvature of the Earth; it is therefore only meant to verify the effect that is being discussed here, which is purely geometrical and hydrodynamical.  
The procedure was repeated twice, once by activating the R-effect, and once by ignoring it so that the simulated waveforms could be compared.
## 4 Examples
### Pinched Hemispherical Shell  
The pinched hemispherical shell with a central aperture is discretized into 256 quadrilateral finite elements, with the mesh configuration and loading conditions depicted in Figure 11. The geometric and material properties are specified as follows: radius \(\mathrm{R}=10\) m, top opening angle \(\theta=18^{\lx@math@degree}\), thickness \(\mathrm{h}=0.04\) m, [PERSON]'s ratio \(\
u=0.3\), [PERSON]'s modulus \(\mathrm{E}=6.825\times 10^{7}\) Pa, and mass density \(\rho=1000\) kg/m\({}^{3}\). A constant time step \(\Delta t=1.25\times 10^{-4}\) s is employed for dynamic simulation over a total duration of 1.25 s. External loads \(P(t)\) (unit: N) are applied to the four base nodes as follows:  
\[P(t)=\left\{\begin{array}{ll}\frac{20000}{3}t,&t<0.015\,\mathrm{s}\\ 200-\frac{20000}{3}t,&0.015\,\mathrm{s}\leq t<0.03\,\mathrm{s}\\ 0,&0.03\,\mathrm{s}\leq t\end{array}\right. \tag{46}\]  
Figure 12a presents the time history curve of system energy over a 1.25 s duration. The strain energy and kinetic energy of the hemispherical shell with a central aperture exhibit periodic variations, while the total energy remains conserved throughout the computational period. To facilitate clear observation, the data at 0.3 s are extracted for plotting, as shown in Figure 12b.  
Figure 13 depicts the deformation of the apartured hemispherical shell at 0.01 s intervals. Subjected to concentrated nodal loads, the shell undergoes significant geometrically nonlinear deformation without measurable rigid-body translation or rotation. The structural vibrations exhibit distinct periodicity with a fundamental period of 0.04 s, consistent with numerical predictions by [PERSON] et al. [19]. Analysis of radial displacements at Nodes A and B reveals a phase difference of a half cycle, accompanied by equal vibration amplitudes.  
Figure 10: Temporal evolution of structural deformation of the cylindrical panel.  
Figure 11: Pinched hemispherical shell.  
Figure 12: Energy-time history curves of the pinched hemispherical shell: (**a**) full 1.25 s duration; (**b**) magnified 0.3 s segment.
## 3 Results  
### Soil Granulometric Composition  
The results of the granulometric composition for soil samples from the test areas are presented in Table 2 and Figure 4. The following mineral deposits were found: (i) silt loam (SiL)--sites 1-5, 7-11, and 13; (ii) silty clay-loam (SiCL)--site 12; and (iii) sandy loam (SL)--site 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sites** & **Fractions** & **Proportion of Fractions [\%]** & **Texture (USDA)** \\ \hline \multirow{3}{*}{Site 1} & sand (2–0.05 mm) & 16 & \\  & silt (0.05–0.002 mm) & 75 & silt loam (SiL) \\  & clay (\(<\)0.002 mm) & 9 & \\ \hline \multirow{3}{*}{Site 2} & sand (2–0.05 mm) & 19 & \\  & silt (0.05–0.002 mm) & 71 & silt loam (SiL) \\  & clay (\(<\)0.002 mm) & 10 & \\ \hline \multirow{3}{*}{Site 3} & sand (2–0.05 mm) & 26 & \\  & silt (0.05–0.002 mm) & 71 & silt loam (SiL) \\  & clay (\(<\)0.002 mm) & 3 & \\ \hline \multirow{3}{*}{Site 4} & sand (2–0.05 mm) & 39 & \\  & silt (0.05–0.002 mm) & 58 & silt loam (SiL) \\  & clay (\(<\)0.002 mm) & 3 & \\ \hline \multirow{3}{*}{Site 5} & sand (2–0.05 mm) & 33 & \\  & silt (0.05–0.002 mm) & 53 & silt loam (SiL) \\  & clay (\(<\)0.002 mm) & 14 & \\ \hline \multirow{3}{*}{Site 6} & sand (2–0.05 mm) & 56 & \\  & silt (0.05–0.002 mm) & 43 & sandy loam (SL) \\  & clay (\(<\)0.002 mm) & 1 & \\ \hline \multirow{3}{*}{Site 7} & sand (2–0.05 mm) & 17 & \\  & silt (0.05–0.002 mm) & 65 & silt loam (SiL) \\  & clay (\(<\)0.002 mm) & 18 & \\ \hline \multirow{3}{*}{Site 8} & sand (2–0.05 mm) & 16 & \\  & silt (0.05–0.002 mm) & 67 & silt loam (SiL) \\  & clay (\(<\) 0.002 mm) & 17 & \\ \hline \multirow{3}{*}{Site 9} & sand (2–0.05 mm) & 19 & \\  & silt (0.05–0.002 mm) & 66 & silt loam (SiL) \\  & clay (\(<\) 0.002 mm) & 15 & \\ \hline \multirow{3}{*}{Site 10} & sand (2–0.05 mm) & 10 & \\  & silt (0.05–0.002 mm) & 75 & silt loam (SiL) \\  & clay (\(<\) 0.002 mm) & 15 & \\ \hline \multirow{3}{*}{Site 11} & sand (2–0.05 mm) & 13 & \\  & silt (0.05–0.002 mm) & 66 & silt loam (SiL) \\  & clay (\(<\)0.002 mm) & 21 & \\ \hline \end{tabular}
\end{table}
Table 2: Division of mineral deposits into granulometric subgroups according to the percentage of sand, silt and clay fractions of soil samples taken from test stands.
## 4 Practical Implementation
### Special Risk Groups
#### 4.1.2 Geriatric Patients  
Patients aged \(\geq\)65 years comprise a large portion of the critically ill population and often struggle with nutritional intake and risk of malnutrition. In the ICU and IMC-U setting, additional acute illness on top of an already frail state often leads to poor outcomes [95]. The assessment of the frailty score in this subgroup might be a valuable addition to malnutrition screening [21].  
A state of sarcopenia in acutely ill elderly patients is associated with increased short-term mortality [22]. The course of elderly patients according to their nutritional status (BMI and albumin levels) was evaluated and resulted in a higher prognostic value than their absolute age [23]. However, a recent review emphasized the importance of additional parameters such as energy intake, weight loss, and grip strength, whereas biochemical markers were susceptible to the underlying disease process and were not always reliable [96].  
Swallowing and chewing problems may be an important issue affecting oral intake in geriatric patients. Therefore, smaller and more frequent meals completed by oral nutritional supplements (ONS) are suggested [96]. Evaluation of swallowing, dental status, oral health, and potential drug side effects that might impair oral feeding should be conducted systematically [95].  
About 20% of older critically ill patients have sarcopenia before hospitalization [22; 97] and are subjected to various antianabolic and procatabolic factors during critical illness. An ESPEN expert working group recommends 1.2-1.5 g protein/kg/day in elderly patients suffering or at risk for malnutrition, since they have acute or chronic illness. Even higher protein intakes are recommended for this patient population in case of severe illness or injury [98]. In a recent review of older ICU patients, besides the provision of at least 1.2-1.5 g protein/kg usual BW/day, a combination including regular and early ambulation (if possible) and/or physical therapy and follow-up rehabilitation are prudently recommended to counteract muscle loss [97]. These recommendations are based on observational data or studies in non-critically ill patients [99; 100]. Therefore, this review acknowledges the need of further research to consolidate the most efficacious in-unit and post-discharge nutrition and physical activity strategies with well conducted clinical trials [97].  
Vitamin D deficiency is common in the elderly and the critically ill and is associated with increased mortality. According to the 2019 ESPEN guidelines, vitamin D can be supplemented (GPP) with a single dose of 500,000 IU vitamin D3 within one week after admission (grade 0) in case of measured low plasma levels (25-hydroxy vitamin D <12.5 ng/mL, or <50 nmol/L) [17].
## 2 Methodology
### Performance Evaluation
#### 2.4.2 Classification Evaluation  
To train and evaluate the GAN and XGAN models, we employed a strategy based on regions of interest (ROIs). Figure 6 illustrates the classification evaluation process. Initially, we reserved 20% of the dataset for testing and divided the remaining 80% into five stratified folds. Each fold's images were cropped into 64 \(\times\) 64-pixel ROIs to ensure that ROIs from the same image did not appear in both the training and validation sets. We conducted classification using cross-validation, with four folds for training and one fold for validation. For data augmentation, we used the GAN and XGAN models exclusively in the training group to prevent overfitting, with an augmentation rate equal to 100% of the training set size. We fine-tuned Transformer models, initially trained on the ImageNet dataset, for the datasets under investigation. Classification performance was evaluated using the accuracy metric, which measures the proportion of correctly classified samples. Finally, we compared the classification performance of Transformer models with and without data augmentation using the proposed XGAN and the original GAN architectures.
## 1 Introduction
Knowing the importance of electrical machines in the functional operation of the electric propulsion system, prognosis and RUL calculations are crucial for hybrid electric vehicles in general and for used electric machines specifically [23; 24; 25]. Permanent magnet machines are widely used and advantageous in HEV applications [26] compared to other types of electrical machines due to their high efficiency, high power density, and simple construction [27; 28; 29; 30]. It is hard to find a prototype for electrical machines where cracks are easily integrated for the aim of monitoring and collecting data for selected parameters that are affected by this type of fault, such as torque, current, temperature, and vibration [31; 32; 33] This is why in [34; 35; 36], a finite element model (FEM) of surface PMM is built; the electromagnetic, thermal, and vibration aspects of the machine are encountered. Additionally, in [37], a global HMM is built with the aim of detecting several types of faults in PMM: demagnetization represented by a crack in one piece of magnet in the machine, turn-to-turn short-circuit faults in one slot and eccentricity faults. In this paper, two novel ideas will be discussed. Firstly, we present a strategy for computing the remaining useful life (RUL) related to a permanent magnet machine for the case where there is a crack in one of the magnets of the machine. Secondly, it is typically difficult to collect real-time data from a machine that gradually transitions from operating normally to becoming faulty over time due to a primitive fault. To this end, we utilize the finite element method for machines, as reported in [34; 35; 36]. The proposed FEM is validated via comparisons with the analytical model of machines in [34]. The FEM is an accurate way to model the electrical machine but in a specific state with no change in the physical aspect of the machine. To alleviate this problem, we combined this FEM with the Paris equation, which is a mature model for crack propagation. In this study we will consider the demagnetization fault where a tiny crack of 1 mm will be integrated in one magnet of the PMM. Then, a remaining useful life calculation strategy will be presented. This strategy will permit the calculation of the RUL before total failure of the defective element occurs after detecting the presence of the elementary crack using the HMM previously described in [37]. In Section 2, we will present the physical characteristics of the machine used. The modeling of the crack growth will be shown in Section 3. In Section 4, estimation strategy of RUL calculation will be discussed using a database model. In Section 5, the generated model will be applied on the selected surface PMM where the results will be illustrated.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 3 Proposed Approach
### Loss Function  
Based on the coarse-to-fine strategy, our model consists of three stages, and each stage outputs a restored image. Thus, we optimized the model with multi-scale loss as well. The loss function adopts three kinds of losses: multi-scale content loss, multi-scale frequency reconstruction loss [8], and multi-scale perceptual loss. We can suppose that the \(l_{k}\) is a ground-truth image in stage \(k\), and the corresponding \(\hat{S}_{k}\) is the corresponding restored a more image in stage \(k\).  
(1) Multi-scale content loss: we minimized the content loss between the ground truth and the predicted image with the Charbonnier loss [29] function. Minimizing the content loss gradually can restore more accurate results. \(\varepsilon\) was set to \(10^{-3}\).  
\[L_{content}=\sum_{k=1}^{3}\sqrt{\left\|\hat{S}_{k}-S_{k}\right\|_{1}+ \varepsilon^{2}}, \tag{14}\]  
(2) Multi-scale frequency reconstruction loss: The blurry image mainly lost the high-frequency information [8]; therefore, it is also important to reduce loss in the frequency domain. In this case, we employed the fast Fourier transformer (referred to as \(F\)) to obtain the \(L1\) loss as the frequency loss between the ground-truth image and the predicted output.  
\[L_{fft}=\sum_{k=1}^{3}\left\|F(\hat{S}_{k})-F(S_{k})\right\|_{1}, \tag{15}\]  
(3) Multi-scale perceptual loss: to further obtain perceptually satisfactory results, we used pre-trained VGG-19 [30] as the feature extractor. Like the multi-scale frequency reconstruction loss, we also adopted the \(L1\) function to measure the percentage of loss between the two images.  
\[L_{precent}=\sum_{k=1}^{3}\left\|\varphi(\hat{S}_{k})-\varphi(S_{k})\right\|_ {1}, \tag{16}\]  
Overall, the whole loss function can be expressed as follows, where \(\lambda_{1}\) is set to 0.1 and \(\lambda_{2}\) is set to 0.01.  
\[L=L_{content}+\lambda_{1}L_{fft}+\lambda_{2}L_{precent}, \tag{17}\]  
Figure 6: The structure of Supervised Attention Block.
## 2 Related Work
### Literature Review
They also proposed a quantization parameter for optimization, which was used to reduce the residuals' magnitude reduction. They improved coding efficiency by 3.34% on average. Some researchers also worked to improve VR contents' quality assessment. To assess the visual quality of the VR image, [PERSON] et al. (2018) proposed a VR quality score predictor and human perception guider based on deep-learning methods. They encoded positional and visual information regarding patches to obtain weight and quality scores. They predicted the overall image quality score by aggregating the quality score of patches with a respective weight of patches. [PERSON] et al. (2018) used a full-reference video quality assessment metric: Video Multimethod Assessment Fusion (VMAF) for 360-degree video assessment. They proved that the VMAF is feasible for 360-degree videos without any specific training and adjustment. Most of the aforementioned studies focused on creating and processing virtual reality content rather than remediating existing content. To the best of our knowledge, our work is a novel approach that has not been addressed in the academia and the JPEG-dominant digital imaging market. As our work is related to metadata in JPEG images, the coding and file format of JPEG are introduced briefly.