esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Automated Diagnosis of COVID-19
### Pixel-Level Diagnosis: Segmentation-Based Models
#### 3.2.2 Preprocessing  
Similar to classification-based models, the following preprocessing methods are used during preprocessing: data augmentation and lung segmentation.  
Data augmentation, such as random clipping, left-right and up-down flipping, mirroring operation, rotation, scaling, etc. [71; 107; 123; 194], is of vital importance to train the neural network to achieve a high generalizability. In addition to these simple and common methods, [PERSON] et al. [42] used a cubic interpolation approach for image normalization. [PERSON] et al. [194] minimized the influence of various random noises (e.g., words) on the segmentation. To deal with the imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, [PERSON] et al. [123] developed a dual-sampling strategy to mitigate the imbalanced learning.  
As classification-based models, lung segmentation can reduce the interference from the area out of the lung and therefore boost the model performance. [PERSON] et al. [168] trained UNet++ to extract valid areas in CT images. [PERSON] et al. [47] employed an inception residual recurrent convolutional neural network with a Transfer Learning (TL) approach for COVID-19 detection and NABLA-N network model for segmenting the regions infected by COVID-19. [PERSON] et al. [161] used a fully automatic DL model (DenseNet121-FPN) to segment lung areas in the chest CT image. However, they found that some inflammatory tissues attaching to the lung wall may be excluded falsely by the model. In addition, there are also many other lung segmentation methods, such as: VB-Net [123], U-Net [195], ANN [71], FCN-8s, V-Net and 3D U-Net++ [42]. Among them, VB-Net replaces the conventional convolutional layers in the up and down blocks with bottlenecks, and achieves good and efficient segmentation results. U-Net is a fully convolutional network that uses skip connection to fuse the information of multi-resolution layers. V-net uses a volumetric and fully convolutional neural network to achieve three-dimensional image segmentation.
## 5 Results
### MSE, RMSE, and MAE  
We calculate the MSE, RMSE, and MAE for the different models with different time windows during VMD. The results are shown in Table 4.  
The MSE, RMSE, and MAE for the different models with different time windows without VMD are shown in Table 5.  
As can be seen from Table 4, the MSE, RMSE, and MAE of different models and different time window sizes do not show significant differences. The data in Table 5 also reflect this. However, overall, the error values after VMD are smaller than those without VMD. There is a slight improvement in terms of MSE, RMSE, and MAE.
## 3 Proposed Method
After classification, the LSTM network is trained for each day type. From Figure 4, dashed boxes represent the training examples that input to the LSTM network. The feature dimensions \(d\in D\) for \(M\) number of training examples include exogenous and categorical features for each example, where \(t\) presents the time steps, which are 24 h in day-ahead forecasting for each training example. The network consists of various layers, including the input layer, several LSTM-based hidden layers, the fully connected layer, and the output layer. The inclusion of more than one LSTM-based layer increases the depth of the network and helps it to learn the temporal dependencies associated with the relevant time steps. The fully connected layer and output layer are used to output the predicted result. From the input layer, the first-time step of the training example \(X_{1}\) is fed to the first LSTM unit. The initial state of the network is also fed into the LSTM unit in order to compute the first output \(h_{1}\). It updates cell state \(c_{1}\) as well. At time step \(t\), the corresponding LSTM unit takes the current state of the network ( \(c_{t-1}\), \(h_{t-1}\)) and time step \(X_{t}\) from training example to generate the output \(h_{t}\) and update the cell state \(c_{t}\). After passing the examples from the network, final outputs for day-ahead SI are generated as \(G_{1,...}G_{T}\).
## 2 Materials and Methods
### GC-MS Analysis of Silylated Primary Metabolites in C. sativus and L. siceraria Fruits  
100 Mg of finely freeze-dried powdered sample (for both fruits) was extracted with 5 mL 100% methanol with sonication for 30 min using Branson CPX-952-518R set at 36 \({}^{\circ}\)C, (Branson Ultrasonics, Carouge, SA Switzerland.) and with regular shaking, followed by centrifugation (LC-04C 80-2C regen lab prp centrifuge, Zhejiang, China) at 12,000\(\times\)\(g\) for 10 min to eliminate debris. For evaluation of biological replicates, 3 independent samples for each fruit was analyzed under the same conditions. Then, 100 \(\upmu\)L of the methanol extract was kept in opened screw-cap vials and left to evaporate under stream of nitrogen gas until full dryness. For derivatization, 150 \(\upmu\)L of N-methyl-N-(trimethylsilyl)-trifluoroacetamide (MSTFA), previously diluted 1/1 with anhydrous pyridine, was mixed with the dried methanol extract and incubated (Yamato Scientific DGS400 Oven, QTE TECHNOLOGIES, Hanoi, Vietnam) for 45 min at 60 \({}^{\circ}\)C previous analysis using GC-MS. Separation of silylated derivatives was completed on a Rtx-5 MS Restek, Bellefonte, PA, USA (30-m length, 0.25-mm inner diameter and 0.25-m film). Analysis of these primary metabolites followed the exact protocol detailed in [PERSON], [PERSON] et al., and [PERSON], [PERSON] et al. [11; 12].  
Metabolites Identification and Multivariate Data Analyses of Volatile and Non-Volatile Silylated Components Analyzed Using GC/MS  
Identification was performed by comparing their retention indices (RI) in relation to n-alkanes (C8-C30), mass matching to NIST, WILEY library database and with standards if available. Peaks were first deconvoluted using AMDIS software (www.amdis.net, accessed on 23 April 2022) before mass spectral matching. Peak abundance data were exported for multivariate data analysis by extraction using MET-IDEA software ([PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], 2006). Data were then normalized to the amount of spiked internal standard (Z)-3-hexenyl acetate, pareto scaled and then subjected to principal component analysis (PCA), hierarchical clustering analysis (HCA) and partial least squares discriminant analysis (OPLS-DA) using SIMCA-P version 13.0 software package (Umetrics, Umea, Sweden). All variables were mean-centered and scaled to Pareto variance.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 2 Related Works
### Recent Developments in Edge Computing  
Edge computing has emerged as a promising direction for improving location privacy, although it is still typically based on fixed infrastructure. [PERSON] et al. [2] proposed an innovative dual k-anonymity system using edge servers, demonstrating the potential of distributed privacy protection. [PERSON] et al. [9] and [PERSON] et al. [30] further explored edge based caching schemes, showing how distributed storage can enhance both performance and privacy. Although these approaches move away from fully centralized solutions, they maintain dependency on fixed edge infrastructure, which is a limitation our MANET-based approach overcomes.  
Our work advances beyond these existing approaches by implementing the first fully distributed collaborative caching system in MANETs without external repositories. Although previous solutions like [PERSON] et al. [6] relied on external access points and recent work has explored edge servers [2], our approach uniquely combines collaborative caching, privacy protection, and MANET capabilities without fixed infrastructure dependencies. This integration enables robust privacy protection while maintaining system efficiency through peer collaboration, addressing key limitations of previous approaches. By eliminating reliance on fixed infrastructure while preserving the benefits of distributed collaboration, our solution provides a more flexible and privacy-preserving framework for location-based services.
## 2 Concepts of Clustering Approaches
### Cluster Head and Clustering Operations  
Clustering systems divide sensor networks into many groups. Each group elects a specific node as a cluster head to represent the group in the data sending process, and the remaining nodes are regular nodes [1].  
The rule in operations of clustering considers several rounds. Every round comprises two stages; the preparation stage and the steady-state stage. The first stage involves a mass formation and a cluster head (CH) election process, and the second stage performs the process of data transmission. The CHs' energy consumption generally is high, due to the data collection and transmission process [13]. As the consumption of CHs is high, therefore it is, necessary to rotate this functionality.
## 3 Results
### Ground Reaction Forces and Joint Dynamics
#### 3.1.1 Peak Collision Force  
[PERSON]'s test revealed that the assumption of sphericity was violated for both the main effect of time and the time \(\times\) velocity interaction. Therefore, the Greenhouse-Geisser correction was applied. The results indicated significant main effects of time, with F (3, 117) = 5.23, \(p\) < 0.05, n\({}^{2}_{\rm P}\) = 0.06, and \(1-\beta\) = 0.48; and velocity, with F (2, 78) = 4.18, \(p\) < 0.05, n\({}^{2}_{\rm P}\) = 0.08, and \(1-\beta\) = 0.59; as well as a significant interaction between time and velocity, with F (6, 234) = 3.45, \(p\) < 0.05, n\({}^{2}_{\rm P}\) = 0.09, and \(1-\beta\) = 0.63. Post hoc Bonferroni comparisons revealed a transient decrease in the PCF at 30 s under the high-speed condition; however, this reduction was not statistically significant compared to baseline ([PERSON]'s d = 0.24, 95% CI [\(-\)0.20, 0.68]). At 4 and 12 min, the PCF values were significantly higher than those at 30 s (\(p\) < 0.05), and a significant increase from baseline was observed at 8 min (\(p\) < 0.05, d = 0.54, 95% CI [0.10, 0.98]). No significant changes were detected in the medium- or low-speed conditions. Between-group comparisons indicated that both high- and medium-speed conditions elicited significantly greater PCF responses at 4, 8, and 12 min compared to the low-speed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Intensity** & **Time** & **PCF (N/kg)** & **d vs. Baseline 95\% CI** & **PEF (N/kg)** & **d vs. Baseline** \\ \hline \multirow{4}{*}{Medium speed} & 30 s & 19.64 \(\pm\) 4.4 & 0.25 [\(-\)0.19, 0.69] & 14.08 \(\pm\) 3.04 * & 0.58 [0.14, 1.02] \\  & 4 min & 22.32 \(\pm\) 4.46 & 0.32 [\(-\)0.12, 0.76] & 17.4 \(\pm\) 3.28 *,* & 0.50 [0.06, 0.94] \\  & 8 min & 22.19 \(\pm\) 4.91 & 0.29 [\(-\)0.15, 0.73] & 17.63 \(\pm\) 4.79 *,* & 0.59 [0.15, 1.03] \\  & 12 min & 21.89 \(\pm\) 5.61 & 0.23 [\(-\)0.21, 0.67] & 17.11 \(\pm\) 3.33 *,* & 0.49 [0.05, 0.93] \\  & 30 s & 19.14 \(\pm\) 5.64 & 0.36 [\(-\)0.08, 0.80] & 14.84 \(\pm\) 2.27 & 0.33 [\(-\)0.11, 0.77] \\  & 4 min & 20.13 \(\pm\) 3.79 & 0.07 [\(-\)0.37, 0.51] & 15.53 \(\pm\) 3.04 & 0.26 [\(-\)0.18, 0.70] \\  & 8 min & 20.85 \(\pm\) 4.22 & 0.01 [\(-\)0.43, 0.45] & 15.58 \(\pm\) 3.31 & 0.13 [\(-\)0.31, 0.57] \\  & 12 min & 21.06 \(\pm\) 4.37 & 0.05 [\(-\)0.39, 0.49] & 15.28 \(\pm\) 3.18 & 0.23 [\(-\)0.21, 0.67] \\ \hline \hline \end{tabular} Note. * indicates a significant difference compared to the baseline value; * indicates a significant difference compared to the 30 s mark; PCF: peak collision force; PEF: peak extension force; EP: eccentric power of the knee joint; CP: concentric power of the knee joint; EM: extension moment of the knee joint. Effect sizes (Cohen’s d) are reported with 95% confidence intervals (95% CI). The statistical power (\(1-\beta\)) for main effects ranged from 0.55 to 0.88.  
\end{table}
## 4 Conclusions  
This study presents a 3D excimer laser micromachining system in which the required machined profile is obtained by deliberately exploiting the optical diffraction effect. The relationship between the mask pattern feature size and the laser machining rate has been investigated using a hole-array mask pattern. 3D microstructures with conical, trihedral, and pyramidal surface profiles have been fabricated on polycarbonate substrates. It has been shown that the machined profiles are in good qualitative agreement with the designed profiles in every case. The machined microstructures have a characteristic size of approximately 50 \(\upmu\)m and have been patterned over a machining area with  
Figure 7: Machined pyramidal micro-structures. (**a**) An overall view of the arrayed microstructures, (**b**) designed and (**c**) obtained contours of machined depth, and designed (dash-lines) and machined (solid lines) cross-section profiles along (**d**) x- and (e) y-axes.  
dimensions of 1.2 \(\times\) 1.2 mm\({}^{2}\). In practice, a larger machining area can be easily achieved by stitching individual machining areas.  
The proposed 3D micromachining method has several important advantages over existing excimer laser micromachining systems. For example, since the machining process relies on achieving the optical diffraction effect, the feature size requirement of the hole-array mask pattern is less stringent than that for traditional photo-mask laser machining systems, so the mask can be more easily (i.e., cheaply) made. For the microstructures fabricated in the present study, the minimum line-width requirement for the photo-mask patterns is 2 \(\upmu\)m, which is easily obtained using standard industrial practices. Furthermore, the mask pattern design is straightforward since it is necessary only to code the hole-radius into each element according to the required machining depth at the corresponding location on the substrate surface and the known relationship between the laser machining rate and the hole-area opening ratio. Finally, the proposed method provides a versatile technique for creating 3D microstructures with atypical surface profiles and complex pattern arrangements. For example, the conical, trihedral, and pyramidal microstructures shown in Figures 5-7 can be arbitrarily mixed within a single machining area by simply re-coding the hole-array mask pattern as required. As a result, the proposed method has significant potential for the realization of a wide range of micro-optics, micro-fluidics, and micro-electro-mechanical-system (MEMS) devices.  
[PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of Hydrogen on Silicon-Carbon Nanocomposite  
According to previous research, carbon black can be obtained by plasma pyrolysis of methane [31], while graphene can be further prepared by adding an appropriate ratio of hydrogen [32]. As for the effect of hydrogen, our research group has carried out in-depth research [32], through simulation, it was found that the addition of hydrogen can generate a large number of hydrogen atoms, which can form C-H bonds with carbon atoms on the edge of the carbon cluster, resulting in the decrease of carbon dangling bonds, and delaying the formation of spherical particles in the carbon cluster. Related studies have reported that the formed hydrogen atoms can react with the carbon cluster and form bonds, terminating the carbon-centered free radical to form sheet-like carbon materials [33]. On this basis, adding hydrogen is expected to prepare a new composite structure containing tremelliform carbon nanosheets, which could effectively buffer the volumetric changes of Si nanoparticles. Therefore, the influence of the hydrogen flow rate on the properties of the silicon-carbon nanocomposite was investigated under the conditions of a DC input voltage of 8000 V, a carrier gas flow of 6000 mL/min, silane flow rate of 120 mL/min, and methane flow rate of 360 mL/min.  
Figure 4 shows the SEM images of the composites prepared under the different rates of hydrogen to methane (0:1, 1:20, 1:10, 1:5, 1:3, and 1:2). As shown in Figure 4a, without the addition of hydrogen, the products are mainly spherical carbon and silicon nanoparticles with uneven dispersion. After the hydrogen is injected, the spherical carbon material becomes significantly larger and tends to tremelliform. Meanwhile, the Si nanoparticles and carbon materials are dispersed well (Figure 4b). When the ratio of hydrogen to methane increases, the proportion of the tremelliform carbon increases dramatically (Figure 4c,d). When the ratio of hydrogen to methane increases to 1:3 (Figure 4e), almost all of the carbon materials are tremelliform nanosheets, as the carbon changes from a spherical to a tremelliform nanosheet structure, the Si nanoparticles are dispersed much better among the tremelliform carbon nanosheets. The volume changes of Si nanoparticles can be effectively restrained by the tremelliform carbon nanosheets during the cycles. As the ratio of hydrogen to methane increases from 1:3 (Figure 4e) to 1:2 (Figure 4f), the structure of the composite remains basically unchanged. Because of that, the optimal ratio of hydrogen to methane is 1:3.  
Figure 3: Cyclic performance of the silicon-carbon nanocomposite prepared under different silane/methane ratios at 150 mA g\({}^{-1}\) between 0.01 and 1.5 V.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
###### Abstract  
Generalized frequency division multiplexing (GFDM) with offset quadrature amplitude modulation (OQAM) is an alternative non-orthogonal modulation scheme for future generation wireless broadband systems. The nonlinearity of high power amplifiers (HPAs) has a very significant effect on the performance of GFDM systems. In this paper, we investigate the effects of nonlinear distortion on the multiple-input multiple-output (MIMO)-GFDM system when the signal is passed the HPA, which is modeled with amplitude and phase distortion. The effects of nonlinear distortion due to the HPA include amplitude distortion, phase distortion, and the spread of signal constellations. These effects also produce harmonic signals and intermodulation outside the frequency band which results in spectral spread. This will then reduce the performance of the MIMO-GFDM system. The contributions of this paper concern three key areas. Firstly, we investigate the effects of nonlinear distortion on the MIMO-GFDM system. We also derive the new closed-form expression bit error rate (BER) in MIMO-GFDM systems that use a memoryless HPA, which is modeled using the Saleh model when passed through the additive white Gaussian noise (AWGN) channel. This model was chosen because it is simple and has AM/AM and AM/PM curves. Secondly, we propose the application of techniques for the linearization of each HPA predistorter on the transmitter side of the MIMO-GFDM system separately. This predistorter is able to compensate for nonlinear distortion caused by the HPA without memory operating in the saturation region. The main contribution of this paper is to investigate the predistorter, which can linearize nonlinear distortion in MIMO-GFDM transmitters. The performance of the proposed scheme is evaluated in terms of spectrum analysis, PAPR analysis, a constellation diagram, and bit error rate (BER) analysis. The simulation results show that the proposed predistorter design succeeds in compensating for nonlinear distortions caused by the HPA for large input back-off (IBO) values.  
generalized frequency division multiplexing; predistorter; high power amplifier; multiple input multiple output; Saleh model +
Footnote †: journal: Electronics Electronics
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 7 Measurement Result and Discussion
### Link Budget  
The six times multiplier on the radar side gives an output power of 10 dBm. The antenna connected to it is a horn antenna (AINFO LB-12-25-A, Chengdu, China) that produces a 25 dBi gain. At the tag side, another slot array antenna provides another 22 dBi gain. The distance between the tag and the radar is around 65 cm, which introduces around 66.4 dB of free space path loss. Thus, the received LO signal at the tag side in our case is around \(-9.4\) dBm. After using an amplifier (QuinStar QPI-W01128, Torrance, CA, USA) and a 3 dB power splitter, the LO power used for the tag is around 8 dBm without considering cable loss.  
A pair of I and Q channel is used for each tag with an input power of \(-10\) dBm at each channel. The conversion loss is around 30 dB at the frequency that we used. The RF output power from the tag is around \(-37\) dBm. Antennas in use for two tags have 25 dBi and 20 dBi gain, respectively. On the radar side, the slot array antenna provides 22 dBi gain. The received signal power at the radar side from each tag is \(-56.4\) dBm and \(-61.4\) dBm, respectively.
## 3 AI Techniques and Their Applications
### Hybrid and Multimodal Approaches
#### 3.3.1 Physics-Informed AI Models  
Physics-informed neural networks (PINNs) and similar architectures incorporate constraints derived from Maxwell's equations or material models into the loss function of AI models. This improves interpretability and ensures physically plausible outputs, particularly when utilizing sparse or noisy data. These examples include estimating permittivity profiles using AI models regularized by way of electromagnetic theory, constraining depth estimates in layer detection based on known propagation velocities, and so on [53; 54].  
These methods are highly relevant for geotechnical monitoring, where physical consistency is essential.  
Figure 4: Model architecture comparison: CNN (classification), U-Net (segmentation), and YOLO (object detection) adapted for GPR applications. Each architecture follows a distinct design tailored to specific tasks: CNNs classify patterns (e.g., hyperbolas) in B-scans, U-Net performs pixel-wise segmentation of subsurface features, and YOLO detects and localizes objects by predicting bounding boxes and class labels in a single pass.