esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Error Adaptation  
When we set a MAEdef, we can get the segments and the actual MAE with the OML-PLAC method. However, MAE is usually not the smallest AE for the same number of segments, so we proposed an error adaptation method (EA).  
EA can ensure that the error is controllable when the MAEdef and the maximum number of segments are met. It means that the difference between the final AE and ideal AE as a percentage of MAEdef is controllable.  
The flow diagram of the EA method is shown in Figure 4 and the contents of the yellow boxes in the process represent the operations in Figure 3. EA steps have been briefly described in Section 1. EC_min and EC_max are, respectively, the error bisection window's left and right pointers, and each iteration defines the middle of the error bisection window as the new MAEdef. The cycle_num is the user-defined number of iterations, and the value determines the width of the error bisection window. The final width of the error bisection window is less than (MAEdef \(\times\) 2-cycle_num), so the percentage is (2-cycle_num \(\times\) 100%). When we want to control the percentage, the cycle_num value can be reversed. For example, if we control the percentage at 1%, the value of cycle_num is 7. We add a column in Table 3 to calculate the final MAE of the four functions adjusted by EA (MAEEA). EA process not only reduces the final MAE but has little impact on hardware performance because the number of segments is not changed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
## 3 Ultra-High Spatial Resolution (<2 mm) Clinical PET Systems
### Breast-Dedicated PET Systems  
Breast-dedicated PET systems can achieve superior spatial resolutions by employing fine crystal elements and reducing the separation of opposing detectors compared to brain-dedicated PET systems; this smaller detector separation reduces the spatial resolution blurring from photon non-collinearity, but perhaps even more importantly, boosts geometric efficiency, enabling the realization of the desired improved resolution in terms of SNR in the reconstructed images.  
Researchers at M.D. Anderson Cancer Center developed a positron emission mammography (PEM) camera composed of two planar detector modules [54]. Each module contains 60 photomultiplier (PMT)-Quadrant-Sharing (PQS) LYSO blocks, incorporating two types of crystal sizes, 1.5 \(\times\) 1.5 \(\times\) 10 mm\({}^{3}\) and 1.5 \(\times\) 1.9 \(\times\) 10 mm\({}^{3}\), to minimize dead spaces. Due to the dual-panel geometry, the spatial resolutions depend on the distance between the two detector heads, with reported resolutions ranging from 1.19 mm to 4.82 mm along different axes at three different bank-to-bank distances (50, 70, and 90 mm).  
Shimadzu Co. constructed a high-resolution, ring-shaped, breast-dedicated PET scanner using detectors with four-layer DOI capability [55]. Each detector block consists of four layers of 32 \(\times\) 32 LGSO (LU1.8 Gd0.2 SiO5:Ce) crystal arrays, with 1.4 \(\times\) 1.4 \(\times\) 4.5 mm\({}^{3}\) crystal elements, coupled to a position-sensitive PMT (PS-PMT). A unique reflector arrangement was applied to extract DOI information from the stacked crystal arrays [59]. Reported spatial resolutions for FBP reconstruction were 1.6 to 2.7 mm radially, 1.7 to 2.2 mm tangentially, and 2.0 to 2.2 mm axially. With iterative algorithm reconstructions, spatial resolutions were reported to be 0.8 to 1.3 mm radially, 0.8 to 1.0 mm tangentially, and 0.8 to 1.0 mm axially.  
The MAMMI breast PET, developed by Oncovision, Inc., also features a ring geometry and incorporates DOI-capable detector modules, each consisting of 40 \(\times\) 40 \(\times\) 10 mm\({}^{3}\) monolithic LYSO crystals coupled to PS-PMTs [60]. Using 3D MLEM reconstruction, spatial resolutions of 2 mm, 1.9 mm, and 1.7 mm were reported in the radial, tangential, and axial directions, respectively.
## 3 Validations of the Two-Phase and Porous Media Model
### Geometries of the Perforated Sieve Trays of the Small- and Full-Scale FGD  
The perforated hole structure on a sieve tray is illustrated in Figure 5 from the top view (left) and side view (right). For the small-scale tower, the important parameters required to determine the inertial loss coefficient \(C\) in Equation (16) are listed in Table 1 for different cases and phases. The hole diameter \(D_{h}\) is 20 mm, the tray thickness \(t_{h}\) is 15 mm, and the pitch \(P_{h}\) is 28.8 mm. With the aligned layout shown in Figure 5, the hole diameter \(D_{h}\) and pitch \(P_{h}\) can determine the porosity \(f_{h}\) as 34.3%. The hole Reynolds number for gas phase \(Re_{h,g}=\rho_{\mathrm{S}}V_{\mathrm{B}}D_{h}/f_{h}\mu_{\mathrm{g}}\) is 5130, where \(\rho_{\mathrm{S}}\) and \(\mu_{\mathrm{g}}\) are the density and dynamic viscosity of the gas phase, and \(V_{\mathrm{S}}\) (1.4 m/s) is the cross-sectional average velocity of the gas phase within the tower. \(L/G\) is defined as the mass flow rate ratio of the total liquid to the exhaust gas. There are two setups for the small-scale tower, and this value varies as 4.9 or 3.2 for the  
Figure 4: This figure highlights the details of the large-scale FGD tower. The unit is meter.  
Figure 3: This figure displays (**a**) details of the small-scale FGD tower and (**b**) the layout of the nozzle arrays from the top view with the sampling locations for the SO\({}_{2}\) concentration. The unit is meter.  
small-scale tower, namely, Case 1 and 2. For Case 1, the velocity of the liquid solution is 0.766 m/s from each spray nozzle with a diameter of 0.01 m, and the velocity of Case 2 is 0.511 m/s. Similar to gas phase, the hole Reynolds number for liquid phase \(Re_{h,l}\) is 860 and 570, respectively.  
Each sieve tray has 308 holes for the small-scale tower. By using the porous media model in Equation (16), these aforementioned parameters are used to determine the inertial loss coefficient to replace the detailed perforated structures. As for the tray of full-scale tower, its perforated hole is 8 mm. The porosity \(f_{h}\) is 27.7%, \(t_{h}/D_{h}\)=0.75, the hole Reynolds numbers \(Re_{h}\) for liquid and gas phase are 5500 and 940, respectively, which are all similar to those of Case 1 in the small-scale tower. As for the number of holes, there are 200,000 holes at each sieve tray for the large-scale tower.
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 3 Importance of Carbon Nanomaterials for the Corrosion Mitigation  
Carbon materials and composites, mostly carbon fibres, are intensively used in spacecraft manufacturing for the production of, e.g., re-entry vehicle nose tips, wing leading edges, rocket nozzles and aircraft brake disks [59]. Carbon nanomaterials have exceptional mechanical, electrical and thermal properties, as well as high chemical stability, making them quite promising for protecting against corrosion in space [60]. Moreover, carbon nanomaterials can be incorporated into various matrix materials which can then be applied to metal surfaces as a coating, providing a protective barrier preventing the attachment of corrosive species [61]. The ability of carbon nanomaterials to improve the adhesion and durability of protective coatings is another factor that makes them promising for space applications where long-term performance and reliability are critical [62]. Carbon nanotubes (CNT), multiwall carbon nanotubes (MWCNT), diamond-like carbon [63; 64], carbon nanorods, nanocones and nanofibers are some examples for the carbon nanomaterials used in space applications [65; 66]. Below, we will discuss several specific applications of carbon nanomaterials for space materials.
## 4 Experimental Validation and Analysis
### Experimental Verification  
The effectiveness of the proposed method was verified in the aircraft wing-body assembly process, as shown in Figure 7. The left wing box of the aircraft was scanned using a handheld scanning device, and the point cloud data of the wing box was obtained, as shown in Figure 8. The original point cloud data contain about 1.04 million data points and contain a lot of noise, and are difficult to analyze and align.  
Figure 6: Analysis of noise filtering result by adding different percentages of noise.  
Figure 7: Measurement experiments during wing-body assembly.  
The proposed method was used to analyze the wing box point cloud data. The point cloud resampling results are shown in Figure 9, and the point cloud denoising results are shown in Figure 10. In the process of point cloud resampling, the data volume is reduced to 5% of the original, and it can be seen from the figure that the proposed method retains the edge features of the point cloud well, and the denoising results after resampling also show that the proposed method eliminates the point cloud noise.  
In terms of analysis time, the time cost is extremely high for the analysis of the original point cloud. The analysis of point cloud data by a workstation equipped with i7-10700K and 32 GB memory takes more than 3 h to align, but the overall time of the proposed method is less than 5 min, which can greatly improve the efficiency of aircraft assembly.  
Furthermore, as shown in Figure 11, we align the wing point cloud data with the theoretical numerical model to obtain the deviation between the wing box area and the numerical model, which provides a data basis for the evaluation and prediction of the gap between the wing and the central wing box.  
Figure 8: Original point cloud data of the wing box.  
Figure 10: The point cloud denoising result of the wing box.  
Figure 9: The point cloud resampling results of the wing box.
## 3 Materials and Methods
### Data Augmentation Strategies  
To address the challenges posed by the high-noise environment of air-ground communication, this study incorporates three data augmentation techniques--speed perturbation, noise suppression, and spectral masking--aimed at enhancing the accuracy and robustness of speaker identification. These methods are specifically applied to the training data to improve the model's ability to generalize across diverse and noisy conditions, while the testing data remains unaltered to evaluate the model's performance under more realistic  
Figure 3: Diagram of the sub-center AAM-Softmax loss function calculation.  
conditions. To further assess the model's generalization capability across different datasets, data augmentation is also employed during the training phase for the publicly available datasets used in the evaluation process, as described later in the study. These datasets, including TIMIT, LibriSpeech, and VoxCeleb1, and CN-Celeb are subjected to the same augmentation techniques during training.  
Speed augmentation involves accelerating or slowing down the speech playback speed. By adjusting the speech speed, diverse training samples are generated to help the model adapt to the speaker characteristics at different speaking speeds in real-world applications. Speed perturbation is implemented using the fmpeg toolkit in Python 3.7 to perturb the speech data's speed, generating multiple versions of the speech signal with speed factors of 0.9, 1.0, and 1.1 to simulate variations in speech features under different speaking speeds.  
Considering that real air-ground communication speech already contains significant noise, instead of using common noise addition methods, noise suppression is implemented using a U-Net model. This approach reduces background noise while appropriately attenuating the speech intensity, thereby enhancing the model's robustness under low speech intensity conditions.  
The core idea of spectral masking is to perform data augmentation in the spectral domain. Unlike traditional methods that directly manipulate the audio waveform, spectral masking processes the spectrogram of speech in the feature space. Specifically, the Fbank features extracted from the audio are first normalized and then masking operations are performed in both the time and frequency domains. By randomly masking the time and frequency of the audio features in the training set, more diverse and uncertain training samples are generated. This augmentation method effectively improves the model's adaptability to noise and variations while reducing overfitting, providing more robust feature representations for air-ground communication speaker identification tasks.
## 7 Conclusions  
This paper presents the design and implementation of a parallel DMAS algorithm using the Spark framework on a standalone PC and on an Eddie cluster. The Spark framework distributes the data needed for image reconstruction across several worker nodes. The most computationally intensive part of the DMAS is computed in parallel on separate worker nodes and then retrieved to a master node to aid faster computation. The parallel version of DMAS is compared with sequential and multiprocessing-based DMAS. Computational experiments demonstrate the superiority of the proposed algorithm over the widely used sequential version of DMAS as well as on Python multiprocessing. The proposed algorithm is generalisable and can be implemented on any cluster with a master-slave architecture. The algorithm is best suited to being deployed on the cloud. This paper shows the potential use of big data frameworks such as Spark as an accelerator for medical image processing.  
The proposed algorithm is tested on homogeneous nodes. In the future, the proposed parallel algorithm will be tested on heterogeneous computing nodes. These big data frameworks can also be utilised for tomography-based and data-adaptive algorithms. The computationally intensive and independent process in these algorithms can be parallelised using Spark to improve their performance.  
Conceptualisation, R.U.; methodology, R.U.; software, R.U.; validation, R.U.; formal analysis, R.U. and T.A.; writing--original draft preparation, R.U.; writing--review and editing, R.U. and T.A.; supervision, T.A.; project administration, T.A. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data available on request from the authors.  
This work made use of the resources provided by the Edinburgh Compute and Data Facility ECDF ([[http://www.ecdf.ed.ac.uk/](http://www.ecdf.ed.ac.uk/)]([http://www.ecdf.ed.ac.uk/](http://www.ecdf.ed.ac.uk/)), accessed on 22 February 2021).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HFC & High-Performance Computing \\ GCP & Google Cloud Platform \\ RF & Radio Frequency \\ AWS & Amazon Web Services \\ DAS & Delay and Sum \\ DMAS & Delay Multiply and Sum \\ PDMAS & Parallel Delay Multiply and Sum \\ RDD & Resilient Distributed Datasets \\ \end{tabular}
## 5 Data Analysis and Results
### Results Using Sweep Signal  
System identification with a sweep signal provides compact SINDy models, each with two identified parameters, \(\zeta_{2}\) and \(\zeta_{3}\). The excitations have three levels with the peaks 5.4 N, 10.6 N, and 21.6 N in forces. Under these different settings, the identified coefficients match well with the results from the experiments with impulse excitations, as shown in Table 6.  
Associating the identified coefficients \(\xi_{2}\) and \(\xi_{3}\) with the excitation force, it was found that with higher excitation strength the identified coefficients \(\xi_{2}\) and \(\xi_{3}\) decrease slightly. At the same time, the coefficient of determination \(\mathrm{R}^{2}\) decreases and the relative errors increase compared to theoretical values. Figure 18a,b shows the linear dependencies of the coefficients \(\zeta_{2}\) and \(\xi_{3}\) on the maximum excitation strength \(F_{max}\). The lines through the data points were analyzed using linear regression. The estimated linear relationship is given by the following equations:  
\[\xi_{2} =-(46263+52F_{max})\ \mathrm{s}^{-2}, \tag{26}\] \[\xi_{3} =-(20.90+0.037F_{max})\ \mathrm{s}^{-1}. \tag{27}\]  
A possible explanation for this lies in the numerical integration, because zeroing out a data point in a Fourier series corresponds to subtracting a sine signal with the same frequency, amplitude, and phase from the time domain. The time series of velocity and displacement can thus be compressed. Another potential reason is the absence of terms with higher polynomial orders. The verification of these hypotheses and the improvement of the numerical integration can be investigated in further work. Overall, compared with the impulse excitation, the identified governing equation using sweep signals shows a more consistent result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\(F_{max}\) **in N**} & \multicolumn{2}{c}{**Coefficient**} & \multicolumn{2}{c}{**Relative Error**} & \multirow{2}{*}{\(\mathbf{R^{2}}\)} \\  & & \(\xi_{2}\) & & \(\xi_{3}\) & \(\frac{-\xi_{2}-\omega_{3}^{2}}{\omega_{3}^{2}}\) & \(\frac{-\xi_{2}-2D\omega_{3}}{2D\omega_{3}}\) \\ \hline
1 & 5.4 & \(-\)46,556 & \(-\)21.1 & 6.6\% & 26.6\% & 0.9998 \\ \hline
2 & 10.6 & \(-\)46,790 & \(-\)21.3 & 7.1\% & 27.7\% & 0.9997 \\ \hline
3 & 21.6 & \(-\)47,385 & \(-\)21.7 & 8.5\% & 29.7\% & 0.9996 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Identified coefficients by using sweep signal excitation and their errors compared with theoretical values.  
Figure 17: Dependency of \(\xi_{6}\) on the magnitude of the excitation force.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 7 Conclusions  
This paper introduces artificial intelligence tools for IoT applications and a novel framework to enable on-device learning for LoRa-based devices. Machine learning principles can prove to be very beneficial for IoT applications but, due to the challenges imposed by IoT and LoRaWAN ecosystems such as data privacy, low bandwidth, etc., traditional machine learning techniques cannot be efficiently applied. However, a decentralized learning technique known as federated learning can be used while maintaining data privacy. The performance of models trained via traditional machine learning and federated learning principles was analyzed. This analysis proves that, for the representative application, federated learning can be viably employed in place of machine learning. However, federated learning requires independent bidirectional communication. The legacy LoRaWAN protocol fails to satisfy the communication requirements of federated learning; hence, FL-LoRaMAC has been proposed to fill this gap. The performance of the framework was evaluated by implementing the framework on a representative application. The results show that the framework was able to successfully support all the design requirements of federated  
Figure 15: Performance comparison of the trained model with QoS and legacy: (**a**) precision and (**b**) accuracy with the varying total number of devices in network.  
learning, even with communication losses in the system. The proposed framework also incorporates bandwidth optimization to enable efficient resource utilization.  
The proposed framework considers all devices running the application taking part in the learning process. Typically, a subset of these devices possesses a substantial amount of information and holds the capacity to effectively train the learning model. To mitigate potential escalations in computational and communication expenses, a nuanced approach is necessary, wherein the training process is applied to to only a small subset of devices without regard for their information contribution. This will further reduce the communication cost.  
This also leads to an intriguing avenue for further investigation. The development of a selection mechanism can lead to identifying devices based on the richness of their information and the quality of their channel conditions. This aspect remains underexplored at present but bears substantial significance, particularly within the context of federated-learning-driven applications for smart cities. Hence, delving into this aspect could yield valuable insights and enhancements for federated learning in such contexts.  
Conceptualization, S.A. and A.N.; methodology, S.A. and A.N.; software, S.A.; validation, S.A. and A.N.; formal analysis, S.A.; writing--original draft preparation, S.A.; writing--review and editing, A.N.; supervision, A.N.; project administration, A.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 3 Discussions and Outlook
### The Integration of Graphene ECG Electrodes  
Daily health monitoring is a kind of comprehensive physiological information monitoring, which not only requires the integration of ECG electrodes into daily wear, but also requires integrated sensing, transmission, and processing of various physiological information. Multifunctional clothing is a well-integrated carrier for daily health monitoring [91, 92]. In 2021, graphene-based smart clothing integrating four functions, including ECG monitoring, pressure sensing, strain sensing, and sound emitting was proposed [93]. The graphene electrodes and sensors in the clothing can detect ECG signals, breath, movement, and heart rate, which can comprehensively diagnose a person's physical condition, and, when there are health problems, the garment will start sound an alarm with an incorporated graphene acoustic device (Figure 6h). Through the integration of functions, graphene electrodes are not limited to monitoring signals, but become part of a complete daily health monitoring system. Moreover, the nylon-textile-based electrode mentioned in the fabric-based electrodes section above is also a typical case of graphene ECG electrodes integrated into garments [53].  
Figure 6: (**a**) Electrode based on silver nanowire bridging graphene. Reprinted with permission from Ref. [89]. Copyright 2020, Elsevier. (**b**) Styrene-butadiene foam/graphene ECG electrode (**left**) and its SEM image (**right**), scale bar: 300 μm. (**c**) The production of roll-to-roll graphene films and their application in ECG electrodes. Reprinted with permission from Ref. [15]. Copyright 2021, IOPscience. (**d**) The recycled non-woven fabric/graphene ECG electrode. (**e**) The fabrication process, photo, and SEM image of the substrate-free graphene electrode; scale bar: 200 μm. Reprinted with permission from Ref. [26]. Copyright 2020, American Chemical Society. (**f**) The spray-coated nanofiber-based graphene ECG electrode and acquired ECG signal. (**g**) The intelligent graphene nanomesh electrode. Reprinted with permission from Ref. [18]. Copyright 2022, Wiley. (**h**) The graphene-based multifunctional clothing for daily healthcare. Reprinted with permission from Ref. [93]. Copyright 2021, American Chemical Society.