esa-sceva/satcom-chunk-collection huggingface dataset:
# Sirtuin 1 and Hormonal Regulations in Aging
## 3 SIRT1, Hypothalamus, and Endocrine Regulations
### SIRT1 and Other Hormonal Regulations
Similarly, upregulation of SIRT1 mediates the anti-inflammatory effects of melatonin in the aorta and heart of ovariectomized rats [55] and prevents vascular smooth muscle loss in a mouse model of thoracic aortic aneurysm and dissection [56]. In a streptozotocin-induced male Wistar rat model of Alzheimer's disease, melatonin-induced (10 mg/kg applied intraperitoneally (i.p. )), SIRT1-dependent reduction in amyloid-beta accumulation and cognitive decline, suggesting a protective mechanism against neurodegeneration [57]. SIRT1 regulates the activity of angiotensin II (AT-II), a hormone of the renin-angiotensin-aldosterone system that controls water and salt balance and blood pressure. Resveratrol, a natural activator of SIRT1, down-regulates the type I receptor of AT-II in vascular smooth muscle cells, thereby mitigating the hypertensive effects of AT-II in rat model of sporadic Alzheimer's disease induced by streptozotocin [58]. Another study demonstrated that resveratrol suppressed the prorenin receptor-angiotensin-converting enzyme-AT-II axis in the aorta of an aging mouse via pathways involving upregulation of SIRT1 [59]. Prevention of AT-II-induced pro-sensory changes in rodent and human vascular smooth muscle cells by PNU-282987 (a selective \(\alpha\)7 nicotinic acetylcholine receptor agonist) also depends on normal SIRT1 expression [60]. Experiments with cardiomyocytes from fibronectin-type-III-domain-containing, protein 5 transgenic mice (male eight-week-old, 20-25 g), have further revealed that SIRT1 activation prevents AT-II-induced cardiac hypertrophy and fibrosis, emphasizing its role in cardiovascular protection [61]. Furthermore, fibroblast growth factor 21 (FGF21) has been shown to reduce AT-II-induced hypertrophic, apoptotic, and fibrotic changes in mouse hearts via SIRT1-dependent pathways, highlighting the importance of SIRT1 for cardiovascular health [62]. Collectively, these findings illustrate the diverse roles of SIRT1 in the regulation of various hormonal pathways, from glucose metabolism to cardiovascular protection, as well as its potential as a therapeutic target in age-related diseases.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 3 Preliminaries
### Differential Privacy  
Differential privacy is a privacy preserving technique that ensures the overall statistics of a dataset will remain same, regardless of change in a single tuple. For example, any algorithm \(\Lambda\) satisfies \(\epsilon\)-differential privacy (\(\epsilon\)-\(DP\)), if it satisfies the following:  
\[P[\Lambda(D)\in T]\leq\epsilon^{\epsilon}P[\Lambda(\overline{D})\in T] \tag{3}\]  
where \(P\) indicates privacy, \(D\) and \(\overline{D}\) represent any two neighbouring datasets that have only a single different element, \(T\) denotes a set of tuples, and \(\epsilon\) represents the privacy budget. Whereas, the privacy budget \(\epsilon\) is an important factor in differential privacy which ranges from 0 (minimum-\(\epsilon\)) to 1 (maximum-\(\epsilon\)) [14].
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Mechanisms** & **Metaheuristic Algorithms** & **Constraint-Handling Technique** \\ \hline
[5] & four–bar mechanism & Genetic Algorithm (GA) & Penalty Function (PF) \\
[41] & Hand robot mechanism & Pareto Optimum Evolutionary & Feasibility Rules (FR) \\  & & multi-objective Algorithm (POEMA) & \\
[31] & four–bar mechanism & Differential Evolution (DE) & PF \\
[32] & Six-bar mechanism & DE & PF \\
[33] & four–bar mechanism & DE & PF \\
[34] & four–bar mechanism & GA–fuzzy logic (GA-FL) & PF \\
[17] & four–bar and & Malaga University Mechanism & PF \\  & Six-bar mechanisms & Synthesis Algorithm (MUMSA) & \\
[35] & four–bar mechanism & GA, DE, & PF \\  & & Particle Swarm Optimization (PSO) & \\
[36] & four–bar mechanism & Ant-gradient (AG) & PF \\
[45] & four–bar mechanism & GA–DE & PF \\
[6] & Six-bar mechanism & Cuckoo Search (CS) & PF \\
[37] & four–bar mechanism & Imperialist Competitive Algorithm (ICA), & PF \\  & & GA, DE, PSO & \\
[8] & four–bar mechanism & Modified Krill Herd & PF \\
[46] & four–bar mechanism & Teaching-Learning-Based & PF \\  & & Optimization (TLBO), GA, PSO & \\
[38] & four–bar mechanism & Hybrid Lagrange Interpolation DE & PF \\  & & (HLIDE) & \\
[39] & four-bar and & Hybridization DE with Generalized & PF \\  & Six-bar mechanisms & Reduced Gradient & \\
[47] & four–bar mechanism & DE & \\
[48] & four–bar mechanisms & CS, TLBO, DE, MUMSA & - \\  & & auto-adaptive modified DE, & \\  & & combined-mutation DE (CMDE) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of investigations using metaheuristic algorithms and constraint-handling techniques in mechanism synthesis problems in a broad context and for specific rehabilitation purposes.
# Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter
## 4 Experimental Analysis
### Frequency Stability Analysis and Noise Type Identification  
To verify the accuracy and effectiveness of FBPBPF in extracting periodic terms from clock bias data, the main periodic terms were extracted as listed in Table 2. Frequency stability  
Figure 8: The average RMSE of the QP, the SAM and the FBP prediction errors from 1 to 24 h.  
curves and lag 1 autocorrelation function (ACF) noise type [38; 39] were plotted before and after periodic term correction, as shown in Figures 9 and 10. The Allan deviation (with the clock bias trend removed) was used to calculate frequency stability, and the periodic term extraction method was evaluated based on improvements in frequency stability at averaging times of 10,000 s, 20,000 s, and 40,000 s, along with suppression of oscillatory peaks.  
As shown in Figure 9, the Allan deviation curve becomes smoother after the periodic term correction, indicating that removing the periodic terms from the clock bias effectively suppresses the anomalous peaks and oscillations present in the original frequency stability curve. The FBPBPF significantly enhances the stability at each smoothing interval. Specifically, for averaging times of 10,000 s, 20,000 s, and 40,000 s, the frequency stability is improved by an average of 42.7%, 46.6%, and 38.5%, respectively, over the original data. Notably, the frequency stability improvement for C12 is the lowest, likely due to high atomic clock noise. These results show that the FBPBPF effectively reduces oscillatory peaks in the original Allan deviation curve, and the degree of improvement in frequency stability is closely related to factors such as satellite system type, onboard clock type, and satellite orbit.  
As shown in Figure 10, the noise type of the BDS satellite clock changes as the smoothing time increases. However, in general, the BDS satellite clock is mainly affected by white frequency modulation noise, flicker frequency modulation noise, and random walk frequency modulation noise. The BDS satellite clock before the periodic term is extracted has a large noise index in the smoothing time range of 5000 s to 50,000 s. The reason for the large fluctuations is that its clock bias data have a more pronounced periodicity. After the periodic term is eliminated using an FBPBPF, the abnormal fluctuations in the lag 1 ACF noise type in the longer smoothing time range are effectively suppressed, and its original noise characteristics are revealed.  
Figure 9: Allan deviation \(lg\sigma_{y}(\tau)\sim lg(\tau)\) before and after periodic term correction. \(\tau\) is the averaging time interval. The blue line represents the Allan deviation of the original clock bias, the green line represents the Allan deviation of the extracted periodic terms, and the red line represents the Allan deviation after periodic term correction using FBPBPF.
## 7 Outcomes
### RQ2: What Is the Performance of Anomaly Intrusion Detection in IoT Using Deep Learning Techniques?
#### 7.3.5 Training Time  
One of the major problems that existing techniques suffer from is the large and high dimensional datasets used for training [65]. Due to that, more time is needed to train the model for higher accuracy detection. In tackling these issues, deep learning algorithms are proposed because they can work on lesser training duration and dataset. This helpsto increase the efficiency during model training. The batch size may also affect the time consumed in the training phase because of the accumulation of the network onto the weight update [54; 66]. To solve this, multiple layers can be used to build deep learning networks, which facilitates the weighing and recognizing of the set of significant patterns from the datasets. Furthermore, the exploits of storage and processing facilities additionally obstruct the model training time. Dealing with this issue, the adaptation of big cloud-based architecture and data technologies improve the efficiency by reducing the model training duration [63].
# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 2 Related Works  
### Road Surface Temperature Forecast Model  
In order to determine the possibility of black ice formation, a temperature prediction model of the road surface is required. Since the 1980s, several studies [6; 7; 8; 9; 10; 11; 12] predicting road surface temperatures and the occurrence of black ice have been presented. [PERSON] and [PERSON] [6] developed an automated road-ice-prediction model for the first time. This model used automatically collected sensor measurements of surface temperature, air temperature, dewpoint, and wind speed rather than external meteorological data. [PERSON] [7] developed a model of ground heat conduction and energy balance to estimate road surface temperatures for a numerical forecasting system. This system was applied to 200 road station sites to provide a forecast of surface temperature five hours in advance. [PERSON] and [PERSON] [8] also presented a numerical model to forecast road conditions, namely the model of the environment and temperature of roads (METRo). This model combined roadside observations from road weather information systems stations and meteorological forecasts from the operational Global Environmental Multiscale (GEM) model of the Canadian Meteorological Centre for use as input data. [PERSON] [9] developed a road condition hazard map that could predict potential road hazards under various weather conditions. This was achieved by linking real-time meteorological and traffic information based on the accident analysis data for various weather factors. The meteorological data (i.e., precipitation amount, temperature, and average wind speed) were gathered with AWS (Automatic Weather System) and ASOS (Automated Surface Observation System) from the Meteorological Administration's ground weather observation network. In Japan, where climate conditions are similar to those in Korea, several studies on black ice prediction have also been actively conducted [10; 11; 12]. However, the past studies described above lacked consideration of hillshade areas caused by the 3D shape of the topography.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
###### Abstract  
Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net.  
edge detection; capsule networks; point clouds 2021
## 4 Contribution of Thermodynamic Calculations
### Hausmannite Phase Boundaries  
One of the major conclusions of the experimental data compiled in Section 3, is that, for Mn-containing systems, the hausmannite phase formation has to be avoided, because of the poor reversibility of the phase transition during the cooling step of the thermal cycle. As illustrated with the three binary phase diagrams plotted in Figure 7, the binary systems based on Mn must contain a substantial amount (about 0.2 in molar fraction) of copper or iron oxide to make sure that the thermal cycle will not favor the appearance of the hausmannite phase at any temperature. In the case of cobalt additions, the hausmannite phase is stable on a much larger composition range.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Oxide System** & **Phase Diagram** & **Model** \\ \hline Cu-Fe-O & [PERSON], 1977 [53] & [PERSON], 2013 [52] \\ \hline Co-Fe-O & [PERSON], 2013 [54] & [PERSON], 2004 [55] \\  & [PERSON], 2013 [54] \\ \hline Co-Cu-O & [PERSON], 2002 [56] & [PERSON], 2002 [56] \\ \hline Mn-Fe-O & [PERSON], 2009 [57] & [PERSON], 2010 [58] \\  & & [PERSON], 2016 [47] \\ \hline Mn-Co-O & [PERSON], 1985 [49] & None \\ \hline Mn-Cu-O & [PERSON], 1967 [50] & \\  & [PERSON], 2009 [51] & None \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of available phase diagrams and thermodynamic models for the ternary systems Cu-Fe-O, Co-Fe-O, Co-Cu-O, Mn-Fe-O, Mn-Cu-O and Mn-Co-O in air [47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58].  
The mixing of three metal oxides, rarely envisaged in TCES studies [60], can also be evaluated thanks to ternary phase diagrams. The only diagram of this type described in the literature is Mn-Co-Fe-O in air [61], for which the phase boundaries of Hausmannite at 800 and 1000 \({}^{\circ}\)C have been reproduced in Figure 8. This diagram shows that the combination of iron and cobalt additions does not significantly reduce the stability range of the hausmannite phase.  
Apart from the diagrammatic data discussed here, the use of a thermodynamic model is a strong support to explore a larger set of operation conditions. For instance, in the system Mn-Fe-O, equilibrium calculations show that the decrease of oxygen partial pressure in the gas phase is beneficial to the stability of the hausmannite phase (Figure 9). At low oxygen partial pressure, more Fe thus is required to stabilize the spinel phase.  
Figure 8: Composition domain containing the Hausmannite phase in the ternary system Mn-Co-Fe-O in air, at 800 and 1000 \({}^{\circ}\)C (from [61]).  
Figure 7: Mn-rich part of the Mn-Cu, Mn-Co and Mn-Fe phase diagrams in air (replotted from [49; 51; 57]).
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 3 Elastography Techniques
### B: Shear Wave Imaging  
Shear wave imaging is an elastography technique that results in the generation of shear waves in perpendicular or parallel dimensions. After applying dynamic stress, researchers estimate tissue elasticity by measuring the shear wave speed qualitatively and quantitatively. Shear wave imaging is currently approached using three various techniques: one-dimensional transient elastography (1D-TE), two-dimensional shear wave elastography (2D-SWE), and point shear wave elastography (pSWE) [39; 40; 41; 42].
## 3 Results and Discussions
### Performance Characterization of Microcapsules
#### 3.2.2 Degree of Crosslinking in Shell Materials  
Regarding the crosslinking free radical polymerization reaction between MMA and DVB, when the degree of crosslinking is low, the shell structure of the microcapsules is relatively loose, lacking mechanical strength, and is prone to rupture. As the degree of crosslinking increases, after reaching a moderate degree of crosslinking, the shell exhibits good mechanical strength and toughness, effectively releasing the repair agent; however, when the degree of crosslinking reaches a high level, both the hardness and strength of the shell significantly increase, making it difficult for the core material to be released. Therefore, to ensure the stability of the microcapsule structure and its self-repairing performance, the shell should possess a moderate degree of crosslinking.  
By comparing the FT-IR absorption peaks of MMA, DVB, and the polymer shell material, it can be confirmed that the polymerization reaction has occurred. As shown in Figure 7, the absorption peak of the C=C double bond in the monomers significantly weakens in the polymer, indicating that most of the double bonds have participated in the polymerization and crosslinking reactions. The carbonyl1 absorption peak of MMA shifts slightly from 1720.2 cm\({}^{-1}\) to 1733 cm\({}^{-1}\), indicating a change in its chemical environment during the reaction, which further supports the formation of the polymer. However, a residual absorption peak at 1606.3 cm\({}^{-1}\) remains in the FT-IR spectrum of the polymer, showing the presence of unreacted double bonds, suggesting that the crosslinking reaction is not fully complete. The characteristic peaks of the carbonyl (C=O) and ester (C-O) groups in MMA remain unchanged during the reaction and can still be observed in the polymer's spectrum, indicating that the reaction primarily occurred at the vinyl groups, while the ester groups did not participate. Overall, the polymerization reaction has been successfully carried out, with some unreacted double bonds remaining, resulting in a moderate degree of crosslinking that meets the requirements for microcapsule crosslinking.  
Figure 6: Particle size distribution of microcapsules under optimal preparation process parameters.
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 4 SQI for cPPG
### Deep Learning  
Deep learning methods have been employed in the field of PPG signal quality assessment. Feature extraction is integrated in the architecture of a deep neural network (DNN) [38]. It allows one to regroup several quality assessment algorithms into one deep learning architecture [41; 54; 91] and gives higher freedom for the input passed to those networks [40; 54; 55; 92]. Figure 7 illustrates typical methods applied to the quality assessment. [PERSON] et al. [54] converted PPG pulses into 2D images using a recurrent plot and then classified each pulse into good or bad quality using two 2D convolutional neural networks (CNNs) with max dropout and a dense layer with softmax activation (Figure 7a). [PERSON] et al. [92] decomposed each 6 s PPG signal into 78 approximation coefficients using DWT. A 1D autoencoder (with CNN and bidirectional Long Short-Term Memory (LSTM) layers) is trained to reconstruct clean PPG signals. Because the network is only trained on clean signals, the reconstruction of the signal fails for PPG with bad quality and the mean square error between the input and the rebuilt signal is high. If this error is over a threshold, then the signal is considered as bad-quality (Figure 7b). Figure 7c illustrates the method implemented by [PERSON] et al. [56]. The authors trained an autoencoder (UNet with residual blocks) to perform quality assessment as a segmentation task. They assign a score between 0 and 1 to each point of a 30 s PPG signal, which allows a precise estimation of the quality. Then, they evaluate the performance of their trained network with the DICE score (ratio between the intersection of portion of signal estimated and annotated as artifact over the portion of signal estimated or annotated as artifact).
## 5 Experiments and Results
### Result on UrbanSound8K Dataset  
We evaluated a series of deep learning models on the UrbanSound8K dataset to benchmark their performance in environmental sound classification. The results are summarized in Table 3. Traditional models such as TDNN and LGTFB-EN-CNN achieved accuracies of 85.36% and 85.80%, respectively, demonstrating strong baseline performance under shallow architectures. With increasing model depth, LSTM and TFCNN showed significant improvement, attaining 90.15% and 92.68%, indicating that temporal modeling contributes positively to capturing sequential acoustic patterns.  
ResNet-based models yielded even more promising results. ResNet50 achieved a notable accuracy of 94.73%, outperforming previous models. When lightweight channel attention modules were incorporated, ResNet-ECA and ResNet-SE reached accuracies of 94.17% and 93.59%, respectively, maintaining high performance with minor fluctuations.  
Our proposed ResNet-TF model, which integrates a TF attention mechanism, achieved the highest accuracy of 95.42% on the UrbanSound8K dataset. This demonstrates the effectiveness of jointly modeling temporal and spectral dynamics in boosting classification performance. The confusion matrix in Figure 6 confirms that most sound categories were accurately identified, validating the model's robustness and generalization capability on this benchmark dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision Score** & **Recall** & **F1-Score** \\ \hline TDNN [36] & 85.36\% & 86\% & 85\% & 85\% \\ LSTM [25] & 90.15\% & 92\% & 90\% & 90\% \\ Bi-LSTM+Bi-GRU [26] & 93.00\% & 94\% & 93\% & 93\% \\ DNN [45] & 94.50\% & 95\% & 94\% & 95\% \\ ResNet50 & 94.73\% & 95\% & 95\% & 95\% \\ ResNet-ECA & 94.17\% & 93\% & 94\% & 94\% \\ ResNet-SE & 93.59\% & 94\% & 91\% & 92\% \\ ResNet-TF & 95.42\% & 96\% & 95\% & 95\% \\ \hline \hline \end{tabular} The data mentioned follows the format of prior works [26; 43].  
\end{table}
Table 3: Classification result of UrbanSound8K dataset.