esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 2 Dataset and Data Processing
### TenGeoP-SARwv Dataset  
TenGeoP-SARwv is a comprehensive dataset containing over 37,000 labeled SAR images (20 km \(\times\) 20 km) captured over the ocean [50], each associated with ten well-defined geophysical phenomena described in Figure 2 that are frequently observed and expertly categorized. This dataset holds significant utility for researchers and practitioners engaged in the fields of oceanography, meteorology, and remote sensing, as well as those involved in deep learning endeavors. Additionally, this dataset comprises 16,068 imagettes acquired in WV1 mode and 21,485 imagettes acquired in WV2 mode. Each imagette within this dataset is annotated to correspond to ten geophysical phenomena, encompassing both oceanic and meteorological characteristics. For instance, Figure 2 demonstrates ten conventional geophysical phenomena from Figure 2a-j, including atmospheric fronts (AF), biological slicks (BS), icebergs (IB), low-wind areas (LWA), microconvective cells (MCC), oceanic fronts (OF), pure ocean waves (PW), rain cells (RC), sea ice (SI), and windstreaks (WS). Furthermore, data quality assurance is meticulously conducted through a two-stage process. Initially, the raw data undergo transformation through the application of the European Space Agency's developed nominal calibration method, resulting in the generation of the normalized radar cross section (NRCS). Secondly, the NRCS executes recalibration, employing the CMOD5n model function [51] to mitigate the impact of incidence angle variations.  
This dataset is generously supplied by the French Research Institute for the Exploitation of the Sea (IFREMER). It is available at [[http://www.seanoe.org/data/00456/56796/](http://www.seanoe.org/data/00456/56796/)]([http://www.seanoe.org/data/00456/56796/](http://www.seanoe.org/data/00456/56796/)) (accessed on 7 February 2024). Figure 3 displays the distribution of the TenGeoP-SARwv dataset in a 5\({}^{\circ}\times 5^{\circ}\) global spatial grid. Although this SAR dataset includes the majority of WV acquisition such as the Pacific, Indian, and South Atlantic oceans, the SAR image density in each region is quite low, considering that the largest number of WV images is less than 40. The task of recognizing multiple oceanic phenomena can be easily hindered by this lack  
Figure 2: Ten illustrative instances of expertly delineated geophysical phenomena in the form of imagettes: (**a–j**) show atmospheric fronts (AFs), biological slicks (BSs), icebergs (IBs), low-wind areas (LWAs), microconvective cells (MCCs), oceanic fronts (OFs), pure ocean waves (PWs), rain cells (RCs), sea ice (SI), and windstreaks (WSs), respectively.  
of high-quality data. To satisfy the requirement of training an effective classification model, these imagettes are chosen with the criteria that one geophysical phenomenon dominates with its special characteristics. It is worth mentioning that the format of SAR images is important for visual interpretation and dynamic detectability.
## 3 Wave Equations and Theoretical Wave Velocities
### Infinite Permeability  
For the case of infinite permeability, the pore fluid flow through the solid porosity without any obstacle and there is no viscous coupling between the pore fluid and the solid skeleton in saturated soils. The properties of saturated soils are similar to the single-phase soils. In Equations (8) and (9), the permeability is set to \(k=\infty\) and the relation \(\rho_{m}\approx\rho_{f}/n\) is satisfied. Substituting the relation of \(k=\infty\), Equations (8) and (9) is transformed into:  
\[(\lambda+G)\
abla\
abla\cdot\mathbf{u}+G\
abla^{2}\mathbf{u}+\omega^{2}\Big{(}\rho-n \rho_{f}\Big{)}\mathbf{u}-(\alpha-n)\
abla p=\mathbf{0} \tag{31}\]\[\
abla^{2}p+\frac{\omega^{2}\rho_{f}}{nQ_{b}}p-\omega^{2}\Big{(}\rho_{f}-\frac{ \alpha}{n}\rho_{f}\Big{)}\
abla\cdot\mathbf{u}=0 \tag{32}\]  
Using the similar derivational process of the case of the finite permeability, the wave numbers and velocities of P1, P2, and S waves in infinite permeability cases, respectively, are:  
\[\left\{\begin{array}{l}\left(\frac{l_{P1,2(k=\infty)}}{\omega} \right)^{2}=\left(\frac{1}{C_{P1,2(k=\infty)}}\right)^{2}=\frac{\rho_{1,2(k= \infty)}}{\lambda+2G}\\ \left(\frac{l_{S(k=\infty)}}{\omega}\right)^{2}=\left(\frac{1}{C_{S(k=\infty )}}\right)^{2}=\frac{(1-n)\rho_{s}}{G}\end{array}\right. \tag{33}\]  
where,  
\[\begin{array}{l}\rho_{1,2(k=\infty)}=\rho\Big{(}b\mp\sqrt{b^{2}-ac}\Big{)} \\ a=\frac{\lambda+2G}{Q_{b}}\\ b=\frac{1}{2\rho}\bigg{(}\rho-2\alpha\rho_{f}+\frac{\lambda+2G+ \alpha^{2}Q_{b}}{Q_{b}}\frac{\rho_{f}}{n}\bigg{)}\\ c=\frac{\rho_{f}}{n\rho}-\bigg{(}\frac{\rho_{f}}{\rho}\bigg{)}^{2}\end{array} \tag{34}\]  
From Equation (33), we can see that there are two compressional waves and one shear wave in saturated soils in the case of infinite permeability. The forms of the wave equations of P1, P2 and S waves are same with Equations (27) and (28).
# Performance Analysis of Hybrid Kinematic Mechanism for Fusion Reactor Maintenance
## 2 HKM Kinematic Analysis  
The simplified geometry of the HKM studied in this paper is shown in Figure 2, which contains a parallel mechanism and a series mechanism. The parallel mechanism consists of the classic Tricept (3 UPS-PU) mechanism, which contains a kinetic platform, three supporting pistons, a follower prismatic chain, and a static platform. Considering the constraints of the DEMO vacuum vessel, the three support pistons are arranged in an equilateral triangle configuration, and the structural design parameters are shown in Table 1. Since the vacuum vessel does not allow any oil contamination, a motor-threaded drive system is used to achieve motion control of each support piston of the parallel mechanism. By controlling the stroke of the three support pistons, the kinetic platform \(\mathbf{B}_{1}\), \(\mathbf{B}_{2}\), \(\mathbf{B}_{3}\) can be rotated along the \(x_{1}\) and \(y_{1}\) axes at the point \(\mathbf{P}\) of the static platform. At the same time, they can be moved along the \(z_{2}\) axis on the point \(\mathbf{O}\) of the kinetic platform. Since the 3 UPS-PU parallel mechanism has only three degrees of freedom, it cannot meet the requirement of the end-effector for any orientation adjustment in any position. Therefore, a series mechanism with three axes of rotation is fixed to the parallel mechanism kinetic platform to form the whole HKM, which can meet the requirement of orientation adjustment in any position.
## 4 Conclusions  
In this paper, three configurations with different grating parameters, including diffraction efficiency and groove density, were investigated. It was demonstrated that the external-cavity laser performance can be improved by increasing the groove density of grating in the Littrow configuration. For configuration A, the SMSR was relatively low. The SMSR was improved significantly in configuration B, which may be due to the polarization mismatch between the polarization direction of the incident light and the grating. When the grating groove density was increased appropriately, as shown in configuration C, the laser performance including SMSR can be further improved.  
Figure 5: Threshold current of the external cavity diode laser at different wavelengths with configuration A, B, and C.  
Figure 6: The output power of the ECDL was measured with the change in wavelength for the (**a**) configuration A and (**b**) configuration C. The injection current was 300 mA.  
The improvement of SMSR is attributed to the larger diffractive angle with a high grating groove. Larger diffractive angle means a larger spot size, which can cover more grating lines and improve the grating resolution. In this way, the noise can be suppressed better and a higher SMSR obtained. At the same time, the tunable range is also increased. When a grating with 1200 lines/mm and a first-order of 91% was used, the SMSR of the ECDL reached up to 65 dB, and more than 50 dB was achieved in most of the tuning spectrum range of the ECDL. The tunable range reached 209.9 nm. The threshold current was 37 mA and the output power was 48.9 mW. We have achieved the highest SMSR, compared with the five references [13; 14; 15; 16; 17], which are listed in Table 2.  
**Author Contributions:** [PERSON]. ([PERSON]) and [PERSON] conceptualized the work. [PERSON], [PERSON], and [PERSON] prepared the experiment platform and equipment. [PERSON]. ([PERSON]), [PERSON], and [PERSON] performed the characterization of the external cavity diode laser. [PERSON]. ([PERSON]), [PERSON], [PERSON] ([PERSON]), [PERSON], and [PERSON] analyzed and prepared the data. [PERSON]. ([PERSON]) and [PERSON] wrote the article; all authors reviewed data and manuscript.  
**Funding:** This research was funded by National Key Research and Development Program of China with Project No. 2018 YFB2201103 and Science and Technology Key Project of Jilin Province with Project No. 20170204013 GX.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results
### Predictive Significance of Genetic and Social Factors on the Risk of Dementia  
Receiver operating characteristic (ROC) analysis was conducted on logistic regression models utilizing various predictors. The impact of genetic factors was evaluated across the entire cohort (n = 867), and the ROC curves are displayed in Figure 2a. The area under the curve (AUC) for the PRS model alone was 0.559, while the inclusion of _APOE-e4_ improved the AUC to 0.604. The AUC for _APOE-e4_ alone was 0.573.  
Since detailed questioning of all patients did not occur, a separate analysis was conducted in a group of 347 individuals (88 CN volunteers and 259 patients with dementia) who had available data regarding the presence of a family, higher education, and intellectual work. This analysis considered the impact of social factors in addition to the PRS  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictor** & **OR** & **95\%CI** & _p-Value_ \\ \hline _APOE-e4_ + Qu1 & 1.56 & 0.89–2.72 & 0.12 \\ \hline _APOE-e4_ + Qu2-3 & 1.78 & 0.89–2.64 & 0.0045 \\ \hline _APOE-e4_ + Qu4 & 3.52 & 2–6.38 & \textless{}0.001 \\ \hline OR, odds ratio; CI, confidence interval; Qu, quartile of PRS. & & & \\ \end{tabular}
\end{table}
Table 2: Association of the _e4_-allele of the _APOE_ gene with dementia depending on the PRS quartile.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictor** & **OR** & **95\%CI** & _p-Value_ \\ \hline _PRS_ (per sd) & 1.26 & 1.09–1.45 & 0.0014 \\ \hline _APOE-e4_heterozygous & 1.81 & 1.31–2.51 & \textless{}0.001 \\ _APOE-e4_homozygous & 7.98 & 2.89–28.11 & \textless{}0.001 \\ \hline _APOE-e2_heterozygous_ & 1 & 0.67–1.49 & 0.99 \\ _APOE-e2_homozygous_ & 2.46 & 0.4–18.99 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Association of PRS and _e_-alleles of _APOE_ gene with dementia.  
Figure 2: Discriminative ability for predictive models with genetic and social factors. (**a**) ROC curves for predictive models with genetic factors: PRS (AUC = 0.559), _APOE-e4_ (AUC = 0.573), PRS, and _APOE-e4_ (AUC = 0.604). (**b**) ROC curves for predictive models with social factors, including family, higher education, and intellectual job (AUC = 0.69), and both social and genetic (PRS and _APOE-e4_) factors (AUC = 0.7343).  
and _APOE-e4_. When these variables were included, the AUC increased from 0.69 to 0.7343 (Figure 2b). The genetic factors demonstrated a statistically significant improvement in the model (Table S2).
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## Appendix A  
### Experimental Results After Applying Electrical Interference Noise  
Electrical interference noise was configured at four levels (Levels 1, 2, 3, and 4) with a fixed frequency of 50 and amplitudes of 0.03, 0.1, 0.2, and 0.5, respectively. The experimental results indicate that the CWRU and MFPT datasets maintained high performance (over 90%), while the PU dataset achieved over 80% accuracy. Across all three datasets, performance degradation increased with higher noise intensity, but the differences between RNN-based and CNN-based approaches were not significant. The GRUAttention model performed the worst on the CWRU dataset, the LSTMAttention model on the MFPT dataset, and the XceptionTime model on the PU dataset. While the TSequencer model narrowly achieved the highest Level 4 precision metric on the MFPT dataset, the MultiPatchTST model consistently outperformed other models on all remaining metrics for the MFPT dataset and on all metrics for the CWRU and PU datasets. These results suggest that patch-based learning and self-attention structures, as implemented in the MultiPatchTST model, are robust to electrical interference noise. These structures enable the retention of critical information even in the presence of significant noise, whereas other models without these features show more pronounced performance degradation. Detailed performance metrics for each model are presented in Tables 1-3, with noise levels converted to Signal-to-Noise Ratio (SNR).
## 8 Future Directions
* **Contrastive learning:** Contrastive learning is an innovative machine learning approach that helps the model adapt to concept drift using pairs of similar and dissimilar data points to continuously update the model, ensuring that it recognizes relevant changes in the data over time. As a future prospect, it can also be used for digital twins to enhance their adaptability and accuracy in reflecting data distribution changes in real-world systems. * **Advanced AI models:** TWIN-ADAPT can be further extended using deep neural network methods such as CNNs, Multi-Layer Perceptrons (MLPs) and Autoencoders for anomaly detection and classification. These models can not only handle large volumes of data but also better generalize across different types of data, thus improving accuracy in digital twin frameworks. Additionally, Physics-Informed Neural Networks (PINNs) [57] can be used to incorporate domain-specific physical properties into neural network architecture, enhancing model robustness in dynamic environments. However, these models require large training datasets and continual learning to stay updated, which often leads to the problem of catastrophic forgetting [58]. Thus, continuous learning approaches are essential to ensure models retain past knowledge while adapting to new data.
## 4 Case Study and Results
### Comparative Analysis
#### 4.3.1 Scenario Impact Summary  
Figure 7 compares total energy consumption across all scenarios for a 5 kg payload, representative of typical urban parcel weights. The energy consistently increases from Scenario 1 to Scenario 4, illustrating the cumulative cost of adding realistic constraints.  
* Scenario 2 (delivery time constraint) results in a **10-15%** increase in energy compared to Scenario 1.
* Scenario 3 (CFD correction) adds or slightly mitigates energy depending on the delivery profile.
* Scenario 4 (early return) yields the highest overall energy consumption, with an up to **26%** increase relative to Scenario 1.
## 4 Privacy-Aware Selection Modes
### Privacy-Aware Selection Modes
#### 4.2.1 Trust Threshold-Based Selection Mode  
This next-hop selection mode is based on defined trust thresholds. Nodes prioritize neighbors above a configured minimum trust level to forward to (see Figure 2a). Indeed, each node calculates a relationship score (e.g., using the number of common friends metric) with each neighbor. This score represents the strength of social ties. Trust thresholds are then defined to determine whether a neighbor is sufficiently trusted to relay messages. A node selects the next hop during message forwarding if its relationship score exceeds the defined trust threshold--a high threshold like 0.8 means picking strongly tied nodes across the entire message path for high privacy. A lower threshold like 0.3 allows the selection of even weak tie neighbors, improving message spread but reducing privacy. The threshold can be tuned based on different social contexts and privacy needs--from allowing only the most trusted friends to relay messages to allowing even strangers to do so. This flexibility helps address diverse real-world scenarios. A simplified trust evaluation using just a single defined threshold to classify neighbors as \"trusted\" or \"not trusted\" allows us to address different social privacy requirements.
## 3 Simulation Environment and RMWBI System
### RMWBI System and Visualization in the Simulation Environment
Time delay (Equation (9)), distance (Equation (10)), speed of the wave (Equation (11)) are shown below respectively:  
\[\Gamma_{n}(\Gamma)=\frac{2}{v\Delta t}\mathrm{d}_{i} \tag{9}\]  
\[\mathrm{d}_{i}=\frac{\sqrt{\left(x-x_{i}\right)^{2}+\left(y-y_{i}\right)^{2}+ \left(z-z_{i}\right)^{2}}}{v} \tag{10}\]  
\[v=\frac{c}{\sqrt{\epsilon_{r}}} \tag{11}\]  
Here, \(\epsilon_{r}\) represents the relative permittivity of the medium and \(c\) represents the speed of light. The signals sent from the antenna to the phantom and reflected back are shown in Figures 8a and 8b, respectively. The tumor response was obtained by applying filtering and DMAS imaging steps to the reflected signals. A small tumor with a diameter of 0.9 mm was dropped into the heterogeneous breast phantom in the simulation environment as shown in Figure 6. As seen in Figure 9, energy profiles of the heterogeneous breast model were created by applying signal processing steps. As shown in Figure 9a, the structured image of the location of the 0.9 mm diameter tumor in the heterogeneous breast phantom in the X-Y plane (top view) was obtained. As shown in Figure 9b, the image of the location of the tumor in the X-Z plane (side view) was obtained. As a result, the location of the tumor placed in the heterogeneous breast model was displayed in the correct position. Figure 7:0 Transmitted and reflected signal display: (**a**) transmitted signal and (**b**) reflected signal. Figure 7:1 Images of a 0.9 mm diameter tumor in a 100 mm diameter hemispherical heterogeneous breast model (skin: 2 mm; fat: 35%; glandular tissue: 61.2%): (**a**) X–Y (top view) planes and (**b**) X–Z (side view) planes.
## 6 Future Research Directions
### Novel Algorithms  
In addition to DL-based algorithms, unsupervised learning algorithms can address user clustering [44]. After learning the inherent frameworks and correlation between users, the expectation-maximization method can be adopted for user clustering in fixed and dynamic environments. NOMA can decrease the aggregation latency in FL model updating by ensuring multiple access in the same channel. In addition, based on well-trained DRL and DL, transfer learning can also be implemented for updating the partial/full neural network parameters with limited time and data.
# Investigation of Titanium Alloy Cutting Dynamics in Thin-Layer Machining
## 2 Methods and Materials  
### Experimental Setup  
Two titanium alloys were used in this study. The first was annealed Titanium Grade 2, which consisted of 100% alpha phase. It is characterized by a hardness of 80 HRB with a modulus of elasticity of 105 GPa and thermal conductivity of 16.4 W/m-K. In contrast, an alloy with a more complex microstructure was selected, Titanium Grade 5, a Ti-6 Al-4V composite. The hardness and modulus of elasticity of this alloy are higher--36 HRC and a modulus of elasticity of 113.8 GPa, respectively. The composition and structure of this titanium alloy cause its thermal conductivity to be lower than that of Ti Grade 2, which is 6.7 W/m-K. Specifications of the chemical and mechanical properties of the titanium alloys are provided in Table 1.  
The cutting insert used was an ISO WNMG080404-MF1, grade CP200, manufactured by SECO Tools. It was made of sintered carbide and coated with a multilayer (Ti,Al)N + TiN coating applied via the Physical Vapor Deposition (PVD) method. This insert was chosen for its suitability for finishing operations, enhanced wear resistance, and compatibility with the geometry of the available turning tool holder (DWLNR-2020-K08). The insert was mounted in the holder, which was then installed in the lathe's turret head.  
The experimental study was carried out on an NEF 400 CNC lathe. The research investigated the orthogonal and oblique turning process under conditions of very low feed rates with a constant cutting speed of 28 m/min and a fixed depth of cut of 300 \(\upmu\)m. Cutting parameters were selected based on a designed experimental plan using Statistica.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{Chemical analysis \%} & \\ \hline  & N & C & H & Fe & V & Al. & O & Ti & Other \\ \hline Ti Grade 2 & 0.01 & 0.02 & 0.001 & 0.06 & 0 & 0 & 0.12 & 99.689 & 0.1 \\ \hline Ti Grade 5 & 0.001 & 0.03 & 0.001 & 0.14 & 4.14 & 5.85 & 0.14 & 89.598 & 0.1 \\ \hline \multicolumn{8}{c}{Mechanical properties} \\ \hline  & Yield strength 0.2\% & Tensile strength & Elongation & Reduction in area \\ \hline Ti Grade 2 & 350 MPa & & 467 MPa & 33\% & & 57\% \\ \hline Ti Grade 5 & 905 MPa & & 980 MPa & 18\% & & 51\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the titanium alloys.  
Prior to machining, the cutting edge radius was measured using a contact profilometer, with three repetitions performed on 60 cutting edges and evaluations carried out by two independent observers. Nose radius and insert positioning repeatability were also verified using an optical microscope.