esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation and Verification of the Real-Time Working Characteristics of a Viscous Coupling
## 3 The Temperature Calculation of the VC at Working Course  
The working temperature of the VC is affected by the working environment temperature and the heat generated during working process. Because the former is relatively stable, the latter is the main factor. Part of the heat generated by the VC is transferred to the outside air through the plates, housing shell, and shaft, enabling the heat exchange between the inside and outside of the shell.  
The structure of the VC is complicated, and it needs to be simplified to develop a model. In the simplified structure model of the VC, the main parts, such as the shaft, shell, end cover, and blade are considered, while the small parts, such as the sealing parts of coupling, are ignored. In order to simplify the calculation, the influence of surface morphology of each part, especially blades, is ignored. The schematic diagram of the simplified structure of the VC is shown in Figure 2.  
The heat absorbed by the VC is transferred between the seven parts of the VC and the outside air. Figure 3 shows the heat-transfer model of the VC. It can be seen in the figure that the quantity of heat \(Q\) entering the VC is absorbed by silicone oil, then transfers to each part of the VC and to the outside air. The heat transfer between the seven parts of the VC and the outside air is \(Q_{xy}\) (\(xy\) indicates heat transferred from \(x\) to \(y\); the numbers 0-7 in the subscript correspond to outside air, silicone oil, case, left cap, right cap, axle, inner  
Figure 1: The relationship between constant A and the viscosity of silicon oil.  
Figure 2: A simplified structure of a VC.  
plate, and outer plate, respectively). The arrows in the figure indicate the direction of the heat transfer between each part of the model. The following assumptions are made for this model:  
1. Ignoring the thermal conductivity between the shell and the left and right caps, that is, \(Q_{23}\), \(Q_{24}\), \(Q_{53}\), \(Q_{54}\) are all zero.
2. Ignoring the thermal conductivity between the seals and the silicone oil, and with the sealed parts.
3. The inner and outer plates are pieces of metal immersed in silicone oil. There is no heat exchanging between the plates, the shell, and the axle, so \(Q_{65}\), \(Q_{75}\) are zero. The heat is exchanged between the plates and the silicone oil. The plates are metal parts and good conductors of heat generally. The thickness of the plates is less than 1 mm generally, and therefore the heat conduction from the plates' surface to the inside can be ignored. While the VC works, the temperature rises slowly, considering the heat exchange between the plates and the silicone oil being completed instantaneously at the moment of temperature difference. That is, the temperatures of the plates and the silicone oil are always equal.
4. Ignoring the radiation heat transfer from the shell and the cap to the outside; namely, the heat transmission from the cap and the cap to the outside is only the convective heat transfer.
5. The heat transmission of all parts of the VC is a steady heat transfer process.
# Three-Dimensional Reconstruction-Based Vibration Measurement of Bridge Model Using UAVs
## 2 Methods
### Displacement Tracked by DIC  
The DIC principle is shown in Figure 2. To track the movement of the measurement point \(P(x,y)\) on the image \(I(x,y)\), the area of \(N\times N\) pixels surrounding it is taken as the reference sub-region (Figure 2a), while the sub-region centered on \(Q(x^{\prime},y^{\prime})\) (where \(x^{\prime}=x+\Delta x\), \(y^{\prime}=y+\Delta y\)) on the deformed image \(J(x,y)\) is defined as the deformed sub-region; hence, the correlation between the two sub-regions can be expressed as [39]:  
\[C(\Delta x,\Delta y)=\frac{\iint_{S}I(x,y)J(x+\Delta x,y+\Delta y)dxdy}{\sqrt {\iint_{S}I^{2}(x,y)dxdy}\cdot\sqrt{\iint_{S}I^{2}(x+\Delta x,y+\Delta y)dxdy}} \tag{1}\]  
where \(S\) is the area of two sub-regions; the real displacement of \(P(x,y)\), \((\Delta x,\Delta y)\), maximizes the correlation coefficient \(C\)[32].  
Figure 1: Flowchart of the work.  
The pixel values of the points of interest in the process of bridge model vibration can be obtained by the DIC. The tracked displacement by the DIC is the combination of the absolute displacement of the measurement point and the false displacement induced by UAV motion. The next section introduces the homography transformation method to remove the false displacement.
## 4 DT-Based Gearbox Monitoring: A Framework for Future Research
Flexible shafts with a low diameter-to-length ratio can enter resonance at certain rotational speeds, causing excessive vibrations and failures. DTs can simulate these effects to identify critical speeds and optimize machine design and operation. * Improving the alignment between the signals simulated by DTs and the real-world signals is a major challenge. This requires accounting for complex phenomena, such as the response of the gearbox to mechanical play or bearing excitations. By refining models to better reflect reality, DTs become more reliable and useful for monitoring and predictive maintenance. Table 3 presents an evaluation of potential future contributions based on two key criteria: industrial priority and technological priority. This assessment is accompanied by identified technical bottlenecks and required resources. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Future Direction** & **Industrial Priority** & **Technology Maturity** & **Main Technical Bottleneck** & **Required Resources** \\ \hline \multirow{2}{*}{1} & Monitoring of combined gear and bearing faults & High & Medium & Overlapping of vibrational signatures & Labeled real-world datasets + source separation algorithms \\ \hline \multirow{2}{*}{2} & Integration of less-studied faults (misalignment, structural nonlinearities) & Medium & Low & Modeling complexity of nonlinear behavior & Advanced models + dedicated experimental measurements \\ \hline \multirow{2}{*}{3} & Sensor placement optimization using DTs & High & Medium & Identification of fault-sensitive zones & Multi-physics simulations + optimization tools \\ \hline \multirow{3}{*}{4} & Integration of thermal phenomena (e.g., friction-induced heat propagation) & Medium & Low to Medium & Reliable thermo-mechanical coupling & Thermal models + infrared cameras + calibration tools \\ \hline \multirow{2}{*}{5} & Extension of DTs to other rotating machines (turbines, compressors...) & Medium to High & Medium & Model adaptation to each machine’s dynamics & Machine-specific data + domain expertise \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prioritization of future research directions for DT-based gearbox monitoring.
## 4 Discussion
### SMAP/CYGNSS TB Uncertainties
#### 4.6.1 SMAP/CYGNSS Collocation Uncertainties  
There are a few uncertainties related to the collocation of SMAP and CYGNSS data. First, since the temporal collocation period varies from 1-6 days, the chance of rainfall occurring between collocated SMAP and CYGNSS observations is non-negligible. Rainfall between collocated observations could lead to reduced accuracy in downscaled TBs, since the SMAP and CYGNSS observations being merged would not be representative of the same NSSM. Second, derived \(\Gamma_{C}\) are not always representative of the spatial heterogeneity within a 33 km box. Due to the pseudo random spatial coverage of CYGNSS, some \(\Gamma_{C}\) are calculated using a single CYGNSS reflectivity value, while other \(\Gamma_{C}\) are calculated using tens of reflectivity values. When \(\Gamma_{C}\) is calculated using a single reflectivity value, \(\Delta\Gamma\) will always equal zero. Third, the moving window averaging method used to calculate \(\Gamma_{C}\), based on the coarse-scale gridding scheme, introduces bias into the downscaling algorithm. When the 3 km SMAP/CYGNSS TBs are upscaled, they do not always equal the 9 km SMAP TBs.
## 2 Few-Layer Transition Metal Dichalcogenides
### Nonlinear Optical Properties
#### 2.3.1 Saturable Absorption
Due to differences in fabrication procedures and the concentrations of nano flakes in the reported SA devices to date, it is difficult to directly compare the performance from literature values; however, [PERSON] _et al._ recently performed a comparative study under controlled fabrication conditions, showing that their MoSe\({}_{2}\) SA possessed a stronger modulation depth (6.7%) compared to MoS\({}_{2}\) (2.2%), WS\({}_{2}\) (2.5%) and WSe\({}_{2}\) (3.0%), in addition to lower non-saturable loss [42]. Further studies are required to critically evaluate the many emerging TMDs for fibre laser SA applications.
## 4 SNR Analysis of a QADA Receiver
### Simulation Results and Discussion
Please note that the scale is different from Figures 7 and 8 to clearly show the changes of the SNR within the FOV-C. The receiver, within its FOV-C, can still provide very good performance. However, we can see that when the FOV-C is reduced from \(63.4^{\circ}\) to \(45^{\circ}\), the area where the SNR is less than 0 dB is significantly reduced and the receiver has almost half the coverage area of the receiver with FOV-C of \(63.4^{\circ}\). Like Figure 7, the SNR is high within FOV-P.  
Figure 8: SNR in dB versus QADA location in an area equipped with a single transmitter with \(m=4\) and with the receiver parallel to the plane on which the luminaire is located with a FOV-C of \(63.4^{\circ}\) for (**a**) each quadrant; (**b**) the combined signal using MRC. Finally, to demonstrate the effect of tilt, Figure 10 depicts the SNR performance when the receiver is tilted by 20 degrees along the \(x\)-axis and 5 degrees along the \(y\)-axis. This represents a person holding and looking at a smartphone equipped with a QADA. Here we consider a FOV-C of 63.4\({}^{\circ}\) and \(m=1\). The results show that even with a tilt, the QADA still provides good performance across a wide area around the luminaire. Compared with the case when the receiver is parallel to the plane on which the luminaire is located, there is a shift in the SNR contour plots toward the larger values of \(y\). When no tilting is applied to the receiver, the SNR is higher than 20 dB, for displacements between \(-3.2\) m and 3.2 m along the \(y\)-axis (Figure 7). Now at displacements that are less than \(-1.8\) m along the \(y\)-axis, the SNR is less than 0 dB because, at these locations, most of the light is blocked due to the tilt. Like Figures 7-9, the SNR is always high within the FOV-P.  
Figure 9: SNR in dB versus QADA location in an area equipped with a single transmitter with \(m=1\) and with the receiver parallel to the plane on which the luminaire is located with a FOV-C of 45\({}^{\circ}\) for (**a**) each quadrant; (**b**) the combined signal using MRC.
# Electromagnetic Field Distribution and Data Characteristics of SUTEM of Multilayer Aquifers
## 4 Electromagnetic Field Diffusion Characteristics Study of SUTEM
### Uniform Half-Space Model
The underlying mechanism has been analyzed in detail in the distribution characteristics of the above fields. As the EMF z component \(\partial B_{z0}/\partial t\) at different depths below the center point of the transmitting loop has always existed and is relatively large, we chose \(\partial B_{x0}/\partial t\) at different depths of the transmitting loop center for analysis, as shown in Figure 10b. When the receiving point is located on the surface, \(\partial B_{x0}/\partial t\) decays with time. When the receiving point is located underground, \(\partial B_{x0}/\partial t\) increases first and then decreases with time, and there is a peak in the curve. With an increase in the depth of the observation point, the time of the peak appears later. At the same time, the tail branches of the \(\partial B_{x0}/\partial t\) trends at different depths tend to be consistent. Figure 9: Distribution of the uniform half-space model at different times. Therefore, it can be seen that there are positive and negative changes in the x-component \(\partial B_{x0}/\partial t\) of the EMF at different depths, and it is relatively complicated to convert the apparent resistivity based on this component. In addition, when the relative error is calculated by inversion processing, the inflection point of positive and negative transformation induces large error, which makes it difficult to search the inversion model. In contrast, the variation in \(\partial B_{z0}/\partial t\) at different depths is relatively simple: there exist only rising and falling segments, and there exists no inflection point of positive and negative changes. Therefore, it is relatively easy to calculate the apparent resistivity and inversion interpretation. To provide effective guidance for the interpretation of SUTEM and the detection of multilayer aquifers, our following investigation of the characteristics of SUTEM data mainly focuses on \(\partial B_{z0}/\partial t\).
# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Multivariate Data Analysis on RR Time Series
The training process has been performed without feature extraction and thereby the RNN can use all the available information. This approach has been trained on data from 20 patients (AFDB) and blind tested with the data from the remaining 3 patients of AFDB. This strategy achieved 98.32% and 98.67% as sensitivity and specificity, respectively, with 10-fold cross-validation process against 99.87% and 99.61% as sensitivity and specificity, respectively, with the blind fold validation. Among the reported limitations of this work is the significant time of training phase of the algorithm and the limited number of subjects under study. Another recent account of deep learning capabilities for AF arrhythmia detection is offered by the work of [PERSON] et al. [71], where the high performance of convolutional neural networks (CNN) for image analysis is exploited by translating AF detection into a suitable problem using of 400-point HRV sequences transformed into four extended _Poincare_ plots that serve as the CNN (visual) feature matrix inputs. The final input matrix choice is supported by a genetic algorithm. Addressing the problem of Paroxysmal AF detection in 5-min HRV segments, performance achieved values of 80.4% and 89% sensitivity and specificity, respectively, in a system trained by the Paroxysmal Atrial Fibrillation Prediction Challenge data that is tested with a combination of NSRDB and AFDB HRV sequences including Paroxysmal AF cases from the latter. In the work of [PERSON] et al. [72], important progress is shown in moving ECG-based AF detection methods to photoplethysmography-equipped remote health tracking devices. Although still falling short in performance with respect to benchmark tests on databases, partly due to class imbalance for the device acquired data, this work successfully paves the way for future transfer knowledge directions. Having trained CNNs fed with 13 temporal HRV features by using 30 s samples from MIT-BIH Arrhythmia, AFDB and NSRDB databases, data is downsampled to 50 Hz and tweaked to more closely match the pulse rate metrics form the device operation. As a result, the classifiers are able to achieve a performance of 94.50% sensitivity and 96.00% specificity for a 5-fold cross-validation on the ECG dataset, and a sensitivity of 77.80% and specificity of 88.54% for a test with wrist device data (25% of test datapoints, with 60% retraining and 15% validation) that has followed weight retraining and a 4-fold stratified cross-validation.
# Parametric Simulations on Leakage and Performance of a Miniature Free-Piston Generator (MFPG)
## 2 MFPG Prototype
Methanol (CH\({}_{3}\)OH) has a flame propagation speed of 0.523 m/s, which is much higher than 0.337 m/s and 0.39 m/s of gasoline and diesel [52], respectively. As an oxygenated fuel, its ignition limit is 6.7-36%, which is wider than that of traditional fuels, in addition to the lower minimum ignition energy, which is conducive to the rapid formation of a flame core. Moreover, methanol can burn stably when the exhaust gas recirculation (EGR) is more than 23% [53]. This feature can tolerate the low scavenging efficiency of a two-stroke engine and is therefore beneficial in complete combustion and reducing cycle variation. Methanol is then selected as the fuel of this MFPG prototype. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Bore/mm & \(D\) & 15.5 \\ Top limit position/mm & \(x_{film}\) & 0.5 \\ Bottom limit position/mm & \(x_{blim}\) & 16 \\ Ignition position/mm & \(x_{ign}\) & 3 \\ Exhaust port opening/mm & \(x_{EPO}\) & 10 \\ Exhaust port height/mm & \(x_{Eth}\) & 4 \\ Scavenging port opening/mm & \(x_{SPO}\) & 11.5 \\ Scavenging port height/mm & \(x_{Sh}\) & 4 \\ Scavenging temperature/K & \(T_{sc}\) & 310 \\ Scavenging pressure/bar & \(p_{sc}\) & 1.15 \\ Combustion duration/ms & \(t_{cl}\) & 3.5 \\ lower heating value/J/mg & \(H_{u}\) & 19.66 \\ Combustion quality factor & \(m\) & 1.5 \\ Air-Fuel Ratio of methanol & \(AF_{meth}\) & 6.45 \\ Adiabatic exponent & \(\gamma\) & 1.35 \\ Piston mass/g & \(m_{p}\) & 156 \\ Motor Constant/N/A & \(k_{motor}\) & 7.24 \\ Motoring Current/A & \(i_{motor}\) & 5.5 \\ Motor Inductance/mH & \(L_{s}\) & 2.38 \\ Back EMF constant/V/m/s\({}^{-1}\) & \(k_{emf}\) & 7.24 \\ Output current/A & \(i_{out}\) & 9 \\ Motor resistance/\(\Omega\) & \(R_{s}\) & 5.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the miniature free-piston generator (MFPG) prototype. Figure 2: Working principle of the MFPG prototype. (**a**) Scavenging stroke; (**b**) Compression stroke; (**c**) Expansion stroke; (**d**) Exhaust stroke. The green arrows display the air flow during the four strokes.
## 3 Fundamentals of GLI-Split
### Communication between Upgraded GLI-Nodes
#### 3.4.1 Communication within a GLI-Domain  
GLI-node 1 communicates with GLI-node 2 in the same GLI-domain (see Figure 1). Node 1 queries the local mapping system for a local GLI-address of ID 2. As both GLI-nodes are part of the same GLI-domain, the mapping system responds with one or several local GLI-addresses for node 2. Node 1 chooses one of them as destination address and its own local GLI-address as source address for communication with node 2.
## 6 Performance Results
### Video Latency  
Table 2 lists twelve cases of video latency results. For each combination of transport protocols (TCP/UDP) and encoding types (H264/MJPEG), a test with and without encryption was conducted. When comparing arithmetic mean values, it is noticeable that for all cases, tests with encryption resulted in overall higher latency. This is expected due to the fact that there is additional data processing in tests with encryption. The difference varies from 3 to 300 ms for different cases. The results for Group 1, 2 and 3 are presented in Figures 11-13, respectively. Measurement results are presented in the form of box plots. Tests without encryption are denoted with case number while the same tests with encryption are denoted with ENC.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 3 Principles of Tolerance-Aided Interference Degradation Algorithm
### Transmitted Signal Model  
For the sake of brevity and clarity in notation, we conduct our analysis within the confines of a single OFDM symbol duration. In our pursuit of high spectral efficiency, we employ \(M\)-ary quadrature amplitude modulation (\(M\)-QAM). For the \(i\)th subcarrier, we denote the modulated data as \(X_{i}\), where \(M\) signifies the size of the modulation constellation. It is essential to emphasize that, in order to maintain a real-valued OFDM signal, \(X_{i}\) and \(X_{N-1-i}\) must constitute a conjugate pair, with \(N\) denoting the total number of subcarriers, as articulated in [17]. Following the \(N\)-point inverse fast Fourier transform (IFFT) operation and the parallel-to-serial conversion, the \(m\)th sample of the OFDM symbol can be expressed as  
\[x_{s}[m]=\frac{c}{N}\sum_{k=0}^{N-1}X_{i}\exp\left(\frac{i2\pi km}{N}\right),~{} m=0,1,\cdots,N-1, \tag{1}\]  
where \(c\) is the modulation index controlling the scale of the transmitted signal magnitude. It is worth noting that \(c\) can be subject to optimization, effectively striking a balance between signal power and the mitigation of clipping distortion arising from the peak radiation power constraint imposed by the light sources.  
At the transmitter, the bipolar signal \(x_{s}[m]\) is reformed to be unipolar by the CEO-OFDM rules. Then, the \(n\)th sample of the transmitted signal is denoted [7]:  
\[\begin{split} x_{t}[n]=&\underbrace{\varphi(x_{s}[n] )}_{\text{frame 1}}+\underbrace{\varphi(-x_{s}[n-N])}_{\text{frame 2}}\\ &+\underbrace{\varphi(|x_{s}[n-2N]|-P_{\text{max}})}_{\text{frame 3 }}\end{split} \tag{2}\]  
where  
\[\varphi(x)=\left\{\begin{array}{ll}P_{\text{max}},&x\geq P_{\text{max}}\\ x,&0<x<P_{\text{max}}\\ 0,&x\leq 0\end{array}\right., \tag{3}\]  
which represents the nonlinear response of the light sources with the peak power constraint. The response of the working zone is modeled as a linear function of the input current. \(P_{\text{max}}\) is the radiation power limit.
## 3 Fault Feature Extraction
### Selection of Basic Parameters
#### 3.1.2 Decomposition Scale and Selection of Characteristic Quantities  
A wavelet function has the advantage of being able to extract the signal mutation location from the maximum value of the pair of modes as a result of its small scale, but its disadvantage is its sensitivity to noise and inability to extract the fault position accurately. When the decomposition scale is large, harmonic interference can be prevented, but the position of the maximum modulus and the signal mutation point cannot coincide effectively, so it is highly important to choose the appropriate decomposition scale.  
In this paper, a maximum modulus matrix \(A_{ij}\) is established to help determine the decomposition scale, where i represents the fault line and j represents the decomposition scale. The maximum modulus matrix is established as shown in Equation (10).  
\[A_{ij}=\begin{bmatrix}a_{11}&a_{12}&a_{13}&a_{14}&a_{15}&a_{16}\\ a_{21}&a_{22}&a_{23}&a_{24}&a_{25}&a_{26}\\ a_{31}&a_{32}&a_{33}&a_{34}&a_{35}&a_{36}\end{bmatrix} \tag{10}\]  
After the decomposition scale is determined, the energy value of the corresponding characteristic frequency band is calculated using Equation (11).  
\[E_{6,J}=\int_{-\infty}^{+\infty}\big{|}S_{6,J}(t)\big{|}dt=\sum_{k=1}^{n} \big{|}d_{j,k}\big{|}^{2} \tag{11}\]  
The frequency band energy corresponding to the zero-sequence current of each circuit is calculated using Equation (11), and the maximum energy value in each frequency band is selected to form a characteristic matrix. Through the analysis of the training model, fault type identification and diagnosis are achieved. The characteristic matrix \(B_{cd}\) is shown in Equation (12).  
\[B_{cd}=\begin{bmatrix}b_{11}&b_{12}&b_{13}&b_{14}&b_{15}&b_{16}\\ b_{21}&b_{22}&b_{23}&b_{24}&b_{25}&b_{26}\\ b_{31}&b_{32}&b_{33}&b_{34}&b_{35}&b_{36}\end{bmatrix} \tag{12}\]where \(c\) is the number of lines and \(d\) is the decomposition scale.