esa-sceva/satcom-chunk-collection huggingface dataset:
# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement
## 2 Methodology
### Image Conversion Stage
#### 2.1.4 Conversion from Time-Series to Gramian Angular Difference Field  
The Gramian Angular Field (GAF) method was used to transform the scaled one-dimensional time-series into a polar coordinate system and construct the objective mapping between one-dimensional time-series and two-dimensional space. Then, the GAF method could be divided into two implementation methods according to the calculation angle between different time points, including Gramian Angular Summation Field (GASF) and GADF. A GAF image was a graphical representation of a Gramian matrix in which each element was the superposition of directions between different time intervals, and the polar coordinate system was used to retain the time correlation.  
The conversion processes of GASF and GADF were similar, and the conversion process of GADF was as follows.  
Step 1. One-dimensional time-series were scaled numerically. The time-series in the Cartesian coordinate system was scaled to \([-1,1]\) interval and the calculation of time series scaling is  
\[\widetilde{z_{i}}=\frac{(z_{i}-\text{max}(z))+(z_{i}-\text{min}(z))}{\text{ max}(z)-\text{min}(z)},\ \ \widetilde{z_{i}}\in[-1,1] \tag{12}\]  
Step 2. The scaled sequence data \(\widetilde{z_{i}}\) were transformed from a Cartesian coordinate system to a polar coordinate system in which the value was regarded as the cosine of the included angle, the timestamp was treated as the radius, and the acc  
Figure 4: The effect of converting the time-series into MTF images.  
for mapping. This method retained the time dependence through the \(r\) coordinate, and the coordinate transformation equations is  
\[\left\{\begin{aligned} \varnothing_{l}=\arccos(\widetilde{z_{i}}),& -1\leq\widetilde{z_{i}}\leq 1,\,\widetilde{z_{i}}\in\widetilde{z}\\ r_{l}=\frac{t_{l}}{\gamma}& t_{l}\in\gamma\end{aligned}\right. \tag{13}\]  
where, \(t_{l}\) is the time stamp, and \(\gamma\) is a constant factor to regularize the span of the polar coordinate system.  
Step 3. The GADF matrix was obtained by the trigonometric function transformation of two angular differences. The calculation of the GADF matrix is  
\[\text{GADF}=\left[\begin{array}{ccc}0&\cdots&\sin(\varnothing_{1}- \varnothing_{n})\\ \vdots&\ddots&\vdots\\ \sin(\varnothing_{n}-\varnothing_{1})&\cdots&0\end{array}\right] \tag{14}\]  
Similar to other image characterization methods mentioned earlier, the PAA method was used to retain the sequence trend and reduce the sequence size at this stage.  
The effect of converting the time-series into GADF images is described in Figure 5.  
The GADF images reveal the temporal correlation between data pairs and preserve the spatial variation law intuitively. And the variation in GADF images is roughly the same as that of the abovementioned three image representation methods.
## 3 Results and Discussion
### A Discussion of the Adjacency Effect in LST Downscaling  
The LST and remote sensing indices' relationships are used in most LST downscaling methods frequently. While the LST relationships of adjacent pixels have received limited attention, the OBD method could introduce the relationship of adjacent pixels in the downscaling process.  
Figure 10 illustrates the process and results of applying the OBD method while considering adjacent pixels with the same land use type; in different objects, it should exhibit similar decomposed LST values. Figure 10a depicts two objects, A and B, each containing four subpixels with distinct true LST values. Figure 10b represents the true LST of the parent pixels at a lower spatial resolution, corresponding to the subpixels in Figure 10a. Figure 10c shows the initially estimated decomposed LST based on a hypothetical LST-RSI relationship, while Figure 10d presents the weights of each subpixel, calculated from this initial estimate. If only the principle of energy conservation during decomposition is considered, the results are as shown in Figure 10e. An imperfect result is observed in the pink rectangle in Figure 10e, where the decomposed LST difference between two adjacent water pixels reaches 1.7 K. Such a discrepancy is unrealistic in most natural environments. To address this, the rule specifying that the decomposed LST of adjacent water pixels across  
Figure 9: Comparisons of different indices with initial LST errors and OBD LST errors: the ME of the object under initial LST errors in the ranges of (**a1**) \(\pm\)1 K, (**a2**) \(-\)4–0 K, and (**a3**) 0–4 K; the STD of the object under the initial LST errors in the ranges of (**b1**) \(\pm\)1 K, (**b2**) \(-\)4–0 K, and (**b3**) 0–4 K; the RMSE of the object under the initial LST errors in the ranges of (**c1**) \(\pm\)1 K, (**c2**) \(-\)4–0 K, and (**c3**) 0–4 K.  
different objects should be consistent should be introduced. The decomposed LST results, considering the adjacency effect, are shown in Figure 10f. The LST difference between the adjacent water pixels in the pink rectangle is significantly reduced, resulting in a more realistic distribution. Additionally, the decomposed LST values for all the subpixels in Figure 10f are closer to the true LST values compared to those in Figure 10c,e. Thus, incorporating the adjacency effect of different objects effectively enhances the accuracy of the decomposed results.
## 3 Outer Layer Modeling and Optimization
### Steady State Operation Modeling of Integrated Energy System
#### 3.1.2 Natural Gas System  
The steady state mode of a natural gas system can be formulated similarly to the electric power system [2; 3; 4; 6; 7; 8; 9]. For a gas pipeline \(g\) from node \(i\) to \(j\), the gas flow can be expressed as:  
\[\left\{\begin{array}{l}m_{g}=\delta_{ij}K_{ij}\sqrt{\delta_{ij}(P_{i}^{2}-P_ {j}^{2})}\\ \delta_{ij}=\left\{\begin{array}{cc}+1,&P_{i}\geq P_{j}\\ -1,&P_{i}<P_{j}\end{array}\right.\end{array}\right. \tag{18}\]  
where \(m_{g}\) denotes pipeline volume flow; \(P_{i}\) and \(P_{j}\) represent pressure at pipe start node and end node, respectively; \(\delta_{ij}\) stands for the direction of flow in a gas pipeline; \(K_{ij}\) denotes the transmission coefficient of the pipeline from bus \(i\) to bus \(j\), which depends on the characteristics of this pipeline.  
The compressor is one of the most important components of the natural gas system, and its function is to supplement the pressure loss of the gas pipe. The electric compressor model of the gas pipe is shown in Figure 3.  
\[P_{com}=K_{com}\cdot m_{g}^{kn}\cdot T_{k}\Bigg{[}(\frac{P_{k}}{P_{m}})^{\frac{n-1 }{\alpha}}-1\Bigg{]} \tag{19}\]  
where \(K_{com}\) denotes compression ratio; \(P_{com}\) stands for the power consumption of the compressor; \(\alpha\) is the specific heat ratio; \(m_{g}^{kn}\) represents the gas flow through the compressor; and \(T_{k}\) denotes the temperature of natural gas.
## 4 Risk Analysis for Current Protocols
### NMEA 2000
#### 4.2.3 Packet Sniffing  
NMEA 2000 operates on a single broadcast domain. All nodes on the message channel (similar to a bus on CAN Bus) receive all messages [32, 33]. It is the responsibility of the nodes to discern which messages to discard as unneeded. We suggest that this makes passively sniffing messages a trivial task, assuming physical access is possible.
## 2 Water Resources Monitoring in Semi-Arid Regions: The WARM-SAR Project  
In this section, we recall the results of the WARM-SAR project, which is devoted to exploring the possible uses of SAR images for water resource monitoring.  
WARM-SAR exploited a set of 16 stripmap (3 m resolution) and 7 spotlight (1 m resolution) images with coverage of almost one year and a half, provided at no cost by the Italian Space Agency (ASI) under the aegis of the 2007 COSMO-SkyMed Announcement of Opportunities [21]. The SAR images cover a rectangular area of almost 1600 km\({}^{2}\) of the Yatenga district in the north of Burkina Faso, a small West African country where nearly 80% of the 14 million inhabitants live in rural areas and the main economic activity consists of subsistence farming and ranching. More than two million people are food insecure and about 34% of the population is subject to chronic malnutrition [22]. The region is characterized by a semi-arid climate, with a rainy season lasting three months.  
In the frame of the WARM-SAR project, we developed specific applications for low-income countries characterized by a semi-arid climate. In order to estimate the performance expected by the use of Sentinel-1 data in a similar context, in this section we recall the basic principles of the following applications:  
1. Digital Elevation Models (DEMs) generation and related products;
2. Land cover mapping;
3. Monitoring of water volumes retained at reservoirs.
## 3 Numerical Simulation of the Effect of Deep Excavation on Adjacent Tunnels  
### FEM Numerical Model  
To determine the influenced zone of deep excavation on the adjacent tunnel, numerical simulations were conducted using the finite element code MIDAS. Theoretically, the effect of deep excavation on the adjacent tunnel is a 3D issue, and the excavation-induced tunnel displacement along its longitudinal axis appears as a single-peak curve, as shown in Figure 1a. Considering more than 700 simulation conditions are set in this study, and 3D numerical simulation takes a lot of time and cost; therefore, the section of tunnel with a single-peak displacement is selected for 2D analysis.  
Figure 4 shows the 2D FEM model for the effect of deep excavation on the adjacent tunnel. The deep excavation is supported by a diaphragm wall and horizontal struts. The depth of deep excavation is \(H_{\text{e}}\), which is analyzed as a variable, and the half-width of deep excavation is \(B\) = 30 m. The thickness of the retaining wall is set to \(t\) = 0.8 m, and the insertion depth of the retaining wall is set to \(H_{\text{i}}\) = \(H_{\text{e}}\). The adjacent tunnel is a subway shield tunnel, which has a diameter of 6.0 m and a wall thickness of 0.3 m. The horizontal distance from the tunnel center to the deep excavation is \(L_{\text{t}}\), and the buried depth of the tunnel center is \(H_{\text{t}}\), which are also analyzed as variables. In the model, the horizontal distance from the right boundary to the retaining wall is 120 m, and the vertical distance from the bottom boundary to the base slab is 54 m, which can eliminate the boundary effect under different simulation conditions.  
In the model, the soil is simulated using a plane strain element; the retaining wall, horizontal struts and existing tunnel are simulated using a beam element; and the contacts between the retaining wall soil and tunnel soil are simulated using an interface element. To increase the calculation efficiency and ensure calculation accuracy, the mapping mesh is used. The mesh size near the deep excavation and tunnel is 1 m, and that near the boundary is 5 m.  
For displacement boundary conditions, the left and right boundaries are limited to horizontal displacement, the bottom boundary is limited to horizontal and vertical displacements, and the top boundary is kept free. For load boundary conditions, only gravity is considered, and no other external loads are considered. For hydraulic boundary  
Figure 4: 2D FEM model for the effect of deep excavation on the adjacent tunnel.  
conditions, the initial groundwater table is 1.5 m below the ground surface, and the pressure head on the groundwater line is 0.
## 5 FPGA Prototyping
### System Description  
The system is prototyped using the VC707 evaluation board integrating the Xilinx Virtex-7 XC7 VX485T FPGA. The selected device includes 75,900 configurable logic block slices, where each slice contains four LUTs and eight flip-flops, in addition to 2800 DSP slices and 27,000-KB block RAM blocks.
## 3 Measurements
### Laboratory Testing  
An advantage of testing in the laboratory lies in the possibility of having a certain level of control over the testing environment. Powerlines used as communication channels can be easily connected through flexible electrical power cables to achieve desired lengths of power line. External devices reducing or adding noise into the network can also be easily connected.  
For our laboratory testing, four different topologies with BPL modems were prepared:  
**A--Isolated network:**  
1. A pair of BPL modems and 100 m of extension cord, see Figure 3;
2. A pair of BPL modems and 100 m of cord, where a repeater was placed in the middle of the power line, see Figure 4.
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
## 2 The Principle of Notch Equivalent Stress and the Proposal of a New Method for Life Prediction
### Comparison of Goodness of Fit between Traditional and New Three-Parameter Power Function Models  
The test data of titanium alloy welded structures were fitted by two methods, the traditional three-parameter power function S-N curve and the new three-parameter power function S-N curve. It can be seen that the fatigue limit parameter obtained by the new three-parameter power function method is relatively large. Through calculation and analysis, the coefficient of determination R\({}^{2}\) of the two methods to measure the goodness of the fitting curve can be obtained. Comparing the size of R\({}^{2}\), the applicability of the two S-N curve-fitting models to the titanium alloy welded structures can be obtained. The coefficient of determination R\({}^{2}\) of the two fitting models is shown in Table 5.  
From Table 5, it can be seen that the new three-parameter power function model proposed in this study significantly improves the fitting degree of the experimental data of titanium alloys. It shows that the new three-parameter power function model improves the  
Figure 7: S-N curve of titanium alloy welded structure fitted by two methods. (**a**) Traditional three-parameter power function model. (**b**) New three-parameter power function model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fatigue Life Characterization Model of Titanium Alloy** & **Coefficient of Determination R\({}^{2}\)** \\ \hline Traditional three-parameter power function model & 0.8452 \\ New three-parameter power function model & 0.9407 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficient of determination R\({}^{2}\) of different S-N curve fitting models.  
relatively low goodness of fit of the traditional three-parameter power function model and lays a foundation for the further combination with the correction notch equivalent stress method.
# Recent Development of High-Energy Short-Pulse Lasers with Cryogenically Cooled Yb:YAG
## 3 Suppression of ASE Effect  
With the decrease in temperature, the emission cross-section of Yb:YAG crystals increases sharply, which brings beneficial changes such as increasing the laser gain and increasing the extraction efficiency on the one hand, and increased probability of spontaneous emission and aggravated ASE effect, on the other. As a result, a large number of inversion particles are consumed, and the efficiency of the laser is reduced. Therefore, how to suppress the ASE effect has become an important topic in the study of cryogenically cooled Yb:YAG lasers. As shown in Figure 5, in the existing cryogenically cooled Yb:YAG high-power laser, the ASE effect is suppressed by bonding undoped YAG crystal and absorber (Cr4+:YAG crystal) cladding, and there are also traditional methods such as chamfering edges and grinding around the edges [16; 17; 18; 19].  
As shown in Figure 5a, a layer of YAG crystal is bonded on the surface of the Yb:YAG crystal. Since the two refractive indices are the same, photons larger than the total reflection angle of the interface are introduced into the YAG crystal. The YAG crystal has no gain, and photons decay rapidly in it, thereby reducing the ASE effect [16]. If no suppression measures are taken, ASE photons are reflected on the side of the gain medium and return to the gain medium, in which they are amplified again and even form parasitic oscillations, in order to prevent ASE photons from being reflected back. As shown in Figure 5b, a layer of Cr4+:YAG crystal is wrapped around the Yb:YAG crystal as an absorber for absorbing ASE photons [17]. In addition, by changing the shape of the side of the gain medium, such as by edge chamfering [18], as shown in Figure 5c, and edge grinding [19], as shown in Figure 5d, the ASE photons are refracted out of the crystal so that they can no longer be amplified, which also suppresses the ASE effect.
# Overview of Predictive Control Technology for Permanent Magnet Synchronous Motor Systems
## 3 An Overview of the Recent Development for Predictive Control Methods of PMSM
### FCS Model Predictive Control of PMSM
#### 3.2.2 Multi-Vector Application Control  
Due to the limited switch state adopted by FCS-MPC, the output voltage vector amplitude of the inverter was fixed, resulting in high-frequency current harmonics. Many scholars have tried to control the motor by outputting multiple switch state combinations in a discrete control cycle. Therefore, they are collectively referred to as \"multi-vector strategies\" [109; 110; 111; 113].  
In [114], the active voltage vectors and zero vector were applied in a single cycle. Adjust the amplitude of the active voltage vector through the zero vector. On this basis, reference [109] proposes a dual vector control strategy. After determining an optimal vector, the strategy continued to use the prediction algorithm to determine the second voltage vector, thus synthesizing a vector with an adaptable phase angle or amplitude to control the motor. \"Three vector strategy\" has been proposed successively [115]. Figure 7 is from [115], and as shown in Figure 8 is the schematic diagram of three vector functions. In [111]'s \"three-vector strategy\", this strategy used a prediction algorithm to determine three vectors and used the space vector synthesis principle to obtain the three-phase duty cycle corresponding to the inverter. Among many finite set model predictive control  
Figure 6: Flow chart of weight coefficient calculation based on neural network.  
algorithms, the output vector amplitude and phase of this strategy can be adjusted freely, and the control accuracy of torque and flux linkage were greatly improved.  
In addition, the virtual voltage vector is proposed [116]. In [117], a hybrid control based on voltage vector combination is proposed. It uses more virtual vectors and unifies the switching frequency, which also makes the calculation more complex, but improves the quality of the stator current. For polyphase motors, a virtual voltage vector combination has been proposed based on the characteristics of the space voltage vectors [118, 119, 120]. In [119], a multi-vector control strategy for the dual three-phase PMSM drive system is proposed. By predicting the rotor position, the rotor position information is introduced into the predictive model, and the optimal voltage vector is calculated through the optimization algorithm to achieve the optimal torque and minimum current waveform distortion. A summary of the applications of different voltage vectors mentioned above is shown in Table 3[18].
## 3 Progress in Grating Fabrication Technology
### Mechanical Scribing Technology
[PERSON] of Germany simulated and refined the grating-groove geometry of the mechanically scribed grating and employed a scanning probe microscope to monitor the wear of and mechanically characterize the grating groove, resulting in a scribed grating with an 85% blazing efficiency [65]. When processing gratings, modern research units must still rely on the processing experience of equipment operators to some extent. Engineers must install tools in the scribing equipment and adjust scribing settings to ensure that the diffraction gratings generated fulfill the criteria of use. Optimal adjustment increases both the time and cost of processing. The Changchun Institute of Optics and Mechanics suggested a method in 2019 for altering the diamond location in a scriber to achieve nanoscale groove placement. The control precision of the CIOMP-6 scribing machine was further enhanced by enhancing control techniques, such as creating the optical route, and a range of high-quality gratings are manufactured using this scribing mechanism. The first was a high-efficiency grating with groove densities of 500 g/mm, 768 g/mm, and 1520 g/mm, and blaze angles of 10\({}^{\circ}\), 9.7\({}^{\circ}\), and 12.7\({}^{\circ}\), respectively. The experimental diffraction efficiency was close to the theoretical design efficiency; the RMS values at 632.8 nm were 0.079 \(\lambda\), 0.109 \(\lambda\), and 0.145 \(\lambda\), respectively. The second produced was a high-efficiency echelle grating of 64.285 g/mm, the blaze order was \(-7\), and the PV and RMS values at 632.8 nm were 0.093 \(\lambda\) and 0.014 \(\lambda\), respectively. The maximum ghost and scattered light intensity of the third echelle grating with a line density of 79 g/mm was cut in half, and the as-fabricated diffraction efficiency approached 90% of the theoretical value [66]. Although researchers have conducted corresponding research on scribed gratings in recent years, the scribing technology requires the use of diamond tools to make diffraction gratings; there is a certain error in scribing accuracy, and it is difficult to achieve if the grating period is too large or the groove depth is too deep. There is a certain deviation from the theoretical design, and there are the problems of ghost lines and high stray light, which limit the application of scribing technology to a certain extent.