esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 5 Natural Resource Extraction
### Above-Ground Mining Operations
#### 5.2.2 Example Case-Study: Cadia Tailing Dam Failure (New South Wales)  
Solid and liquid tailings containment infrastructure within mine site operations can also be monitored using InSAR (e.g., [56]). In March 2018, partial failure of a dam wall occurred at a tailings storage facility at the Cadia gold mine, New South Wales, and the subsequent time-series analysis of Sentinel-1 imagery in the \(\sim\)2 year period prior to failure reveals the potential for InSAR to provide early warning of collapse (Figure 8).  
At the upper layers of the dam, steady motion totalling \(\sim\)50 mm is detected over the 2-year observation period prior to collapse and is likely related to the expected settlement of new layers recently added to the earthen dam wall (points 1, 2, and 5; Figure 8). However, during the three months prior to collapse between late January to late February 2018, \(\sim\)80 mm of motion is detected in pixels coincident with the zone of failure (points 1-4 in Figure 8B, where the white lines show approximate failure zone). Our identification of precursory motion is corroborated by other independent assessments of this event (e.g., [56]).  
In addition to detecting the timing of significant changes, these measurements suggest that there is scope to use InSAR to identify the location of weaker or weakening parts of mine infrastructure since the magnitude of displacements is greater for InSAR pixels located within the part of the dam wall that later failed (points 1-4; Figure 8) than those  
Figure 7: LoS velocities at the Loy Yang mine in the Latrobe valley, Victoria, derived from the InSAR analysis of ALOS PALSAR between 20070915 and 20110812. (**A**) Average LoS velocities overlain on a background Google Earth image from 20110812. The coverage of coherent pixels is limited by the rapid displacements and satellite imaging geometry. (**B**) Time-series shows the time history of LoS displacements at three pixels located outside of the open-cut operations area, and these pixels are highlighted in A with circles.  
located adjacent to it (points 5 and 6; Figure 8). Thus, InSAR could have potentially been used not only as an early-warning tool with a lead-time exceeding 1 month but also to map the weak area prior to failure and focus adequate mitigation efforts at these locations.
## 4 Experimental Results
### 2-DE Images Classification
#### 4.3.3 Comparison of Various Source Tasks  
Investigating the difference in performance, we changed the source domain data, which were obtained from Caltech-101 [27], CIFAR-10, and MNIST. The \(N^{s}\) of MNIST and CIFAR-10 was 50,000 (\(D_{y}^{s}=10\)), while that of Caltech-101 was 9146 (\(D_{y}^{s}=102\)). All images were resized to \(D_{x}=53\times 44=2332\) to ensure that they were aligned with the 2-DE images. The number of epochs for Caltech-101/CIFAR-10/MNIST was set to 1000. \(H\) was selected from \(\{1,2,3\}\), and \(D_{h}\) was selected from \(\{188,500,1000\}\) to provide the best performance.  
Table 5 lists the classification performance, which shows that the classification performance was higher than that of Caltech-101/CIFAR-10/MNIST, although the number of 2-DE images was smaller. An exception to this is the set of results for NPV, which was based on the use of 2-DE images. However, these results indicate that information regarding the differences between protein extraction and refining protocols can be useful for classifying sepsis.  
Figure 5B shows the comparison of \(d\) between different source domains. As shown in this figure, \(d\) became larger than the other domains when 2-DE images were used in the source domain. Notably, the \(d\) of Caltech-101 became smaller than the other domains. These results are consistent with their classification performance. Thus, the distance between the relation vectors is related to the classification performance. Moreover, these results imply the possibility of selecting an effective first DNN before the minimization of Equation (3). We will examine this in the future.
# Inactivation of Contaminated Fungi in Rice Grains by Dielectric Heating
## 3 Results and Discussion
### Effect of Dielectric Heating Condition on Fungal Growth
Use of both thermal and non-thermal mechanisms was reported in _Aspergillus fumigatus_ and _Neosartorya fischeri_ in apple juice (83 and 92 \({}^{\circ}\)C with 20 V/cm, 83 \({}^{\circ}\)C with 27 V/cm, and 90 \({}^{\circ}\)C with 9 V/cm) [60,61] and _Aspergillus niger_ in tomato (89 \({}^{\circ}\)C with 36 V/cm) [62]. The results indicate that the contaminated fungi in rice grain products can be eliminated using the dielectric heating machine. The system can generate heat on fungal cells faster than the surrounding media/environments. As a result, the rapid oscillation of the fungal cell leads to elastic limit, rupture, and cell death [63]. This study suggested that dielectric heating is an effective method for fungal inactivation in rice grains. Additionally, the optimal conditions of dielectric heating are suitable for inactivation of contaminated fungi in rice industries. Figure 8: The fungal growth on Petri dish after treated with different electric intensity at 90 \({}^{\circ}\)C.
## 2 Holographic Single-Cell Illumination Setup
### Computer-Generated Hologram Calculation and Binarization  
Since the SLM is used as the focusing optics, the calculation of the focusing hologram \(\boldsymbol{H}\) is straightforward. Each of the SLM pixels represents one spot on the sample that can be illuminated. Therefore, first a matrix \(\boldsymbol{M}\) representing all of the neurons that should be activated has to be created. A complex-valued computer-generated hologram for focusing to these neurons is then calculated by convolving this matrix with a single Fresnel zone plate, which has been pre-calculated for the focal length \(\boldsymbol{f}_{min}\):  
\[\boldsymbol{H}(\boldsymbol{x},\boldsymbol{y})=\iint\boldsymbol{M}(\boldsymbol {x}-\boldsymbol{\tau},\boldsymbol{y}-\boldsymbol{v})\boldsymbol{e}^{i\boldsymbol {v}\cdot\boldsymbol{\varphi}(\boldsymbol{\tau},\boldsymbol{v})}\boldsymbol{d \tau}\boldsymbol{d}\boldsymbol{v} \tag{5}\]  
Because the FSLM is a binary modulator, the complex-valued holograms have to be converted to binary phase holograms. For the case of sparse holograms using well-separated foci, the binary phase \(\boldsymbol{\varphi}\) can be easily obtained using a thresholding algorithm on the continuous phase \(\boldsymbol{\varphi}\) of the complex-valued hologram:  
\[\boldsymbol{\varphi}^{{}^{\prime}}=\left\{\begin{array}{ll}\boldsymbol{ \pi},&\boldsymbol{\pi}/\boldsymbol{2}\leq\boldsymbol{\varphi}<3\pi/2\\ \boldsymbol{0},&\boldsymbol{else}\end{array}\right.. \tag{6}\]  
For illuminating patterns that do not consist of well-separated minimal foci but of larger illuminated areas, a more complicated CGH calculation scheme is applied. Again, all of the areas that have to be illuminated are registered in a matrix, which is then again convoluted with a single complex Fresnel zone plate. This, however, results in a complex-valued hologram, in which the amplitude is sparsely distributed. Reconstructing such a hologram, which was binarized according to Equation (6) with plane-wave illumination, results in deteriorated illumination patterns due to the suppression of high spatial frequencies [36]. We are therefore using bidirectional error diffusion [37] to binarize these complex holograms. This method iterates through all pixels of the hologram in a meandering pattern. Each pixel is binarized separately and the resulting error as compared with the complex hologram is distributed among unprocessed neighboring pixels. The whole process of hologram generation is illustrated in Figure 3.
## 3 Commercial Harvester Characterization
All the parameters of the equivalent circuit of the considered electromagnetic harvester are summarized in Table 1.  
To validate the accuracy of the equivalent circuit in emulating the behavior of the considered commercial harvester, the equivalent circuit was implemented by using electronic components mounted on a solderless breadboard, in the Thevenin alternative form shown in Figure 8 that is equivalent to that in Figure 2. The inductance \(L_{m}\) was implemented through an active circuit based on operational amplifiers, i.e., the Antoniou inductance-simulation circuit. By using the automatic measurement option of the oscilloscope DSOX1204G-D1200 BW1A by Keysight Technology the transfer function \(G_{CIRCUIT}(f_{VIB})=V_{P}/V_{G}\) was obtained for \(R_{LOAD}=1000\)\(\Omega\), as shown in Figure 9a. \(V_{P}\) and \(V_{G}\) are the voltages with respect to ground of points P and G, respectively (Figure 8). \(G_{CIRCUIT}(f_{VIB})\) can be expressed as a function of the complex quantities \(V_{OC}\) and \(V_{LOAD}\), previously measured on the commercial harvester (Figure 7):  
\[G_{CIRCUIT}(f_{VIB})=\frac{V_{P}}{V_{G}}=\frac{V_{OC}-V_{LOAD}}{V_{OC}}=1- \frac{V_{LOAD}}{V_{OC}} \tag{17}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(k\) & 28 mA/g \\ \(R_{m}\) & 700 \(\Omega\) \\ \(L_{m}\) & 29.9 mH \\ \(C_{m}\) & 87.5 \(\mu\)F \\ \(R_{c}\) & 300 \(\Omega\) \\ \(L_{c}\) & 135 mH \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the equivalent circuit of the tested commercial electromagnetic harvester. Figure 8: Alternative Thevenin form of the equivalent circuit of a single coil resonant electromagnetic vibration energy harvester. Therefore, the transfer function \(G_{CIRCUIT}(f_{VIB})\) measured on the implemented equivalent circuit and that calculated by using the measurements on the commercial harvester were compared, as shown in Figure 9b. The comparison shows a very good agreement of the electrical behaviors of the commercial harvester and of the equivalent circuit.
## 3 The Sound Source Monitoring Combing Stereo Vision and Beamforming
### Three-Dimensional Coordinates Extraction of Target Machinery Surface  
Stereo vision can obtain the depth image of the entire field of view including the target, and extracting the three-dimensional coordinates of the target machinery surface is one of the key points of this method.  
The threshold method is used to segment the depth image to obtain the pixel set of the target machinery surface. The threshold method compares the color value of the image pixel with a given threshold and divides the pixel points into two types, the background, and the target. This method has high computational efficiency and stable performance. Assuming that \(f\left(x,y\right)\) is the original image and the threshold is \(T\). The image can be divided into two parts by the following formula:  
\[g(x,y)=\left\{\begin{array}{l}b_{0},f(x,y)<T\\ b_{1},f(x,y)\geq T\end{array}\right. \tag{6}\]  
This method uses \(b_{0}=0\) (black) and \(b_{1}=1\) (white) to realize image binarization. In this study, the black area is the target. All pixels in the black area are coordinated, and the depth of these pixels can be obtained.
# High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects
## 3 High Performance MRAM Writing
### Reliability Issues
#### 3.4.1 Process Variation  
The MTJ switching mechanism has been demonstrated to be intrinsically stochastic, which has been validated by experimental work [40; 41; 42]. On the other hand, due to the limited fabrication precision, the development of submicron CMOS transistors and nanometer-level MTJ devices suffer from parametric process variations. Due to these effects, the switching failure takes place frequently in both MTJ writing (e.g., fails to store the right data) and sensing (e.g., unexpected switching) phases.  
In order to study the process variation, physical parameters are stochastically modeled. For MTJ devices, the deviations of oxide barrier thickness, free layer thickness and TMR ratio are modeled with Gaussian distribution with 3\(\sigma\) range. For CMOS transistor, its compact model is analyzed simultaneously with MTJ compact model to realize hybrid magnetic/CMOS circuit performance. Figure 6 demonstrates the DC simulation and Monte Carlo transient simulation. Write errors may occur if the write duration is less than the required value.
## 4 Experimental Results and Analysis
### Quantization Calibration Set Optimization Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image Num.** & **Max. Variance** & **Average Variance** & **Max. Dynamic Range** & **Average Dynamic Range** & **Cos Sim.** & **Relative Dist.** \\ \hline
10 & 1851 & 1325 & 7066 & 6369 & 0.761 & 0.689 \\
20 & 2192 & 1699 & 8759 & 7273 & 0.824 & 0.600 \\
30 & 2357 & 1895 & 8966 & 7690 & 0.833 & 0.584 \\
40 & 2417 & 2017 & 8966 & 7790 & 0.812 & 0.622 \\
50 & 2461 & 2103 & 9288 & 7878 & 0.839 & 0.579 \\
60 & 2467 & 2163 & 9288 & 7912 & 0.839 & 0.579 \\
70 & 2481 & 2208 & 9288 & 7942 & 0.839 & 0.579 \\
80 & 2508 & 2242 & 9288 & 7921 & 0.839 & 0.579 \\
90 & 2704 & 2279 & 9288 & 7941 & 0.839 & 0.579 \\
100 & 3252 & 2349 & 11,615 & 8150 & 0.839 & 0.579 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The increment experimental results of the calibration set sorted by the image variance. Figure 14: A selection of the quantization calibration images. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image Num.** & **Max. Variance** & **Average** & **Max. Variance** & **Average Variance** & **Cos Sim.** & **Relative Dist.** \\ \hline
10 & 7032 & 6398 & 1852 & 1356 & 0.761 & 0.689 \\
20 & 7702 & 7286 & 2521 & 1862 & 0.824 & 0.599 \\
30 & 7750 & 7299 & 2521 & 2078 & 0.824 & 0.599 \\
40 & 7873 & 7345 & 2521 & 2172 & 0.839 & 0.577 \\
50 & 8090 & 7645 & 2521 & 2205 & 0.828 & 0.596 \\
60 & 8299 & 7815 & 2521 & 2247 & 0.837 & 0.582 \\
70 & 8468 & 7867 & 2521 & 2271 & 0.837 & 0.582 \\
80 & 8794 & 7913 & 2521 & 2262 & 0.839 & 0.579 \\
90 & 9172 & 7964 & 2890 & 2289 & 0.839 & 0.579 \\
100 & 11,615 & 8150 & 3252 & 2349 & 0.839 & 0.579 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The increment experimental results of the calibration set sorted by the image dynamic range. size of 100. It is important to note that when the calibration set is too large, performing a forward pass on all images at once can put significant pressure on the computer's available memory. Therefore, when the computer has limited memory, batch processing can be used to handle the calibration set in smaller groups. The quantization component will average the results from multiple measurements to produce the final measurement result.
## 3 Fuzzy Logic Controller
### Principles of the Proposed Fuzzy Logic Controller  
Fuzzification, logic judgment, and defuzzification are the three consecutive stages of an FLC [28]. The fundamental structure of a fuzzy logic controller is shown in Figure 8.  
Figure 8: The structure of a fuzzy logic controller.  
Figure 7: A sectionalized P-V curve.  
At the stage of fuzzification, the numerical ratio, dP/dV (the change in PV power to the change in PV voltage, \(\Delta\)P/\(\Delta\)V) is translated into a linguistic variable via membership functions. The numerical offset, dV is the previous perturbation offset, \(\Delta\)V. \"dP/dV\" and \"dV\" are two input linguistic variables of the FLC. The output of the FLC, PT is the numerical offset for the next perturbation. The two input variables of the FLC are given by Equations (1) and (2):  
\[dP/dV=\frac{P[k]-P[k-1]}{V[k]-V[k-1]} \tag{1}\]  
\[dV=V[k]-V[k-1] \tag{2}\]  
At the beginning of every control interval of the FLC, the numerical inputs are translated into logic linguistic elements. According to the membership functions of \(dP/dV\) and \(dV\), the input variables can be classified as \"Positive Big\", \"Negative Big\", \"Positive Small\", \"Negative Small\", \"Positive Zero\", and \"Negative Zero\". Different from binary logic controllers, FLCs not only define variables with binary logic values, 0 and 1, but also provide degrees of variables. For example, according to membership functions, the FLC may consider the slope of the present operation point as: 50% PB, 10% PS, 5% PZ, etc. Hence, descriptions for variables could be more diverse and precise in terms of logic control. At the stage of the logic judgment, given fuzzified input variables, the FLC will look up its rule-base to find out all reasonable logic consequences. For example, rule #1 (IF dP/dV is \"PB\" and dV is \"PB\", then PT is \"PZ\") and rule #2 (IF dP/dV is \"PS\" and dV is \"PS\", then PT is \"PS\") can be simultaneously activated, when the FLC detects incoming input pairs, \(\left|dP/dV.dV\right|\). This is the outstanding characteristic of the FLC, compared to other binary logic controller. In the sense of statistics, multiple logic consequences will improve the accuracy of final weighted results which are expectation-type solutions. Defuzzification is an inverse procedure with respect to the fuzzification. In the processing of defuzzification, a linguistic output will be translated into a numerical value by adopting a weighting operation. The general expression of such operations is that:  
\[\text{Next perturbation}\frac{\sum u_{A_{i}}(x)\times B_{i}}{\sum u_{A_{i}}(x)} \tag{3}\]  
where \(u_{A_{i}}(x)\) is the membership function of the output variable fuzzy set and \(B_{i}\) is the fuzzy set's numerical solution.
## 5 Experiment Results and Analysis
### Results of Power Losses under Accurate Responses  
The experimental results under three scenarios are shown in Figure 4 as a holistic view.  
In Figure 4a, the upper curve illustrates the first scenario mentioned in Section 5.1, which means only one datacenter was integrated into the case_ieee30 at bus 15. The power of the datacenter varied along with the input solar generation. As shown in the figure, the peak loss is 25.6 MW. The curve in the middle illustrates the second scenario, in which two datacenters were integrated into case_ieee30 at bus 15 and 25. In this case, the two datacenters consumed an equal amount of power and the sum of the two datacenter's power was consistent with the input solar generation. Besides, the solar station was still at bus 9. We can find the peak loss value was smaller than in scenario 1, which was 25.25 MW. The lowest curve demonstrates the third scenario mentioned in Section 5.1. In this case, two datacenters were integrated into to case_ieee30 at bus 15 and 25 respectively and the power of the two datacenters was dynamically adjusted according to changes in solar power generation, while the total power consumption of the two datacenters was kept constant. We found that the loss value was smaller than in scenarios 1 and 2, which was 23.93 MW at the peak. In Figure 4b, which illustrates that for the results under cloudy/snowy weather conditions, the peak loss values in the same three scenarios were 20.99 MW, 19.82 MW, 18.08 MW respectively and the average value in the third scenario was still the lowest.  
Figure 3: Input solar generation power. (**a**) Sunny day; (**b**) cloudy/snowy day.  
From Figure 4 we can see that the curve of the optimal load-allocation strategy was almost always below the other two curves, which means that the dynamic load management methods can help to reduce the power losses of the grid system and thus provide savings in the operational costs. To examine the values more clearly, the corresponding statistical results are listed explicitly in Table 1. We also examined the scenario of putting three datacenters into the grid and the results are shown in Table 2. The datacenters were put at bus 15, 20, and 25 respectively. Here the two tables only give the results on a sunny day since it can be seen from Figure 4 that the other condition exhibited the same comparative trend.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 4 Applications of ML in D2D Communications
### ML for Network Caching  
Former research on D2D caching strategies implies perfect knowledge of the content distribution. However, ML-based caching policy that makes use of the demand history is not only highly promising but also recommended to save time and complexity. A comprehensive survey articles for different ML and DL techniques for caching are provided in [27; 28], respectively. In [29], the D2D caching problem was formulated as multi-agent MAB to maximize the total predicted caching reward. Q-learning was leveraged to learn how to manage caching choices.
## 3 Numerical Simulation
### Simulation Methods
#### 3.2.2 Parameter Calibration  
For different numbers of freeze-thaw cycles, the parameters of the contact model for the soil-stone mixture specimen are calibrated when it has not undergone freezing and thawing. This is because the contact model between the soil and stone is based on a parallel adhesion model, so the parameters requiring calibration include the effective modulus (emod), effective modulus of adhesion (pb_emod), tensile strength (pb_ten), cohesive force (pb_coh), and coefficient of friction (fric). For the stress-strain curve obtained from the indoor test, there are three main stages: firstly the linear elasticity stage, where the stress grows rapidly; then, the yield stage, where the strain hardens; and finally, the destructive stage, where the stress is basically unchanged. This corresponds to parameter calibration of the numerical simulation divided into the elasticity modulus and the compressive strength stages.  
The modulus of elasticity reflects the elastic properties and deformation capacity of a material when subjected to a force, which is expressed as the slope of the curve in the stress-strain curve. In the parallel bonding model, the slope of the curve can be adjusted by changing the emod and the pb_emod during the operation; in order to facilitate the calibration, the values of the emod and the pb_emod are controlled to be the same, and the value of the emod is adjusted continuously. It can be observed that there is a linear relationship with the elasticity modulus, and the elasticity modulus of the material is the same as that of the pb_emod during the operation, as shown in Figure 7a.  
Figure 7: (**a**) Modulus of elasticity fitting curve; (**b**) compressive strength fitting curve.  
Once the slope of the curve is determined and the compressive strength is calibrated, the ratio of pb_ten/pb_coh is determined to be constant, which is shown in Table 2. The values of pb_coh and pb_ten are adjusted so that the numerical simulation is as close as possible to the compressive strength of the indoor test, and this process is shown in Figure 7b.