esa-sceva/satcom-chunk-collection huggingface dataset:
# Directly Matching an MMIC Amplifier Integrated with MIMO Antenna through DNNs for Future Networks
## 4 Simulation Results of the Optimized System with a Combination of MMIC and MIMO Antenna Designs  
This section is devoted to presenting the outcomes achieved from the modeled MIMO antenna and MMIC design enabling us to optimize overall linearity specification of the system.  
The main challenge in communication systems is to have amplifiers and antennas at the common bandwidth with the perfectly matched impedance. The presented MIMO covers the frequency band of 6.24 GHz to 14 GHz, as Figure 7 shows, and MMIC design enhances the output power at the frequency range of 7.49 GHz to 12.44 GHz. Herein, the common frequency bandwidth is 7.49 GHz to 12.44 GHz for the whole system. Another important specification of the antenna is the S\({}_{12}\) specification, as Figure 8 shows. Figure 9 describes the resistance (Z\({}_{\text{real}}\)) and reactance (Z\({}_{\text{imaginary}}\)) results achieved from the simulations from 5 GHz to 10  
After modeling the MIMO antenna, the class-AB amplifier is deigned and optimized through the WIN 0.25 \(\upmu\)m Gallium Nitride (GaN) process technology and is biased at 28 V and 100 mA/mm. The SRFT method causes inductor-capacitor (LC) ladders at the input and output matching networks where the optimized amplifier leads a linear gain of more than 10 dB with more than 50 % power added efficiency (PAE) at the determined bandwidth with 38 dB output power (see Figure 11). Figure 12 presents the modulated signal response of amplifier after optimization in two cases: before digital pre-distortion (DPD) and after DPD. After employing the proposed methodology, the normalized power spectrum can reach the acceptable level, as desired. The IMDs are occurred due to the amplifier non-linearities. Phase distortion is a suitable specification for considering this effect [46]. Herein, Figure 13 presents the phase distortion before and after modeling at various frequencies.  
Figure 8: \(5_{12}\) of optimized MIMO.  
Figure 10: Radiation pattern of MIMO antenna at \(f_{1}=8\) GHz (pink), \(f_{2}=12\) GHz (blue): \(\phi=0\) (**left**), \(\phi=90^{\circ}\) (**right**).  
Figure 9: Predicated resistance (**right**) and reactance (**left**) of optimized MIMO through DNN.  
bandwidth without additional efforts, (4) enhance the linearity specification of overall system by considering the generated impedances from antenna side, and (5) provide an optimization environment where active and passive designs can be designed and optimized conjointly. In the recently published literature, such contributions are lacking. In contrast, our proposed method leads to reduced computational complexity.
# Three-Dimension Inversion of Magnetic Data Based on Multi-Constraint UNet++
## 2 Materials and Methods
### Network Architecture  
The network structure adopted in this paper is UNet++, which is improved based on the U-Net network. The U-Net network is symmetrically divided into left and right parts, with encoders on the left and decoders on the right. The encoder extracts features from the input data and compresses their dimensions, principally through convolution and downsampling, while the decoder recovers the dimensions and outputs of the feature graphs, principally through convolution and upsampling. In the process of decoding, U-Net fuses the encoder feature graphs with different scales but the same dimensions through skip connections [16]. In summary, U-Net achieves a precise and efficient performance in image segmentation tasks through the leveraging of its symmetric encoder-decoder structure and jump connections. Figure 3 shows a U-Net structure with a depth of \"4\". After the input data are processed by padding, features are extracted by a 3 \(\times\) 3 convolution kernel with a step size of 1, and then the data are processed by batch normalization (BN) and finally output to the next level using the activation function (ReLU).  
As shown in Figure 4, UNet++ is made up of multiple U-Nets of different depths, and dense jump connections are added on top of this. Compared with the U-Net network, the improvement in the UNet++ network comprises the following two points: (1) the addition of more intensive jump connection operations, which can make full use of the information of each layer of the network to extract and learn image feature information; and (2) UNet++ integrates U-Nets with different depths, and the depth of the network is \"variable\". In neural networks, the depth of the network is critical, and the effects achieved by networks with different depths are quite different. An insufficient network depth may lead to underfitting; meanwhile, networks that are too deep may lead to overfitting. In addition, the optimal network depths for different tasks and different datasets are different, and UNet++ integrated with different depth features can reduce the influence of unknown optimal depths [17; 18; 19].
## 4 Case Studies
### Basic Data  
The 33-bus distribution network is shown in Figure 2. The initial load is used as the base load. Parameters of DGs are given in Table 1 and ESSs are given in Table 2. The inverter capacity of WT, PV, and MT is 1.05 times of the rated power. The ramp rate of MT is 150 kW/h. Two CBs are connected to bus 8 and bus 13, whose capacity are both [0, 0.5] MVar with a step size of 0.05 MVar. An SVG with a capacity of [\(-\)0.1, 0.4] MVar is located at bus 30. The adjustment step of OLTC is 0.01 and the switchable range is [0.94, 1.06] pu. The initial storage capacity of ESS I and ESS II are 200 kW and 100 kW, respectively. Moreover, the state of charge (SOC) is always between 20% and 90% to extend the battery life. The time-of-use electricity price is shown in Table 3. The price of power loss is 0.68 V/kWh, while the operation and maintenance cost of MT and ESS are 0.38 V/kWh and 0.05 V/kWh, respectively. Figure 3 depicts the forecast data of the load [39] and the WT and PV power output (percentages of corresponding rated power). The forecast errors of WT and PV are assumed to be 10% of the mean value. By using the MCS and the backward scenario reduction technique, 20 representative scenarios are finally produced in this paper.  
For the 69-bus system, as shown in Figure 4, MT, WT, PV1, and PV2 are connected to buses 23, 47, 50, and 66, respectively. An SVG is located at bus 44. Two CBs are connected to buses 15 and 25. The parameters of DG, CBs, SVG, and ESS are the same as those in the 33-bus distribution network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **DG Type** & **Rated Power (kW)** \\ \hline \(11\) & MT & 200 \\ \(17\) & PV1 & 300 \\ \(24\) & PV2 & 300 \\ \(29\) & WT & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of DG.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & \(E_{s}^{\text{max}}\)**(MWh)** & \(P_{\text{ch}}^{\text{max}}\)**(MW)** & \(P_{\text{dis}}^{\text{max}}\)**(MW)** & \(\mathbf{\eta_{\text{ch}}}\) & \(\mathbf{\eta_{\text{dis}}}\) & **State of Charge** \\ \hline I & 1 & 0.3 & 0.5 & 0.9 & 0.9 & 0.2–0.9 \\ II & 0.5 & 0.15 & 0.2 & 0.9 & 0.9 & 0.2–0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of energy storage systems (ESSs).  
Figure 2: Topology of 33-bus distribution network.
###### Abstract  
The positive temperature coefficient (PTC) effect of the semiconductive layers of high-voltage direct current (HVDC) cables is a key factor limiting its usage when the temperature exceeds 70 \({}^{\circ}\)C. The conductivity of the ionic conductor increases with the increase in temperature. Based on the characteristics of the ionic conductor, the PTC effect of the composite can be weakened by doping the ionic conductor into the semiconductive materials. Thus, in this paper, the PCT effects of electrical resistivity in perovskite La\({}_{0.6}\)Sr\({}_{0.4}\)CoO\({}_{3}\) (LSC) particle-dispersed semiconductive composites are discussed based on experimental results from scanning electron microscopy (SEM), transmission electron microscopy (TEM) and a semiconductive resistance test device. Semiconductor composites with different LSC contents of 0.5 wt%, 1 wt%, 3 wt%, and 5 wt% were prepared by hot pressing crosslinking. The results show that the PTC effect is weakened due to the addition of LSC. At the same time, the injection of space charge in the insulating sample is characterized by the pulsed electroacoustic method (PEA) and the thermally stimulated current method (TSC), and the results show that when the content of LSC is 1 wt%, the injection of space charge in the insulating layer can be significantly reduced.  
Keywords:La\({}_{0.6}\)Sr\({}_{0.4}\)CoO\({}_{3}\); semiconductive layer; PTC effect; space charge; HVDC transmission +
Footnote †: journal: Applied Sci. 2020, 10, 2915; doi:10.3390/app10082915
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 5 Conclusions  
In this paper, LDPC-based product codes under iterative decoding are evaluated using FPGA. The product code with iterative decoding can reduce the error floor and reach an extremely low BER. Furthermore, considering the limitations of software simulation, the hardware test platform is built to accelerate the performance evaluation and evaluate the performance loss due to the fixed-point implementation. When BER is \(10^{-9}\), the product code with iterative decoder has the performance gain of 2.0 dB compared with the LDPC code of equivalent code rate. Meanwhile, the evaluation using FPGA also proves that using iterative decoding of product codes with LDPC codes and high-rate algebraic codes can achieve a good trade-off between complexity and throughput compared with a single long LDPC code.  
[PERSON] proposed the methods and wrote the paper. [PERSON] performed simulations and revised the manuscript. [PERSON] participated in the simulations and revising the manuscript. [PERSON] participated in revising the manuscript. [PERSON] participated in designing the scheme. [PERSON] participated in designing the scheme. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (61671324), the Director's Funding from Pilot National Laboratory for Marine Science and Technology (Qingdao), and the Seed Foundation of Tianjin University.  
The authors declare no conflict of interest.
## 2 Related Work
As stated before, mobility is another key factor affecting message diffusion effectiveness. There are several proposals, such as the ones presented in [37; 38; 39; 40; 41], which evaluate the message dissemination behaviour of the Epidemic protocol by focusing on the mobility patterns of the nodes. In these works, the authors explain the relationship between factors such as speed, mobility model, node density, and places. In [42], the authors introduce a new forwarding protocol, HURRy (HUMan Routines used for Routing), where the routing decision is based on probabilistic routing techniques like PRoPHET (Probabilistic Routing Protocol using History of Encounters and Transitivity), although it incorporates the contact duration of encounters (unlike previous approaches) to estimate the rating probabilities of all possible paths to a certain destination. Finally, other studies analyse how the management of mobile device hardware influences the message delivery probability. In the case of buffer management, some authors [43; 44; 45] evaluate the use of the buffer through priority rules in an attempt to deliver messages without performance loss in the information transmission process. The aforementioned studies provide an outlook of the most recent research works focused on the analysis and performance improvements for the diffusion of messages by improving collaboration among mobile users. These articles also show the importance of cooperative behaviour in opportunistic networking, and the trend towards models and applications that exploit the particularities of delay-tolerant wireless networks. However, these works do not analyse in an integrated way the performance of the message diffusion taking into account the duration of contact times, the buffer management schemes, and the relation between the contact duration time and the message size.
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Supervised Method  
The supervised method requires that the training set and the test set are indispensable, and the samples in the training set must be labeled [39], in which the training set is used to find the inherent laws of the samples and then apply the laws in the test set. Supervised methods can be divided into supervised methods based on metric learning and supervised methods based on representation learning. Among the above supervised surface defect detection methods, the common model based on metric learning includes  
Figure 3: Industrial product surface defect detection method based on deep learning.  
the Siamese Network; according to the three stages of defect detection, the method based on representation learning can be roughly divided into three categories: classification network, detection network, and segmentation network. Among them, the one that is commonly used as classification networks is Shuffle Net; the one that is commonly used as detection networks is Faster RCNN; the ones that are commonly used as segmentation networks are: FCN, Mask RCNN, etc. This section takes the network model mentioned above as an example and briefly introduces its research status in surface defect detection tasks. In the task of defect detection, the focus of the classification network is to solve the \"what\" problem, that is, to determine the type of the image (whether the image contains defects and what is the type of defects); the focus of the detection network is to solve the problem of \"where is the defect\", that is, to obtain the specific location information and category information of the defects by determining the location of the defects; the focus of the segmentation network is to solve the problem of \"how many defects\", that is, to segment the defect area from the background and obtain the location, category, attribute and other information of the defect.
## 3 Results and Discussion
### Adhesion Testing Analysis  
Figure 9 presents test results for the adhesion performance of the slurry-coated geotextiles. From Figure 9a, it can be observed that the clay coating demonstrates the best adhesion under trampling conditions. The trampling force effectively enhances the compaction of the coating, reducing internal pores and voids, thereby increasing the contact area and adhesion between the slurry coating and the geotextile surface. In contrast, coatings containing cement tend to crack under external forces as the number of force applications increases, leading to a reduction in adhesion. Figure 9b shows that all coated samples maintained their adhesion performance without significant decline after exposure to high-intensity rainfall, indicating strong water resistance and resistance to detachment under extreme rainfall conditions. From Figure 9c, it is evident that the adhesion degree of the slurry coatings remains between 95% and 99%, demonstrating that the coatings retain excellent adhesion performance, even under wind force. These results highlight the robustness of the coatings in various environmental conditions.  
Figure 8: Longitudinal and transverse tensile strength deviation: (**a**) longitudinal tensile strength; (**b**) transverse tensile strength.  
From the test results, it can be seen that these sprayed coatings can exhibit good adhesion under both simulated extreme environments and daily use conditions. Based on the above analysis, clay spray coating is more in line with practical application situations.
## 2 Background
### IoMT Context
#### 2.3.3 IoMT Devices  
[PERSON] et al. [19] classified IoMT devices according to their position, such as in-community, in-hospital, in-clinic, in-home, and onbody devices. In this paper, IoMT devices are classified according to their distance from patients, as shown in Figure 3 [21; 22; 23]:  
Figure 1: IoMT system architecture.  
* Implantable devices are fitted inside patients' bodies, usually to help doctors in diagnostic and surgical tasks. Examples of these devices are a capsule containing a camera but which is small enough to be swallowed, and an embedded cardiac sensor.
* Wearable devices are developed by embedding different sensors in wearable accessories. Examples of these devices are necklaces, wristbands, shoes, and watches.
* Bearable devices are equipped with patients' computers for specific purposes. These commonly help people to obtain different services in everyday life. An air quality monitor is an example of a bearable device connected to an asthma patient's smartphone or tablet.
* Nearable devices are anchored on doors, tables, or even beds. The patients do not need to carry these devices. The interaction with these devices is natural, such as with door, motion, pressure, or temperature sensors. They are expected to alert patients, their relatives, or healthcare providers when abnormal signs are gathered. Such devices are aware of the patient's context to help them monitor their activities, such as their quality of sleep and number of bathroom visits.
# Characterization of the Key Aroma Compounds of Shandong Matcha Using HS-SPME-GC/MS and SAFE-GC/MS
## 3 Results
### Multivariate Statistical Analysis of Shandong Matcha and Tencha  
#### 3.3.1 PCA  
Principal component analysis (PCA) can preliminarily show the overall difference between each group and the variation degree between samples within the group. The scores of PCA of different grades of tencha and Shandong matcha are shown in Figure 3. The PCA of HS-SPME data showed that the contribution rate of PC1 and PC2 was 59.34% and 12.86%, respectively, and the total contribution rate was 71.94% (Figure 3a). T-GS and M-GS were obviously separated from other tea samples, M-G1 and M-G2 partially overlapped and T-G1 and T-G2 achieved better distinction. In the PCA plots of SAFE, PC1 and PC2 contributed 39.56% and 25.81%, respectively, and the total contribution rate was 65.37% (Figure 3b). T-GS and M-GS are clustered on the left side of the figure, T-G1 and T-G2 are on the lower right side and M-G1 and M-G2 are on the upper right side. On the whole, three grades of both Shandong matcha and tencha can be well distinguished, especially for the distinction between high-grade and medium/low-grade Shandong matcha and tencha. However, in contrast to tencha, there was some overlap between medium- and low-grade matcha. This may be caused by the change in the aroma of tencha during the grinding process, which narrowed the difference between the two grades of matcha.  
Figure 2: Comparison of total volatile components between Shandong matcha and tencha. (**a**) HS-SPME. (**b**) SAFE.
# Motion Planning in UAV-Aided Data Collection with Dynamic Jamming
## 2 Problem Statement and Formulation  
In this study, a UAV is dispatched to collect data from a low-power SN on the ground. The data collection is performed with a time-division multiple-access protocol. Figure 1 shows the scenario: The UAV is flying at low altitude in a smart city. The wireless communication system comprises \(K\) SNs represented by the set \(\mathcal{K}=\{1,\ldots,K\}\), and the location of the SN \(k\) is represented by \(p_{k}=(x_{k},y_{k},0),k\in\mathcal{K}\). The transmitted power for each of the low-power SNs is fixed at \(P_{k}\). We consider the position of the SNs fixed and known for the trajectory design of the UAV. In practice, the position of the SNs can be obtained from the system database or identified with standard positioning techniques, such as with BeiDou systems or GPS [25]. In our scenario, the UAV arrives in the effective range of communication of the SN and activates the sensors for data transmission. It should be noted that the flight altitude of the UAV depends on the environment. Hence, it is simultaneously subject to air control by the local authorities. The complete flight altitude range is \(H\in[H_{\min},H_{\max}]\). The completion time of the data collection task is \(T\), while the 3D trajectory of the UAV is denoted by \(p(t)=(x(t),y(t),H(t))\), with \(0\leq t\leq T\). The completion time \(T\) is discretized into \(N\) sufficiently small and equal time slots with time discretization in which \(\delta\) represents the length of the slot and \(T=N\delta\). We introduce \(\mathcal{N}=\{1,2,\cdots,N\}\), in which \(\delta\) is a variable. Consequently, the trajectory of the UAV in the 3D space in the \(n\)-th slot is expressed as follows: \(p[n]=[x(n),y(n),H(n)],n=1,\ 2,\ \ldots\ N+1\). In addition, unlike in prior studies, there are \(\mathcal{M}=\{1,\ldots,M\}\) no-fly zones that are blocked by obstacles or interference sources. These are represented by the set, and the location of the projectedcenter of the no-fly zone \(m\) is \(p_{m}=(x_{m},y_{m},0),m\in\mathcal{M}\). For descriptive purposes, we refer to the no-fly zones as \"obstacles\" in what follows. Furthermore, there are \(J\) jammers in the environment, which are represented by the set \(\mathcal{J}=\{1,2,\cdots,J\}\), and the position of the \(j\)-th jammer is \(p_{j}[n]=(x_{j}(n),y_{j}(n),H_{j}(n)),j\in\mathcal{J}\). We assume that the position of the jammers is dynamic at a fixed height, which can obtain the location technology according to the radiation source [26; 27].
## 6 SAR Payload Performance Analysis  
The predicted performance of the S-STEP SAR sensor was determined via simulation during the system design phase. The performance of the SAR payload was accurately calculated prior to the satellite launch based on its ground test results and was subsequently verified by in-orbit tests and evaluations. Herein, the imaging mode design performance is presented in terms of the key performance parameters such as the antenna size, swath width selection, system impulse response function, peak and integrated sidelobe level, azimuth and range ambiguity ratio, NESZ, and data rate.