esa-sceva/satcom-chunk-collection huggingface dataset:
# A Three-Coil Constant Output Wireless Power Transfer System Based on Parity-Time Symmetry Theory
## 1 Introduction
[14; 15; 16], a scheme was proposed to realize long-distance wireless power transfer with multiple loads based on a relay resonant tank constructed using a double-coupled coil. In PT-WPT, [PERSON] et al. analyzed the system output characteristics as well as the frequency characteristics based on the coupled-mode theory for different numbers of relay coils [17]. In Ref. [18], based on a single relay coil, a parallel-series capacitor compensation topology was used for the receiving coil, increasing the freedom of coupling coefficient adjustment. In Ref. [19], based on a three-coil WPT, variable inductance and variable capacitance elements were added to cope with the system detuning caused by the coupling coefficient change between coils. However, the theories of these articles are based on coupled-mode modeling, which analyzes a system's resonant tank from an overall perspective and does not reflect the voltage and current gain characteristics of a system [10]. Knowledge of the output characteristics of a WPT system are essential in practice. Owing to the features of the PT-exact symmetry region, the authors of [20; 21; 22] realized the switching of the two charging modes of CC and CV for a dual-coil PT-WPT system using primary-side information only. There is yet to be an article reporting the analysis of the voltage and current gains and the control of the output of a three-coil parity-time wireless power transfer system. This is because modeling with the coupled-mode theory does not directly reflect the relationship between the electrical system parameters. In this study, a novel power control method based on a three-coil PT-WPT system is proposed. The proposed system's resonant tanks adopt capacitor series compensation. Unlike most output control systems, the proposed output control system only requires current and voltage information at the transmitting end and does not require communication at the receiving end. In addition, based on the frequency characteristics of the three-coil PT-WPT system, a working range detection method is proposed, which only requires information from the transmitting end. To save space for the model, the relay coil and receiver coil of the system were designed with multilayer coils, and the parameters of the coils were optimized. The research results of this article provide valuable insights for further research on the three-coil PT-WPT system. The rest of this paper is organized as follows. Section 2 models the circuit model of the three-coil PT-WPT system using the circuit mutual inductance theory and analyzes the gain characteristics and transmission characteristics of the three-coil PT-WPT system. Section 3 proposes an output control strategy for DC loads based solely on the current and voltage information of the primary side to realize the DC load using only the current-voltage information on the primary side. Section 4 presents an analysis of the parameters of the system and the design of an experimental prototype. Finally, some conclusions are given in Section 5.
## 1 Introduction
The contact surfaces in the reservoir that separate fluids with different densities (often between gas, oil, and water) result from gravity and capillarypressure [20]. Determining the point (depth and gradient) at which the water and hydrocarbon phases come into contact is intricate yet crucial in analyzing the reservoir because just the slightest change in pore type can cause fluid contacts to be misinterpreted [19]. The Bredasdorp Basin is ideal for this study because it is an active hydrocarbon-producing basin, and this study focuses mainly on the Cretaceous sandstone reservoirs because of their good petrophysical properties. Two separate studies on pore pressure prediction [8] and fluid contact determination [19] have been conducted in the neighbouring offshore basins, Pletmos and Orange Basins, respectively. However, no single published study has been conducted in the Bredasdorp Basin's Cretaceous sandstone reservoirs to integrate pore pressure prediction and fluid contact determination with the aim of enhancing the predictability of these parameters and investigating their influence on one another. Therefore, a knowledge gap exists concerning the pressure zones in the Cretaceous sandstone reservoirs, the nature of fluid contacts in these reservoirs, and the best pore pressure prediction method. Over the last decades, several studies [8; 13; 14; 15; 16; 22; 23; 24; 25; 26] have predicted pore pressures using wireline log and seismic data. Refs. [18; 27; 28] determined fluid contacts in the reservoir formation using formation pressure and wireline log. Several models have been proposed and applied to predict pore pressure using in-process data (drilling data). To select the most suitable pore pressure prediction methods for future studies in the study area, the methods proposed by [PERSON], [PERSON], [PERSON], and [PERSON] and Wood were utilized on three wells. All these methods have several strengths and weaknesses; as such, we decided to utilize them to compare the results to see which is more effective in this basin. We also determined fluid contacts within the sandstone reservoirs using well logs and RFT data. Lastly, we attempted to show the relationship between the pore pressure and fluid contacts.
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 2 Discharge Models in Liquids
### Direct Impact Ionization  
Description of the pre-breakdown (streamer) phenomenon in liquids, resulting from direct impact ionization, is similar to the electrical breakdown process in gases [13; 14]. Nevertheless, because of the higher liquid densities, electrons free paths are significantly shorter, which also affects the increased electron beam scattering [16]. Acceleration of free electrons to reach required ionization energy in a short time and free paths is difficult in such conditions and few requirements must be fulfilled. The most important is to provide appropriate voltage rise rate on the electrodes. It allows rapid acceleration of free electrons. As shown, among others, in [14; 16], this process requires extremely high intensities of the electric field at the level of 220 MV/cm. Pulse rise time should be maintained at picoseconds level [20; 21]. The second possibility of impact ionization is to create areas characterized by reduced mass density. In such areas (gas bubbles), electrons free paths between next collisions are longer. In addition, in the areas of lower density, the electrical breakdown voltages are reduced [6; 14; 16].
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 2 Materials and Methods
### Optimal Input Selection
#### 3.3.3 Optimal Input Selection Using Forward Selection  
In this part, the \(FS\) technique is utilized as a nonlinear input selection approach in order to choose the optimal input set between nine parameters. The \(ANN\) method with one layer is used to implement the nonlinear analysis of each set according to the empirical rule proposed by [PERSON] and [PERSON] (1997). The authors have demonstrated that the optimal node number in a simple network is twice the number of input parameters [57]. Firstly, correlation between target and other geotechnical parameters is estimated. Secondly, the parameter having the highest correlation coefficient, (i.e., \(WL\) with \(R\) = 0.55) is chosen as the first and the most significant parameter. Then, the other inputs are added into the model one by one and launch a new \(ANN\) modelling. The one providing the best modeling result (high determination coefficient (\(R^{2}\))) is selected as a new input and gathered into the formerly selected parameters. This work is repeated several times until that appending other parameter to the input set does not improve the modeling performance. Hence, if the determination coefficient increases more than 5%, the novel parameter is selected. Finally, input parameters having the most heavy influence on the target are selected and others ones are rejected. Table 9 shows the results of the forward selection procedure of different input models. The findings indicate that \(WL\), \(Yd\), \(W\), and \(PI\) are selected as inputs for modeling \(C_{s}\) according to the forward selection procedure, and the other parameters are eliminated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Parameters**} & \multicolumn{3}{c}{**Gamma Test Statistics**} \\ \cline{2-4}  & \(\Gamma\) & \(V_{ratio}\) & Mask \\ \hline \(All\) & 0.00014759 & 0.4054 & 11111111 \\ \(All\)-\(Sr\) & 0.00014653 & 0.4025 & 011111111 \\ \(All\)-\(Z\) & 0.00015130 & 0.4156 & 10111111 \\ \(All\)-\(Y_{h}\) & 0.00014672 & 0.4030 & 11011111 \\ \(All\)-\(Y_{d}\) & 0.00014689 & 0.4034 & 11101111 \\ \(All\)-\(W\) & 0.00017471 & 0.4798 & 111101111 \\ \(All\)-\(e_{0}\) & 0.00014712 & 0.4041 & 11110111 \\ All-FC & 0.00019292 & 0.5299 & 111111011 \\ All-WL & 0.00017584 & 0.4829 & 111111101 \\ \(All\)-PI & 0.00016223 & 0.4456 & 11111110 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimal input variable nomination using GT.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input Parameters**} & \multicolumn{3}{c}{**Gamma Test Statistics**} \\ \cline{2-4}  & \(\Gamma\) & \(V_{ratio}\) & Mask \\ \hline \(WL\) & 0.00020688 & 0.5944 & 1000 \\ \(WL\), \(PI\) & 0.00018845 & 0.5176 & 1100 \\ \(WL\), \(PI\), \(FC\) & 0.00017979 & 0.4938 & 1110 \\ \(WL\), \(PI\), \(FC\), \(W\) & 0.00013524 & 0.3714 & 1111 \\ \hline \hline \end{tabular}
\end{table}
Table 8: GT statistics of different input models.
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 5 Simulation Results  
We take advantage of the ns-3 network simulator--in particular, the mmWave module of the simulator that was built by NYU Wireless and the University of Padua as noted before [2, 27]. This module was developed specifically with the aim of simulating 5G cellular networks that are capable of functioning at mmWaves. In order to handle the 5G new radio frame structure and the 5G Numerologies, it contains specialized PHY and MAC classes. It supports carrier aggregation (CA) at the MAC layer and also supports dual connectivity (DC) with LTE BS.  
We approached our adaptive fractional packet duplication schemes in three different ways as mentioned below in detail. SINR threshold or Delta SINR uses two or more RF channels to duplicate packets when their RF characteristics are not very different from each  
Figure 7: Instantaneous and the best SINR of the two base stations.  
other. The second method uses the fade threshold where, if a signal drops below a certain value, packets will be duplicated on two or more RF links. The third method is distribution-based where our rate of packet duplication depends on the random exponential variable.
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
## 3 Analysis of Influencing Factors of Blade Tip-Timing Waveform Shape
When the blade vibration amplitudes are different, the highest point of each tip-timing waveform appears at different times. As the vibration amplitude increases, the time interval of waveform shifting to the left is larger, but the time of blade tip passing through the sensing range of the sensor is basically unchanged. Figure 5c, Case 3, is the case of negative vibration synchronization. When the blade vibration amplitudes are different, the times at which the highest points of each tip-timing waveform appear are basically coincident. With the increase of vibration amplitude, the waveform expands outward and the time for the blade to pass through the sensing range of the sensor becomes longer. Figure 5d, Case 4, is the case of vibration hysteresis. When the blade vibration amplitudes are different, the timing of the highest point of each tip-timing waveform is different. As the vibration amplitude increases, the time interval of waveform shifting to the right is larger, but the time of blade tip passing through the sensing range of the sensor is basically unchanged. Figure 3: Vibration displacement and vibration velocity of blade tip. Figure 4: Comparison of blade tip timing waveform with or without blade vibration: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4. Figure 5: Influence of vibration amplitude on blade tip timing waveform: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4. Figure 6 shows the vibration displacement and vibration velocity curves of the blade tip passing through the sensing range of the sensor obtained by using the waveform mapping theory. Figure 6a-d covers, respectively, the vibration displacement and vibration velocity of the blade tip passing through the sensing range of the sensor mapped when the vibration is in positive synchronization, in vibration ahead, in negative synchronization and in vibration hysteresis. It can be seen from Figure 6 that the values and variation trends of the vibration displacement and vibration velocity of the mapped blade tip passing through the sensing region of the sensor are consistent with those of Case 1, Case 2, Case 3 and Case 4 region in Figure 3, respectively, which verifies the correctness of the waveform mapping theory.
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 6 Performance Evaluation
### Comparison between Mobile and Static Scenarios  
For the evaluation of the proposed link quality-based forwarding strategies in a mobile scenario, the delivery ratio, the mean E2E delivery delay, and the total network overhead experienced in test C1 are presented. As shown in Figure 1(a), the delivery ratio has similar behavior compared to Test A1 and Test A2, i.e., PAmuLQE-U-E2E has a lower delivery ratio, and the other proposed variants have a similar ratio compared to Epidemic. In accordance, Figure 1(b) shows a higher delay for PAmuLQE-U-E2E.  
For scenarios with a higher number of USVs, PAmuLQE-B-E2E starts showing a higher network overhead due to the broadcast of the ACK, and because a higher number of nodes can re-send the same packet (Figure 1(c)). The only disadvantage of PAmuLQE-NACK in mobile scenarios is that, when a USV with a packet navigates away from the swarm, and the only available connection is to a USV where the packet already passed, this packet is lost. This happens because this USV is sending the packet to a USV that already had it, which makes it unable to reach the gateway until a new connection is established with a USV that has never received the same packet.  
Two more experiments were conducted, D1 and E1, to compare the proposed strategies in scenarios with more USVs and more time of simulation. The results are shown in Figures 25 and 26.  
As can be observed in the obtained results, when increasing the number of nodes, the epidemic strategy has no longer a high delivery ratio, a fact that occurs because in this strategy each packet is  
Figure 24: Test C1, Scenario C.  
blindly replicated by its neighbors, therefore increasing the number of copies in the network, and, consequently, the number of duplicated packets received by the gateway.  
The PAmuLQE-B-E2E also starts lowering its delivery ratio when the number of network nodes increases because the number of acknowledgements that are sent in broadcast mode also increases, therefore reducing the available bandwidth for data transmissions. Moreover, because this strategy has a mechanism of retransmitting a packet if an acknowledgement is missing, when the number of nodes increases, the number of unnecessary packet retransmissions also increases. For this reason, overall, the best strategy in large scenarios in the PAmuLQE-NACK, since the neighbor acknowledgements provide the existence of one packet in the network at each time while achieving a high delivery ratio.  
Figure 26: Cont.  
Figure 25: Test DI, Scenario D.
# Semantic Multigranularity Feature Learning for
## 4 Experiments
### Key Design Choices
Feature vectors with higher dimension increase the capacity of subsequent classification modules and have stronger feature representation ability. * **Feature maps rearrangement**. Table 4 reports the effect of feature maps rearrangement. We compare the models with vs. without feature maps rearrangement. A consistent performance gain is observed on both datasets. This is because feature maps rearrangement of different branches can learn subtle fine-grained features of different semantic levels. These features are likely to be complementary to  
and 96.5%, with training ratios of 0.1 and 0.5, respectively. When the training ratio is 0.2, SMGFL-Net has the second-best accuracy of 93.7%, the same as InceptionV3-CapsNet, and is only 0.4% worse than Progressive-MG. The results demonstrate that SMGFL-Net can achieve superior performance with only 10% of data used for training, outperforming the second best model, ResNet50, by 1%. * For the global methods, ResNet50 is better than VGG16 under all three training ratio settings, which is reasonable since ResNet50 is featured by shortcut connections, allowing an algorithm to train deep networks; thus, it can be justified that the 50-layer ResNet50 outperforms the 16-layer VGG16. Moreover, our model adopts ResNet50 as a backbone and obtains a gain of 1%, 0.1%, and 0.7% for the three training settings, respectively. * The only evaluated local method bilinear-CNN performs the worst (91.2%) with a training ratio of 0.5, indicating that the localized features captured by the bilinear-CNN are less effective for this task. * For the FGVC methods, Progressive-MG stands out, with the best accuracy when the training ratio is 0.2. From a design point of view, Progressive-MG also employs multigranularity feature extraction but with a key design difference from ours, i.e., their jigsaw puzzle generator is applied on the input image, leading to meaningless edges that may destroy certain features. Results show that our method outperforms Progressive-MG by 0.6%, meaning that the patch rearrangement at the feature map level is more effective for the RS scene classification task. On the other hand, with subnetwork specifically designed for localizing critical regions, NTS-Net still fails to achieve satisfying accuracy. When the training ratio reduces to 0.1, NTS-Net shows the worst accuracy of 84.5%; as the training ratio goes up to 0.5, its accuracy is raised to 93.2%, meaning that NTS-Net needs more training data to close the prediction bias. * Lastly, for the RS methods, VGG16-MSCP does not perform well, with an accuracy less than 90% for training ratios 0.1 and 0.2. InceptionV3-CapsNet, on the other hand, presents the same accuracy as our method with a training ratio of 0.2 but is worse than ours by 2.6% with a training ratio of 0.1. MGML-FENet, as an effective remote sensing image classification method, achieve the best performance when the training ratio is 0.2. However, SMGFL-Net can achieve better performance when the training ratio is 0.1.
## 6 Discussion, Implications, and Limitations
### Implications for Practitioners from the Validation of the Approach  
Regarding the proposition made, simulation results show that GHG abatement costs vary among different shipment categories (X, Y, and Z) in the URA scenario for Case 1, but less in the ARA scenario. For Case 2, a closer statistical investigation of the abatement costs on the shipment level showed that the abatement costs significantly differ between some inventory groups. Therefore, the more crucial ABC/XYZ dimension for the abatement costs was found to be the XYZ dimension.  
The primary determinant in logistics is the logistics costs. When these costs vary among different alternatives, it implies that the likelihood of implementing certain alternatives is greater than others [14; 15]. However, for decision-makers utilizing our developed method, the objective is not only to maximize economic savings but also to reduce emissions. To combine these two quantitative decision criteria, abatement costs can be calculated. We have demonstrated that the cost and emission impacts of shifting freight transport from road to combined road-rail transport differ depending on the transported goods. Briefly said, from a freight owners' perspective, GHG abatement costs for intermodal transportation vary, depending on the class of the inventory being transported. The most impactful variable, thereby, is the demand characteristics (order frequency and volume), described by the demand stability of the XYZ dimension. Therefore, we see that inventory holding costs, which are affected by the ABC dimension, are a less important criterion.  
Nevertheless, our findings concurrently highlight the necessity for a nuanced assessment of the applicability of combined transport for different use cases to effectively exploit potential savings. The generalized assumption of break-even distances, dispatching strategies, or costs is not feasible, even within a single company. At the same time, the economic and environmental impact of a shift towards combined transport is difficult to evaluate in detail.  
Since costs differ between some of the ABC/XYZ classes, and the ABC/XYZ analysis is a straightforward and well-known measure, this study provides practitioners with an efficient method for identifying potential promising use cases for combined transport. Even though the results differ between our simulation cases, a cost-effective reduction in emissions was demonstrated for the shift of X goods in every instance. For Y goods, the statistical tests showed a higher risk for positive abatement costs, and the Z goods varied across the case studies. This insight allows for minimal effort in identifying suitable goods for combined transport and provides a starting point for CRRT newcomers, decreasing the entry barrier to the intermodal system. Therefore, this approach aids industrial enterprises in efficiently decarbonizing their logistics.
# Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
## 3 Materials and Methods
### Location Estimation
#### 3.4.2 Magnetic Localization
The intuition is that the spatially closer the location candidates are, the higher the probability is to find the accurate user location as shown in Figure 6c. Then \(K_{2}\) number of filtered location candidates are selected where \(K_{2}\) is set to 7. The value for \(K_{1}\) is set higher so that we can initially get location estimates and later filter them to select the best among them using the calculated weight \(W_{L_{E}}\). We follow the same procedure and Figure 6d shows the results for KNN and mKNN. The results show that mKNN can estimate a more accurate location of the user than that of KNN. We use two consecutive frames to calculate user current location following the same procedure. So, the location calculated from the first frame serves as the starting point for the pedestrian dead reckoning (PDR). After that EKF is applied to the locations that we get from PDR and the magnetic database. PDR involves the distance calculation traveled by the user and the heading estimation \(h\). The distance calculation is done using step count \(S_{c}\) in a particular frame and step length \(S_{l}\) estimation. Step detection and heading estimation are calculated using the algorithm proposed in [38] which is based on peak detection in accelerometer data. Before peak detection, the data is preprocessed with a low pass filter to remove noise. Once step detection has been performed, step length of \(i\)th step \(S_{l_{i}}\) is measured using Weinberg model [72]:  
\[S_{l_{i}}=\sqrt[]{a_{max_{i}}-a_{min_{i}}} \tag{6}\]  
where \(a_{max_{i}}\), and \(a_{min_{i}}\) represent the maximum and minimum acceleration of \(i\)th step, respectively, while \(c\) is the step constant. Step constant \(c\) is calculated in the calibration process and depends upon the height and step length of a user. Once the step count \(S_{c}\), step length \(S_{l}\), and heading \(h\) are found, current location is calculated:  
\[x_{i}=x_{i-1}+S_{c}*S_{l_{i}}*cos(h) \tag{7}\] \[y_{i}=y_{i-1}+S_{c}*S_{l_{i}}*sin(h) \tag{8}\]