esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Doherty Power Amplifier Circuit Design
### Output Network Design  
The output network design of a Doherty amplifier is critical for optimizing performance, especially in terms of impedance matching. Effective impedance matching ensures maximum power transfer from the amplifier to the load, which is particularly important in high-frequency applications like 5G. One common approach is using a combination of quarter-wavelength transmission lines and lumped elements to create an impedance transformation network, as shown in Figure 7. The design can be tailored to provide optimal impedance at the operating frequency while also accommodating the varying load conditions that occur during operation. By using quarter-wavelength transmission lines network technique, the same output power can be achieved under low supply condition without decreasing the output impedance of transformer. Meanwhile, transformer-based power amplifiers offer the advantages of compact size, high impedance transformation ratio, and wide bandwidth, making them widely adopted in high-power, high-efficiency PA designs [23; 24; 25].  
Figure 5: Architecture diagram of the typical Doherty power amplifier module.  
Figure 6: Simulated driver gain vs. input power.  
Figure 8 illustrates a three-dimensional representation of the LMN employing a six-layer laminated structure. The 1:4 turn ratio transformer was implemented using three uppermost metal layers, achieving an inductance ratio of 1:10 (Lp = 0.42 nH, Ls = 4.2 nH). This configuration features symmetrical narrow-turn windings in M1 and M3 layers electromagnetically coupled with a broader intermediate M2 layer to maximize coupling efficiency, all contained within an 800 \(\upmu\)m diameter footprint. The laminate-exclusive Pi network inductors, fabricated using only M1 and M2 layers for optimal Q-factor, complement the design. Power supply Vcc2 connects through the transformer's center tap with 10 nF surface-mount decoupling capacitors, requiring merely four external capacitors for simplified LMN implementation.  
ANSYS HFSS 2024R1 simulations of the 3D electromagnetic model incorporating bump pillars demonstrated total losses of 0.3 dB in the carrier path, 0.6 dB in the peaking path, and 0.5 dB transformer loss across the operational bandwidth. This topology provides  
Figure 8: The implementation of output matching circuits.  
Figure 7: Output matching circuit of the [PERSON] power amplifier.  
0.1 dB higher loss in peaking path due to the two-segment Pi network. But it is worth if considering the other benefits. In addition, to extend the bandwidth, the quarter-wavelength transmission line can be equivalently replaced by multiple LC networks, but this will introduce significant loss. Therefore, the circuit design here needs to consider the trade-off between bandwidth and loss.
###### Abstract  
An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method.  
AUV; neural network; VI; EKF +
Footnote †: journal: Applied Sciences
# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 1 Introduction
The node in the graph is each token, and the edge is determined by KL divergence to calculate the distribution difference between the two nodes, and the distribution of the nodes is generated by a trainable Gaussian Graph Generator. Then, the GCNs and dense connection layer are used to obtain local and non-local information of nodes. Finally, to realize joint decoding, we convert the triple (\(s\), \(r\), \(o\)) extraction procedure into quintuple (\(sh\), \(st\), \(r\), \(oh\), \(ot\)) extraction, where subject head, subject tail, object head, and object tail are represented as \(sh\), \(st\), \(oh\), and \(ot\), respectively. We use GlobalPointer to decompose the quintuple into pairs (\(sh\), \(st\)), (\(oh\), \(ot\)), (\(sh\), \(oh\)\(|\)\(p\)), and (\(st\), \(ot\)\(|\)\(p\)) to score. Figure 1: Examples of overlapping triples are divided into normal, single entity overlap (SEO), and entity pair overlap (EPO). Our contributions are as follows:  
1. We employ a Gaussian Graph Generator to initialize the text graph to avoid the problem of missing information caused by pruning. Each word in the sentence is a node in the graph. Edges are obtained by computing the distribution difference between two nodes by KL divergence to encourage information propagation between nodes with high distribution differences. 2. We decompose the quintuple extraction problem into scoring the four token pairs after transforming the triple extraction into a quintuple extraction task. Constructing (_sh_, _st_) matrices and (_oh_, _ot_) matrices using GlobalPointer, as well as (_sh_, _oh_ \(l\) _p_) matrices and (_st_, _ot_ \(l\) _p_) matrices under certain relations, allows for joint entity-relational extraction. 3. We conduct experiments to evaluate our model on the two NYT and WebNLG public datasets. The experimental results show that our model outperforms the baseline model in extracting both overlapping and non-overlapping triples, demonstrating the effectiveness of the graph module and joint decoding module.
## 4 Empirical Propagation Models
### Empirical Models for Vegetation
#### 4.3.1 Weissberger Model  
This model is applicable in situations where wireless signal propagation occurs in wooded environments, and the distance between the transmitting and receiving antennas must be up to 400 m, in the frequency range from 230 MHz to 96 GHz [44]. Its equation for calculating excess loss, added to the free space loss, is given by:  
\[Att_{WEIS}=\left\{\begin{matrix}1.33\times f^{0.284}\times d^{0.588},se&14\ \ \text{m}<d<400\ \text{m}\\ 0.45\times f^{0.284}\times d,se&0\ \ \text{m}<d<14\ \text{m}\end{matrix}\right. \tag{8}\]  
where  
* \(Att_{WEIS}\) is the excess attenuation according to the Weissberger model (dB);
* \(d\) is the distance between Tx and Rx (m);
* \(f\) is the system's operating frequency (MHz).
# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
## 4 Experimental Analysis  
### Experimental Data  
In this paper, the bearing fault dataset of CWRU is used to verify the performance of the TC-CNN fault diagnosis model [40]. The experimental facility is shown in Figure 5.  
The main components of this experimental facility include a 2-hp motor (left), a torque transducer (middle), and a dynamometer (right). The test bearing (6205-2 RS JEM SKF) supports the motor shaft. The signals are collected from the drive end bearing with a sample rate of 48,000 Hz, a motor load of 0 hp, and an average motor speed of 1724 rpm. The fault locations mainly included ball defects (BD), outer ring defects (OR), and inner ring defects (IR). Faults in this database of bearings are generated by electrical machining with fault sizes of 0.007 inches, 0.014 inches, and 0.021 inches. Each fault location has the above three damage conditions, representing different severity. Therefore, there are ten classes of data, and the information is listed in Table 1.  
The symbol @6:00 in Table 1 indicates that the fault direction is at 6 o'clock.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 2 Related Work
### Supervised Cross-Modal Hashing  
To obtain common binary representations, supervised cross-modal hashing methods use category information to maintain semantic discrimination. For example, semantic correlation maximization (SCM [42]) learns hash functions by constructing and maintaining semantic similarity matrices. Semantics-preserving hashing (SePH [43]) converts the semantic matrix into a probability distribution and minimizes the Kullback-Leibler divergence so that the learned hash codes are approximately distributed in Hamming space. Thanks to the prosperity of deep learning, researchers began to realize cross-modal retrieval by deep models that explore more discriminative features. Deep cross-modal hashing (DCMH [17]) is a pioneering achievement in this field, which perfectly combines hash function learning with deep feature extraction, and cleverly exploits the labelling information to construct similarity matrices, thus preserving the subtle similarity relationships in cross-modal data. Meanwhile, cross-modal hamming hashing (CMHH [44]) introduces a pairwise focusing loss based on exponential distribution. It penalises instances with similar semantic content but the Hamming distance exceeds a predefined threshold. Semi-supervised adversarial deep hashing (SSAH [45]) is a groundbreaking innovation that shifts the focus to self-supervised methods and integrates adversarial learning into cross-modal hashing, thus making significant progress in the field. Ranking-based deep cross-modal hashing (RDCMH [46]) uses maximum marginal loss to learn uniform Hamming representations. In addition, deep normalized cross-modal hashing with bi-direction relation reasoning (Bi-NCMH [47]) achieved excellent retrieval performance by constructing high-quality similarity matrices to capture similarity relations between instances with multiple labels. Despite the impressive performance of these techniques, their inherent limitations cannot be ignored: they rely heavily on manual annotation to obtain supervision.
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 1 Introduction
[12] found that the dispersion of Ni-Mo particles on different supports was as follows: SiO\({}_{2}\) > Al\({}_{2}\)O\({}_{3}\) > TiO\({}_{2}\), which led to significant variations in product distribution during the deoxygenation of rapeseed oil. [PERSON] et al. [26] found that the nature of the support modified the preferred route in the deoxygenation of stearic acid over Pd-containing catalysts. Thus, carbon and Al\({}_{2}\)O\({}_{3}\) were highly selective for decarboxylation, while SiO\({}_{2}\) led to higher rates of decarbonylation, although with lower activity. Mesostructured silica-aluminas [27; 28; 29] and zeolites [30; 31] have also been tested in the deoxygenation of fatty acids. In particular, Pd/Al-SBA-15 exhibited a remarkable selectivity toward n-heptadecane in the conversion of stearic acid under a hydrogen atmosphere [32]. In the present work, catalysts consisting of Pd supported over nanocrystalline and hierarchical ZSM-5 zeolites were studied for the liquid-phase transformation of stearic acid into liquid hydrocarbons under mild conditions (temperatures in the range 250-325 \({}^{\circ}\)C and 6 bar of nitrogen atmosphere). These supports were chosen because of their strong acidity and convenient accessibility, which was expected to favor the reaction of bulky compounds. In this regard, desilicated hierarchical ZSM-5 displayed in the catalytic fast pyrolysis of biomass enhanced activity for the conversion of bulky oxygenates, resulting in more aromatics and less coke [33]. Additionally, in the catalytic conversion of rapeseed oil, the presence of hierarchical porosity in ZSM-5 augmented the share of light olefins [34]. As is shown below, the combination of acid and Pd centers, as well as convenient accessibility, in the same catalyst led to remarkable activity in the transformation of stearic acid with a remarkable selectivity toward gasoline-range hydrocarbons.
## 3 Results and Discussion
### Removal Behavior of Pb\({}^{2+}\) by Acclimatized S. pasteurii  
Figure 8 shows the variation of removal ratios of Pb\({}^{2+}\) with increasing the concentrations of Ca\({}^{2+}\) at different urea concentrations. In Figure 8a-d, when the Ca\({}^{2+}\) concentration was 0.5 mol/L, the removal efficiencies of Pb\({}^{2+}\) were greatly improved in contrast with the tests with no Ca\({}^{2+}\), and nearly all the Pb\({}^{2+}\) ions could be removed from aqueous solutions. When both of the urea and Ca\({}^{2+}\) concentrations were 0.5 mol/L, the removal ratio of Pb\({}^{2+}\) could reach up to 98.0-100.0%, as shown in Figure 8a. Increasing the Ca\({}^{2+}\) concentration from 0.5 to 1.0 mol/L had little effect on the Pb\({}^{2+}\) removal when the urea concentrations were 0.5 and 1.0 mol/L, which can be observed from Figures 6b and 8a. When increasing the urea concentration to 1.5 and 2.0 mol/L in Figures 6d and 8c, the decline of the Pb\({}^{2+}\) removal ratio was nonnegligible, and the higher the urea concentration, the more the Pb\({}^{2+}\) removal ratio decreased.  
In Figure 9, we can observed that when there was no Ca\({}^{2+}\) in the solutions, the removal ratio of Pb\({}^{2+}\) exhibited a decreasing trend, suggesting that the high urea concentration was unfriendly to the Pb\({}^{2+}\) removal using _S. pasteurii_. The best removal ratio of Pb\({}^{2+}\), 71.4 to 97.6%, occurred when the urea concentration was 0.5 mol/L.
###### Abstract  
In order to effectively prevent and control the problem of water inrush from the throughput-type fault floor, based on the analysis of the case data of water inrush from the fault structure in the Fengfeng mining area, the types of karst water inrush from the floor are divided into 3 categories, 6 subcategories, and 17 fine categories. Four water inrush modes are summarized: composite structure water inrush, through-type fault water inrush, roof crack expansion water inrush, and floor vertical crack expansion water inrush. Based on the grey correlation theory and combined with the Analytic Hierarchy Process (AHP), this paper evaluates the water inrush risk of the F\({}_{\text{S5-1}}\) through-type fault zone from a micro individual perspective, taking the Xin'an Mine as the engineering background. Based on the water-rock coupling theory, the FLAC\({}^{\text{3D}}\) numerical simulation software is applied to study the disaster mechanism of the water inrush mode of the through-type fault. Through analyzing the changes of primary fracture stress and displacement of the intact floor strata and the aquireg, and the pressure changes of confined water in fault zones, the collaborative water inrush mechanism of the damage and fracture of the bottom plate rock layer, the expansion and connection of the original fracture, and the seepage and progressive intrusion of the pressure water during the advancing process of the working face is analyzed. The research results indicate that the evaluation result of the water inrush risk of the F\({}_{\text{S5-1}}\) through-type fault in Xin'an Mine is Level I, belonging to the high risk and consistent with the actual situation model, verifying the rationality of the evaluation model. The process of water inrush in the numerical model is summarized as follows: a rock pressure failure zone is caused by the rupture and instability of floor aouifuge; a progressive intrusion zone develops--water inrush is connected between rock pressure failure zone and progressive intrusion zone; floor lagging flood occurs. Under the combined action of mine pressure and confined water pressure, the progressive intrusion height of confined water is significantly increased. The research results have important reference basis for future coal mining planning. This provides engineering reference and practical experience for the prevention and control of water damage in structured bottom plates under similar conditions.  
through-type fault; grey correlation degree; risk assessment; type division; numerical simulation 2023  
[PERSON]  
1 School of Earth Sciences and Engineering, Hebei University of Engineering, Handan 056038, China; [EMAIL_ADDRESS] (H.Y.) [EMAIL_ADDRESS] (H.S.); [EMAIL_ADDRESS] (J.C.) [EMAIL_ADDRESS] (Z.J.)  
3
## 6 Simulation of Dynamic Collision Avoidance Algorithm
### Simulation Verification of Improved CS Algorithm  
In order to verify the effectiveness of the improved CS (ICS) algorithm, four sets of test functions were used to test the convergence speed and optimization accuracy of the algorithm. Among them, the \(f_{1}\) function is a unimodal function used to verify the convergence speed of the algorithm; \(f_{2}\), \(f_{3}\) and \(f_{4}\) are multimodal functions with many local extremum points, which are used to verify the optimization accuracy of the algorithm. The ICS algorithm was compared with the standard CS algorithm and particle swarm optimization (PSO). Parameters were set as follows: population number \(N=30\), search space dimension was 2, \(P_{CR}=0.2\), \(K=0.55\) was in the improved CS algorithm, detection probability \(P_{a}=0.25\) was in the CS algorithm, \(c_{1}=c_{2}=2\) was in the PSO algorithm and inertia weight \(w\) decreased linearly from 0.9 to 0.4 [43]. Test functions are shown in Table 1.  
To verify the algorithm by numerical simulation, the test functions were used. Their fitness curve and simulation results are shown in Figure 8 and Table 2.  
Through the verification of test functions and the results in Figure 8 and Table 2, it can be seen that the ICS algorithm improves the convergence speed and accuracy by adjusting the flight step-size adaptively. Mutation and crossover operations were introduced to avoid the population falling into local optima and enhance the ability of global optimization. Compared with the standard CS and PSO algorithms, the ICS algorithm performs better in convergence speed, accuracy and global optimization ability, and can meet the calculation requirements of the collision avoidance strategy when applied to USV dynamic collision avoidance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function Name** & **Function Equation** & **Search Scope** & **Optimal Value** \\ \hline Sphere & \(f_{1}=\sum\limits_{i=1}^{D}x_{i}^{2}\) & \([-5.12,5.12]\) & 0 \\ \hline Ackley & \(f_{2}=-20\exp(-0.2\sqrt{\sum\limits_{i=1}^{D}x_{i}^{2}})-\) & \([-32,32]\) & 0 \\  & \(\exp(\sum\limits_{i=1}^{D}\cos(2\pi x_{i}/D))+20+e\) & & \\ \hline [PERSON] & \(f_{3}=\frac{1}{4000}\sum\limits_{i=1}^{D}x_{i}^{2}-\prod\limits_{i=1}^{D}\cos( \frac{x_{i}}{\sqrt{i}})+1\) & \([-600,600]\) & 0 \\ \hline [PERSON] & \(f_{4}=0.5+\frac{(\sin\sqrt{x_{i}^{2}+x_{i}^{2}})^{-0.5}}{(1+0.001(x_{i}^{2}+ x_{i}))^{2}}\) & \([-10,10]\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test functions.  
Figure 8: Fitness curves of test functions. (**a**) Sphere function; (**b**) Ackle function; (**c**) Girewank function; (**d**) Schaffer function.
## 2 Materials
### Study Area and Field Campaigns
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & \begin{tabular}{c} **Central** \\ **Wavelength (nm)** \\ \end{tabular} & **Bandwidth (nm)** & **Spatial Resolution (m)** & **Purpose** \\ \hline B1 & 443 & 20 & 60 & Atmospheric correction (aerosol scattering). \\ \hline B2\({}^{1}\) & 490 & 65 & 10 & \begin{tabular}{c} Sensitive to vegetation senescing, carotenoid, \\ browning and soil background; atmospheric \\ correction (aerosol scattering). \\ \end{tabular} \\ \hline B3 & 560 & 35 & 10 & \begin{tabular}{c} Green peak, sensitive to total chlorophyll in \\ vegetation. \\ \end{tabular} \\ \hline B4 & 665 & 30 & 10 & \begin{tabular}{c} Maximum chlorophyll absorption. \\ \end{tabular} \\ \hline B5 & 705 & 15 & 20 & \begin{tabular}{c} Position of red edge; consolidation of \\ atmospheric corrections/fluorescence \\ baseline. \\ \end{tabular} \\ \hline B6 & 740 & 15 & 20 & \begin{tabular}{c} Position of red edge; atmospheric correction, \\ retrieval of aerosol load. \\ \end{tabular} \\ \hline B7 & 783 & 20 & 20 & \begin{tabular}{c} Leaf Area Index (LAI), edge of the Near \\ Infrared plateau. \\ \end{tabular} \\ \hline B8 & 842 & 115 & 10 & \begin{tabular}{c} LAL \\ \end{tabular} \\ \hline B8A & 865 & 20 & 20 & \begin{tabular}{c} NIR plateau, sensitive to total chlorophyll, \\ biomass, LAI and protein; water vapor \\ absorption reference; retrieval of aerosol load \\ and type. \\ \end{tabular} \\ \hline B9 & 945 & 20 & 60 & \
\begin{tabular}{c} Water vapor absorption, atmospheric \\ correction. \\ \end{tabular} \\ \hline B10 & 1380 & 30 & 60 &
\begin{tabular}{c} Detection of thin cirrus for atmospheric \\ correction. \\ \end{tabular} \\ \hline
**B11** & 1610 & 90 & 20 &
\begin{tabular}{c} Sensitive to lignin, starch and forest above \\ ground biomass; snow/ice/cloud separation. \\ \end{tabular} \\ \hline
**B12** & 2190 & 180 & 20 &
\begin{tabular}{c} Assessment of Mediterranean vegetation \\ conditions; distinction of clay soils for the \\ monitoring of soil erosion; distinction \\ between live biomass, dead biomass and soil, \\ e.g., for burn scars mapping. \\ \end{tabular} \\ \hline \hline \end{tabular}  
\({}^{1}\) The spectral bands in bold are ones used in this study.  
\end{table} 
Table 2: Sentinel-2 satellite imagery spectral band characteristics [35]. Figure 1: Location of study area and in-situ leaf area index (LAI) quadrats from three field campaigns. The background was the Sentinel-2A image acquired on August 20, 2018 and was shown in a false color band composition of R (8) G (4) B (3), with standard deviation stretch (**right**). The red color in the Sentinel image represents the vegetation (mainly crops and a few trees), the grey color represents the desert and bare land (**right**). The green dots represent the quadrats (including three sample points), and the blue polygons represent agricultural field boundaries (**lower left**).
## 4 Discussion
### Penetrometer Tests
#### 4.2.3 Effect of Application Rate  
Penetrometer tests performed with samples prepared at different application rates showed that the penetration resistance of most biopolymer-induced crusts slightly increased at higher application rates (Figures 10 and 11). For both tested soil types, the two-way ANOVA also indicated that the application rate significantly affects penetration resistance (Table 6). This trend is consistent with previous studies examining the effect of the application rate on the penetration resistance of biopolymer-treated soils [12; 13]. Thereby, the main difference is that previous studies revealed more distinct differences in penetration resistance than the present study did. However, this can be attributed to the generally low tested application rates and small intervals (i.e., 0.2, 0.3, 0.4, 0.5, and 0.6 L/m\({}^{2}\)) relative to the rates tested by previous studies (i.e., 1 and 2 L/m\({}^{2}\) by [13]; 1.3 and 3.5 L/m\({}^{2}\) by [12]).