esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 1 Introduction
Numerous studies have investigated the disturbance effects of foundation pit excavation using this support system, employing laboratory tests, numerical simulations, and on-site monitoring. However, limited research exists regarding theoretical prediction methods and formulas for the spatial deformation of pile-anchorretaining structures. In this study, the spatial deformation mode of the pile-anchorretaining structure is thoroughly analyzed, and a spatial deformation prediction formula is derived based on the principle of minimum potential energy. A numerical simulation model is created using FLAC3D, a finite difference software. The primary objective is to present theoretical prediction methods and formulas for predicting the spatial deformation of pile-anchorretaining structures, addressing the research gap in previous studies. The practicality of the theoretical prediction formula is validated through a comparison of its results with numerical simulation outcomes and on-site monitoring data. Furthermore, a comprehensive analysis of the control effect of relevant factors is performed. These findings establish a robust theoretical foundation for predicting deformations in foundation pits utilizing pile-anchorretaining structures.
## 4 Performance Analysis for Different ERSR Radius Sets
### User Relay Selection Ratio Analysis
#### 4.1.1 Average Number Of UR in the ERSR of One USU  
The condition that a MG user can successfully receive is that \(x_{2}\) assigned with large PAF can be successfully decoded from \(X_{\tau_{1}}\). According to the transmission process of OMS in the first stage of COM-NOMA, for \(x_{2}\), reception SINR should be greater than \(SINR_{\mathcal{S}_{2}}\).  
When USU \(u\) locates in \(A_{n}\), the distance from USU \(u\) to BS, \(\gamma_{u}\), satisfies \(\frac{DI_{n}^{A_{n}}}{2}<\gamma_{u}<\frac{DI_{n}^{A_{n}}}{2}\). Only the MG user who locates within the ERSR of \(u\) has the chance to be selected as URs. Since \(r_{n}<<R\), distance from this MG user to BS approximates to \(\gamma_{u}\). Therefore, according to the analysis in [10], the probability that MG user successfully receives is expressed by  
\[P_{s}(\gamma_{u})=\exp\left(-\frac{\gamma_{u}^{\beta}}{\rho_{0}}\delta_{0} \right)\,, \tag{9}\]  
where \(\rho_{0}=\frac{P}{N_{0}}\) and \(\delta_{0}=\frac{2 SINR_{\mathcal{S}_{2}}}{a_{2}-a_{1}SINR_{\mathcal{S}_{2}}}\).  
Meanwhile, MG users in the cell with radius \(R\) follow Poisson distribution [18; 19; 20] with a density of  
\[\lambda_{MG}=\frac{M_{1}{+}M_{2}}{\pi R^{2}}. \tag{10}\]Depending on the nature of the Poisson distribution, \(\lambda_{\text{MG}}\) also represents the average number of MG users per unit area. When the area of ERSR is equal to \(\pi r_{n}^{2}\), the MG user density in this area is  
\[\lambda_{A_{n}}=\pi r_{n}^{2}\lambda_{\text{MG}}=\frac{\left(M_{1}{+}M_{2} \right)r_{n}^{2}}{R^{2}}. \tag{11}\]  
Therefore, the SU in the ERSR of \(u\) follows the Poisson distribution, which is verified by Figure 4, with a density of  
\[\lambda_{\text{s}}^{A_{n}}(\gamma_{u})=\lambda_{A_{n}}P_{\text{s}}(\gamma_{u}) =\frac{\left(M_{1}{+}M_{2}\right)r_{n}^{2}}{R^{2}}\exp\left(-\frac{\gamma_{u} \prescript{\beta}{\rho_{0}}}{\delta_{0}}\right). \tag{12}\]  
The density represents the expectation of Poisson distribution. So the average number of UR in the ERSR (i.e., SU in the ERSR) of \(u\) is equal to \(\lambda_{\text{s}}^{A_{n}}\).  
Since there is not only one USU at \(\gamma_{u}\), and then the number of USU is calculated in the next subsection.  
Figure 4: Probability of more than one user relay (UR) owned by USU versus the distance from him/her to base station (BS), in different number of MG users \(M_{i}\). (**a**): \(r_{n}=100\) m, \(\forall n\in[1,N]\). (**b**): \(r_{n}=75\) m, \(\forall n\in[1,N]\).
# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## 5 Result Analysis
### Fixed Sea State: Prediction and Control  
The trained LSTM model is employed within the control loop via MATLAB-Python co-design integration. As a supervisory control, the trained LSTM model predicts the incoming wave period, \(T_{p}\), based on WaveBot measurements \((z,\dot{z},F_{\text{PTO}})\), which is then used to determine and apply the proportional and integral gains \(K_{P}\) and \(K_{I}\). Predicted \(T_{p}\) is normalized since Min-Max scaling is applied during training. Therefore, it is scaled back to its original form using the Min-Max values of the features.  
Figures 6 and 7 present the simulation results from MATLAB/Simulink, demonstrating the effectiveness of the trained LSTM model in real-time wave prediction, and control applied based on that prediction. The first subplot of Figure 6 shows the setting of the fixed period case, in which the control parameters are set based on the accurate prediction of a 5 s sea state, and the prediction of the sea state based on observations of the system response. The subsequent plots display the variations in control parameters, \(K_{P}\) and \(K_{I}\), which adapt dynamically based on the predicted \(T_{p}\). Note that \(K_{I}\) gain is limited to \(-2\times 10^{4}\) due to the device constraint as shown in Table 1. The position, velocity, and PTO force (\(F_{\text{PTO}}\)) plots further illustrate the system's response to the incoming waves. Figure 7 illustrates the average power captured over time for two control approaches.  
The PTO power \(P_{\text{PTO}}(t)\) is computed using the instantaneous PTO force \(F_{\text{PTO}}(t)\) and the velocity of the device \(\dot{z}(t)\). The instantaneous PTO power is given by  
\[P_{\text{PTO}}(t)=F_{\text{PTO}}(t)\cdot z(t). \tag{25}\]  
The average power \(P_{\text{avg}}\) is then computed by dividing the total extracted energy by the total time duration \(T\):  
\[P_{\text{avg}}=\frac{1}{T}\int_{0}^{T}P_{\text{PTO}}(t)dt. \tag{26}\]  
In conventional control strategies, \(T_{p}\) remains constant, potentially limiting adaptability to changing wave conditions. In contrast, our approach updates \(T_{p}\) every 10 s based on real-time responses from the WaveBot device. This adaptation leads to a more responsive control strategy, yielding improved power capture. The results show that the proposed  
Figure 5: LSTM model prediction.  
method achieves an average power of 36.65 W, compared to 35.26 W with the fixed \(T_{p}\), slightly improving power capture compared to the fixed \(T_{p}\) approach.  
Figure 6: Simulation results for a \(T_{p}=5\) s sea state. The blue traces are for control, applied assuming a fixed \(T_{p}=5\) s sea state. The orange traces are for adaptive control, in which the period is predicted based on device motion and performance.  
Figure 7: Instantaneous and average power over for \(T_{p}=5\)s. The performance, in terms of average power, is as expected, with both control approaches being approximately equal in performance.
###### Abstract  
A total of five ultra-high-performance concrete (UHPC)-strengthened reinforced concrete (RC) columns and one RC column were built and subjected to eccentric compression testing to examine the force performance of UHPC-strengthened eccentrically compressed plain RC columns. This experimental study examined the crack progression, the damage morphology, the deformation ability, the maximum load-carrying capacity, and the ductile properties of the eccentrically compressed columns. It also investigated the impacts of eccentricity, the reinforcement thickness, and the addition of steel fibers on the effectiveness of reinforcement. The cracking load, peak load, and ductility coefficient of the UHPC-reinforced specimens were increased by 100.28%, 172.30%, and 56.30%, respectively, compared with the RC column at an initial eccentricity of 50 mm. As the eccentricity distance increased, the bearing capacity of the UHPC cocentrically compressed specimens decreased, and the deformation capacity increased. Increasing the steel fiber dosage within the appropriate range decreased the crack width of the specimen. The addition of 2% steel fiber resulted in a 24.8% increase in cracking load, an 8.96% increase in peak load, and a 2.60% increase in ductility coefficient compared to the addition of 1% steel fiber. However, the reinforcing effect of UHPC was weakened under high eccentric pressures. Based on the theory of concrete structure and mechanical principles, the formula for calculating the compressive bearing capacity of RC columns strengthened with high-performance concrete was proposed. The results of calculating the positive section bearing capacity of eccentrically compressed RC columns reinforced with high-performance concrete are in good agreement with the test values. The results of this paper provide an experimental basis and theoretical foundation for the cross-sectional design of UHPC eccentrically compressed columns.  
ultra-high-performance concrete (UHPC); reinforced concrete (RC) column; eccentric compression; ductility coefficient; bearing capacity calculation
## 1 Introduction
The position of each feed must be selected properly to minimize the differences between the different incident fields, so the transmitarray behaves similar to all the feeds. In this case, the feeds are proposed to be placed on a spatial distribution according to the arc defined by the central feed and the main planes of the reflectarray. In order to validate this purpose, the design of the transmitarray is carried out using dielectric-only cells, which is evaluated in a full-wave simulation placing the horn antenna at different positions. Then, the transmitarray is manufactured using a 3D printed technique, obtaining a low-cost prototype that is measured in a planar range facility to evaluate the different focused spots. Finally, measurements and simulations show a good agreement and validate the proposed technique to easily obtain a low-cost dielectric-only transmitarray for multi-focusing near-field applications, such as near-field radars for sensing as having different spots enables examining different points on a surface without an antenna displacement. Additionally, this solution can be applied for efficient wireless power transfer and information connections between devices placed close to each other.
## 7 Time Domain Optimization
### Combined Feedback and Feedforward Control
#### 7.1.2 Baseline Wind Turbine Controller  
The baseline controller used in this paper is a simplified version of the NREL 5 MW controller described in [37] and combines an Indirect Speed Controller (ISC) and a collective blade pitch controller (CPC) (see Figure 7 (left)). The main control goal of the CPC is to maintain the rated rotational speed \(\omega_{\text{rated}}\) in the presence of changing wind speed \(u_{\textit{eff}}\) by adjusting the blade pitch angle \(\beta\). The ISC additionally adjusts the generator torque \(M_{gen}\) to maintain constant electrical power above rated wind speed.
## 3 Design of a Wireless Sensor Network for a Power Quality Monitoring System
### Design of Wireless Sensor Node  
Figure 3 shows the wireless sensor node design on a pole transformer. The block diagram in Figure 4 illustrates the components, consisting of wireless sensor node, and their interactions with one another. Through the 3-phase transformer part, power quality is measured periodically. Measurements are carried out once every second, which is sufficient to provide the power quality monitoring service. Assuming that we store 3,600 data per hour, each hour requires a little over 500 Kbytes of space. This implies that 640 Kbytes of flash RAM are sufficient to store the data. The microprocessor has a number of functions: processing data collection from the sensing part, interfacing the data to the physical radio layer, and managing the radio network protocol. We use the OEM wireless Ethernet (IEEE 802.11b) module with a range of about 500 meters for peer transfers among neighboring pole transformers.
## 2 Strain Gauge Location Optimization Based on a Minimum Correlation Criterion
### Traditional Pearson Correlation Method  
Traditional Pearson correlation is a statistical means that can only be adopted to quantify the correlation degree between two different variables [36]. The linear correlation degree of two variables via Pearson correlation is defined as  
\[r(s_{i},s_{j})=\frac{\text{Cov}(s_{i},s_{j})}{\sqrt{\text{Var}[s_{i}]\text{Var}[s _{j}]}}=\frac{\sum_{i=1}^{n}{(s_{i}-\overline{s}_{i})\big{(}s_{j}-\overline{s}_ {j}\big{)}}}{\sqrt{\sum_{i=1}^{n}{(s_{i}-\overline{s}_{i})}^{2}}\sqrt{\sum_{i= 1}^{n}{(s_{j}-\overline{s}_{j})}^{2}}}, \tag{1}\]  
in which \(r\) indicates the correlation coefficient; \(\text{Cov}\big{(}s_{i},s_{j}\big{)}=\sum_{i=1}^{n}{(s_{i}-\overline{s}_{i}) \big{(}s_{j}-\overline{s}_{j}\big{)}}\) denotes the covariance between variable \(s_{i}\) and variable \(s_{j}\); \(\text{Var}[s_{i}]=\sum_{i=1}^{n}{(s_{i}-\overline{s}_{i})}^{2}\) represents the variance of \(s_{i}\).  
For operation load monitoring based on strain gauges, the corresponding strain values of a strain gauge under various flight loads are a vector. By computing the correlation coefficient between different strain monitoring points, the correlation degree between different pairs of strain monitoring points is obtained. Normally, a certain strain monitoring point can be adopted, and other highly correlated strain monitoring locations should be discarded. The aim of strain gauge location optimization is to maximally decrease the strain monitoring sensors while ensuring the accuracy of structural load identification. Therefore, it is necessary to select some strain gauge monitoring locations with the minimum correlation degree as the final strain gauge locations. In this case, the strain sensor network can be rationally arranged, and the operational loads of an aircraft wing will be accurately and efficiently estimated.  
However, in this method, the correlation degree between the strain data of two locations are evaluated, but it is hard to simultaneously measure the strain data correlation degree of several strain gauge locations. To tackle this challenge, an improved correlation measure is proposed.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 4 Effect of Key Structural Parameters of the PPWJ Device on Jet Characteristics
### Effect of Piston Maximum Displacement \(h\)  
Figure 8 shows the relationship between the jet characteristics and piston maximum displacement \(h\). The pulse pressure remains constant if the piston can complete its acceleration and enters a uniform motion state within a limited maximum displacement. When \(h\) increases from 20 mm to 55 mm, the pulse frequency decreases slowly from 8.33 Hz to 3.03 Hz, and the pulse duration increases linearly from 0.08 s to 0.22 s. The reciprocating motion speed of the piston is not affected by \(h\). As \(h\) increases, both \(t_{\rm r}\) and \(t_{\rm s}\) become longer, which leads to a decrease in pulse frequency.
# Enhanced Methyl _Tert_-Butyl Ether Removal by Mixed Consortium: Performance and Adaptability
## 3 Result and Discussion
### The Mixture of Three Pure Cultures Improved the Adaptability to Variable Temperature and pH  
There are various environment factors that can affect the MTBE biodegradation, and temperature and ambient pH are chosen for discussion here. Figure 5a shows the maximum biomass of strains and the removal of MTBE under different temperatures. It is speculated that low temperature was not conductive to the growth of strains, resulting in a reduction in biomass at 15 \({}^{\circ}\)C. Moreover, the results showed that the tested temperature ranges from 15 \({}^{\circ}\)C to 35 \({}^{\circ}\)C was adequate for all strains to degrade MTBE. The removal of MTBE was decreased at 15 \({}^{\circ}\)C due to the inhibition of enzymes activity involved in bacterial metabolism at low temperatures. With the increase of temperature, the enzyme activity was improved, so that the degradation efficiency increased gradually, reaching a maximum at 25 \({}^{\circ}\)C. However, as the temperature further raised to 35 \({}^{\circ}\)C, the removal efficiency reduced due to the inactivation of enzymes at high temperature [46]. Furthermore, the mixed consortium promoted MTBE removal at either low or high temperature, and the degradation efficiency was increased by about 10% compared with pure strains at 35 \({}^{\circ}\)C, suggesting that mixing three pure cultures enhanced the tolerance to temperature.  
Another common factor is the ambient pH, and the effect of pH on the growth of strains and MTBE removal is shown in Figure 5b. It is illustrated that all strains were sensitive to the ambient pH (\(p<0.05\)), and the growth of strains was significantly inhibited under either a strong acid or strong base. Furthermore, the removal of MTBE changed within a pH range of 4-10, and the degradation efficiency was higher under neutral conditions (pH = 7). The biodegradation of MTBE was decreased due to the inactivation of strains in strongly acidic or basic conditions, and the resistance of strains to acid conditions was much higher than that of basic conditions. It was confirmed that both temperature and ambient pH affected MTBE biodegradation (\(p<0.01\)). Moreover, compared with pure strains, a mixed consortium improved the adaptability to unfavorable conditions, especially at low or high temperatures. Additionally, further researches to promote MTBE biodegradation by regulating other environment factors are needed.
## 1 Introduction
When the mixer's working frequency reaches the terahertz band, the size of the entire circuit decreases dramatically and the parasitic effect makes it difficult to establish an accurate diode model, which is conducive to the design of the terahertz mixer. When the working frequency exceeds 300 GHz, the performance of the mixer has an obvious drop due to the limitations of Schottky diodes. The mixer's RF return loss cannot be as good as the low frequency band. Under this circumstance, pursuing a terahertz mixer with low conversion loss and good return loss becomes a challenge. Meanwhile, the demand for mixers with large working bandwidth  
Figure 1: The active circuit model of mixers combining single stage reduced matching technology with the SDM. Figure 2: The active circuit model of mixers combining single stage reduced matching technology with the GDM. is becoming huge. Therefore, it is necessary to open up a new road for a mixer with high performance. In this paper, to obtain a large working bandwidth, low conversion loss, and good return loss, a high-performance 0.34 THz sub-harmonic mixer combining two-stage reduced matching technology with an improved active circuit model is established and analyzed. Section 2 establishes the model of a Schottky diode, discusses the improved active circuit model and gives the design and simulation of the mixer. In Section 3, measurements are carried out to evaluate the mixer's performance. In the mixer tests, the SSB conversion loss is 8.95-12 dB in the range of 320-360 GHz. In the last section, a conclusion is presented.
# EBIC Imaging of Conductive Paths Formed in Graphene Oxide as a Result of Resistive Switching
###### Abstract  
The electron-beam-induced current (EBIC) method is utilized in this work to visualize conductive channels formed in graphene oxide as a result of resistive switching. Using metal-insulator-semiconductor (MIS) structures, an increase in the electron beam induced current by a few orders of magnitude as compared with the EBIC signal in metal-insulator-metal (MIM) structures is achieved. The mechanism of the EBIC image formation related to the conductive channels is explained by the separation and collection of the e-beam generated excess carriers by rectifying barrier nanocontacts formed at the graphene oxide/Si interface during resistive switching. It is shown that the collection efficiency of the formed nanocontacts decreases with the beam energy, in agreement with the theoretical predictions for the Schottky-like nanocontacts. An important advantage of the EBIC method is demonstrated in its ability to monitor the generation and elimination of high density conductive channels even when the current-voltage measurements cannot detect and separate these processes. EBIC study of the dynamics of the conductive channel formation can help better understand the underlying physical mechanisms of their generation.  
electron-beam-induced current; graphene oxide; metal-insulator-semiconductor structure; resistive switching; rectifying nanocontact +
Footnote †: journal: Applied
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 3 Noniterative Simultaneous Rigid Registration Method (NSRR)
### Estimation of the Translation Vector  
With the rotation matrix estimated, we solve for the optimal translations by solving Equation (1). For notation simplicity, we define \(\mathbf{Z}_{i}=\mathbf{R}_{i}\mathbf{X}_{i,i+1}-\mathbf{R}_{i+1}\mathbf{X}_{i +1,i}\) and \(\mathbf{Th}_{i}=\mathbf{T}_{i}-\mathbf{T}_{i+1}\); the minimization of function in Equation (1) can be rewritten as follows:\[\begin{split}&\operatorname*{arg\,min}_{\mathbf{Th}_{i}}\sum\limits_{i=1 }^{n-1}\left\|\mathbf{Z}_{i}+\mathbf{Th}_{i}\mathbf{e}_{i}\right\|_{F}^{2}\\ & s.t.\sum\limits_{i=1}^{n-1}\mathbf{Th}_{i}=\mathbf{0}_{2\times 1 }\end{split} \tag{7}\]  
Equating the partial derivative of Equation (7) with respect to \(\mathbf{Th}_{i}\) to zero, and using Sherman-Morrison formula [21], we obtain  
\[\mathbf{Th}_{i}=-m_{i}^{-1}\mathbf{Z}_{i}\mathbf{e}_{i}^{T}+\frac{m_{i}^{-1}}{ m_{1}^{-1}+\cdots+m_{n-1}^{-1}}\sum\limits_{j=1}^{n-1}m_{j}^{-1}\mathbf{Z}_{j} \mathbf{e}_{j}^{T} \tag{8}\]  
Hence, the final form of the translation vectors is:  
\[\mathbf{T}_{1}=\mathbf{0}_{2\times 1},\mathbf{T}_{i}=\sum\limits_{j=2}^{i} \mathbf{Th}_{j-1},i=2,\cdots,n \tag{9}\]