esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Scatterometer Wind Retrieval Correction  
Single-Doppler analysis winds provide an estimate of the local wind field that is unaffected by attenuation in the presence of rain, in contrast to scatterometer wind retrievals that can be affected by rain rates as low as 6 mm h\({}^{-1}\)[53]. Moreover, the inability of weather forecasting models to resolve mesoscale phenomena can further complicate the ambiguity selection process, leading to false ambiguity wind selection. The winds retrieved by Doppler radars are reported at an increasing height with distance due to the earth's curvature. At relatively close distances from the radar they can be representative of the near-surface wind conditions [33] and therefore be used to correct the low-quality scatterometer retrievals over precipitation. This process entails the manual examination of the scatterometer's multiple/unambiguous wind direction solutions within a wind vector cell, which in the presence of rain should be more than 2 with similar probabilities, and the selection of the one that more closely matches the Doppler wind direction. Elevated magnitude of wind shear at the low levels of the troposphere is more conducive to generation of quasi-linear convective system [54], which can complicate the proposed correction of the wind direction. Using information of the MCS's location and precipitation one can approximate the wind direction by relying on (radial) outflow processes.
## 3 Results and Discussions
### Compressive Strength
#### 3.2.5 Influence of Extra Water  
The preliminary mixes of group C with 35% AAS content exhibited poor workability characteristics showing slump values from 70 mm to 35 mm when compared to mixes of group A (40% AAS content). In group E (35% AAS), additional water (12 kg/m\({}^{3}\)) was used to enhance the workability of GPC mixes. The influence of additional water on the strength properties of FS-GPC has not been broadly stated yet. Thus, the influence of extra water in the mix was studied to find whether it can be used to enhance the workability of GPC mix with an acceptable strength.  
As illustrated in Figure 7, the compressive strength of all mixes of group E was reduced as compared to their counterparts of group C due to the extra water. However, the influence of extra water was observed to be less prominent on mixes with high slag content. The 28 days compressive strength of group E mixes, E1-S10 (17 MPa), E2-S20 (36 MPa), E3-S30 (51 MPa), and E4-S50 (63 MPa) was reduced by 34%, 22%, 14%, and 8% respectively as compared to their corresponding mixes of group C viz. C1-S10 (26 MPa), C2-S20 (46 MPa), C3-S30 (59 MPa), and C4-S50 (69 MPa). Figure 10 shows the influence of extra water on compressive strength of geopolymer concrete mixes (Group C vs. Group E).  
When AAS content was reduced to 35% and extra water was used to improve the workability of the mix, then the concentration of the AAS decreased; as a result, fewer SH ions were available in the mix to dissolve the aluminosilicates. The compressive strength of the mix containing 50% slag content as binder was least influenced (8% strength reduction) by the addition of extra water. The presence of slag in the mix led to the formation of C-S-H gel in the matrix along with the geo-polymeric gel. This gel leads to substantial increase in strength; consequently, if the amount of slag in the mix is increased then more C-S-H gel will be formed. Thus, if the amount of AAS is reduced and extra water is added to the mix containing higher slag content, there still will be enough OH ions present in the matrix to form enough C-S-H gel. Hence, the result is the strength comparable to the mixes without the extra water.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 4 Scenarios Simulation Under Adjacent Building Conditions
### Effect of Changing the Depth Factor of Adjacent Buildings  
The findings of this section, as presented in Sections 4.1 and 4.2, show that although the impact was not significant under the MC model, it became apparent that when the basement depth exceeded 0.5 He, the effect on the wall tends to converge. The maximum impact was observed at a depth of 0 He, followed by 0.5 He, and the least impact at 1.0 He. This indicates that the load from above-ground structures was transmitted through the basement depth, and as the depth increased, the lateral earth pressure on the shallow diaphragm wall decreased. When the basement depth reached 0.75 He, the maximum deformation was similar to that at 1.0 He. Similar results were observed with the HS and HSS models. The analysis across these three models confirms that the basement depth of adjacent buildings should be considered as a factor in wall deformation. Typically, 2D or 3D  
Figure 12: Comparison of maximum deformation under different loads (HSS model): (**a**) 5F; (**b**) 12F.  
numerical software simulates loads on the surface, which may not fully capture the impact of adjacent building configurations and could potentially overestimate wall deformation.
# A Transformer-Based Channel Estimation Method for OTFS Systems
## 3 Proposed Transformer Estimation Algorithm
### Architecture of the Proposed Channel Estimation Algorithm
Compared to the non-residual network, when using the ResNet to calculate a partial derivative of \(x\), a constant term is added to the derivation result, which keeps the model from losing gradient during training. Layer normalization serves to normalize all dimensions of each input sequence, thus speeding up model convergence and alleviating the gradient dispersion problem in deep network engineering.
## 4 Data Analysis and Results
### DInSAR Analysis
In fact, a few centimeters of variation of LoS displacement measurements (still within the error budget of the conventional DInSAR technique) might also be related to the difference in viewing the angle between the ascending and descending satellite observations, and the contribution of the horizontal (mainly E-W) motion component. Therefore, the interpretation of the observed LoS displacements is in advance favored by the decomposition of the ascending and descending interferograms (Figures 11 and S5). The estimated vertical motion indicates down throw reaching \(-22\) cm (Figure 11a), concentrated within the mainshock epicentral region, whereas a more complex pattern, with a maximum of \(+8\) cm, is presented by the E-W horizontal component (Figure 11b). Figure 11: Vertical (**a**) and E-W (**b**) ground displacement maps as decomposed using Sentinel-1 DInSAR LoS observations from ascending A102 and descending D036 tracks (see Table 1). Black lines correspond to fault zones (after [3]). The relocated epicenter of the 27 September 2021 mainshock is marked with a star.
## A Case Study of the Application of Proposed Support System  
### Engineering Overview  
While several stations of the Shenyang subway (line 10) are constructed using the traditional PBA method, for the construction of the Northeastern Street station (located west of the intersection  
Figure 3: Steel Tube Slab system set-up.  
Figure 2: Traditional pipe-roofing system set-up.  
of Beihai street and Northeastern Road) the traditional PBA method could not be used due to the complexity of the existing structures close to the station, and the heavy traffic load that had to be handled during the excavation. As a result, with considerations based on geographical location and geologic situation of the subway station, it was decided to use a combination of STS and PBA methods, as discussed in the previous section, for the design and the construction of the station.  
The total length and standard cross section of the subway station is 225.95 m and 24.7 m, respectively. And the buried depth of the bottom floor of the platform layer is approximate 17.5 m, with an overburden layer of about 3.4 m thick. For the construction, the station is divided into three sections: north of the station for which covered excavation is used; south of the station for which open surface method is adopted; and the middle section of the station for which a combination of STS and PBA methods is adopted due to the intensity of the surrounding structures and heavy surface traffic load. The length of the southern, middle, and the northern segments of the station are 68.65 m, 114.4 m, 42.9 m, respectively. The engineering details of the Northeastern Street station is shown in Figure 4.  
Figure 4: Layout of the Northeast Street station: (**a**) Cross section; (**b**) General plan.  
The soil stratigraphy at the station location was determined from geological and geotechnical investigations. Accordingly, the soil deposit can be divided into five layers based on the mechanical properties and formation lithology, as shown in Figure 5. And depth of the water table is from 5.2 m to 14.9 m in the construction area. In order to get the strength parameters of sandy soils, a series of super heavy Dynamic cone Penetration test (DPSH) were performed, as shown in Table 1.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel
### Physical Process
#### 2.1.2 Acoustically Induced Surface Waves  
The acoustic signal, when propagated to the water surface, induces water surface waves. This physical process can be mathematically modeled by linearizing the universal hydrodynamic equations based on the linear small amplitude wave theory. It describes the acoustic pressure-induced transverse microamplitude waves on the water surface, and the 3D mathematical model is represented by Equation (5) [27]:  
\[\delta(x,y,t)=\frac{2p_{i}}{\omega\rho v}e^{-\alpha\sqrt{x^{2}+y^{2}}}\text{ cos}\left(k\sqrt{x^{2}+y^{2}}-2\pi ft\right) \tag{5}\]  
where \(p_{i}\) is the underwater sound source pressure, \(\rho\) is the density of water, \(v\) is the speed of sound, and \(x\) and \(y\) are the coordinates on the horizontal plane, respectively.
# Time-Domain Aeroelasticity Analysis by a Tightly Coupled Fluid-Structure Interaction Methodology
## 2 Fluid Model  
### Preconditioned Governing Equations  
The unsteady preconditioned Navier-Stokes governing equations are adopted to predict the aerodynamic loads during the analysis of aeroelasticity from incompressible to compressible flows [6]:  
\[\mathbf{\Gamma}\frac{\partial\hat{\mathbf{W}}}{\partial\tau}+\frac{\partial\hat{ \mathbf{Q}}}{\partial t}+\frac{\partial\hat{\mathbf{E}}}{\partial\xi}+\frac{\partial \hat{\mathbf{F}}}{\partial\eta}+\frac{\partial\hat{\mathbf{G}}}{\partial\xi}=\frac{ \partial\hat{\mathbf{E}}_{\mathbf{v}}}{\partial\xi}+\frac{\partial\hat{\mathbf{F}}_{\mathbf{v} }}{\partial\eta}+\frac{\partial\hat{\mathbf{G}}_{\mathbf{v}}}{\partial\xi} \tag{1}\]  
where \(\hat{\mathbf{W}}\)= (\(p\ u\ v\ w\ T\))\({}^{T}\)/\(I\) is the primitive variable vector \(\hat{\mathbf{Q}}=\big{(}\ \rho\ \ \ \rho u\ \ \rho v\ \ \rho w\ \ \rho e\ \big{)}^{T}\)/\(I\) is the conservative variable vector. The preconditoner \(\mathbf{\Gamma}\) has various forms and the type proposed by [PERSON] is introduced here:  
\[\mathbf{\Gamma}=\left(\begin{array}{cccccc}\Theta&0&0&0&\rho_{T}\\ \Theta u&\rho&0&0&\rho_{T}u\\ \Theta v&0&\rho&0&\rho_{T}v\\ \Theta w&0&0&\rho&\rho_{T}w\\ \Theta H-1&\rho u&\rho v&\rho w\ \ \rho_{T}H+\rho C_{p}\end{array}\right) \tag{2}\]where \(\rho_{T}=\left.\frac{\partial q}{\partial T}\right|_{p}\), \(\Theta=\left(\frac{1}{U_{r}^{T}}-\frac{\rho_{T}}{\rho_{C}p}\right)\), and \(U_{r}\) is the reference velocity given by:  
\[U_{r}=M_{r}a \tag{3}\]  
\[M_{r}=\min(1,\max(kM_{\infty},M)) \tag{4}\]  
\(a\) is the sound speed, \(M_{\infty}\) is the free-stream Mach number, and \(k\) is a constant to avoid the singularity of eigenvector matrix near stagnation points. Let \(k\) be 0.5 here.  
The preconditioned equations may degenerate into the traditional compressible ones for the compressible flows. For low Mach number flows, the preconditioned method relieves the disparity between the convective and acoustic wave velocities, which is beneficial for the efficiency and accuracy of the solution.
## 1 Introduction
The proposed novel research effectively handles both the NP-hard (relay node optimization) as well as NP-complete (fault-tolerant reliable routing) problems, challenging optimization problems simultaneously in EH-WSN. This salient feature of the proposed novel research distinguishes it from other existing literature. 2. The first phase of the proposed novel framework (FRGNWS) is dedicated for exploring the maximum approximate number of node-disjoint routes from all SNs to BS utilizing the least number of RNs in EH-WSNs based on the hybrid adapted grey wolf sine cosine optimizer (HA-GWSCO). 3. The first phase of the proposed novel framework (FRGNWS) also provides a novel approach for discovering k-node-disjoint routes from each SN to BS with k \(\geq\) 2 in EH-WSN. 4. The second phase of the proposed novel framework (FRGNWS) is dedicated for finding the best appropriate reliable route from each SN to BS out of the available k-node-disjoint routes from each SN to BS in EH-WSN based on the hybrid adapted grey wolf whale optimizer (HA-GWWO). 5. To assess the effectiveness of the proposed novel framework in EH-WSN, we have considered five metrics, namely, the energy consumption, lifetime of the network, throughput, delay, and delivery ratio, and analyzed the results by comparing the proposed novel framework with other metaheuristic frameworks. The simulation results attest to the claim that the performance of the proposed novel framework is optimal, and this approach can be adopted for enhancing the capability towards fault tolerance and reliable green communication in next-generation WSN. The proposed novel research article is organized into six sections. The research article is introduced in Section 1; this section briefly narrates the various details, including the salient contributions, of this research article. Next, all similar works from the literature are illustrated in Section 2; this section is named Related Works. Further, the proposed novel research is well explained in Section 3; this section describes both the phases of the proposed novel framework in detail, and is named The Proposed Approach. The validation of the effectiveness of the proposed novel framework is carried out in Section 4; this section analyzes and explains the evaluated results and is named Results and Discussion. Finally, in Section 5, a summary of the proposed novel framework is provided, with future research directions, and this section is named Conclusions.
# Evaluation of Glottal Inverse Filtering Techniques on
## 4 Results
### Phonation Type  
Regarding the evaluation of the behaviour of the GIF methods according to the different phonation types, a general decrease in the RMS and NAQ error metrics can be observed in Figure 4 when the voice is less tense (e.g., whispery). On the other hand, H1H2 increases for whispery and IAIF-based methods for both sexes. For these three errors, QCP and ST-QCP achieve the best results for both the male and female subsets. It is worth mentioning that H1H2 presents a very stable response across phonation types for both sexes. A general observation in the case of the female corpus is the wider spread of the distributions for IAIF-based methods, especially for tense phonations compared to the male distributions, except for H1H2. In terms of the HRF and ST errors, the IAIF methods show larger errors for relaxed phonation types, whereas IOP-IAIF presents larger error values for tense phonation types while being the best method next to ST-QCP for whispery for male speech data. Furthermore, for male data, GFM-IAIF and ST-QCP show more stable behaviour across the phonation type range. On the other hand, for the female case, the IOP-IAIF results do not improve for lax phonations, and QCP-based approaches are clearly better than IAIF-based methods. Finally, the Wilcoxon analysis shows, as presented in Table 15, statistically significant differences compared pair-to-pair in 95.5% of the cases for the male subset and 98.5% of the cases for the female subset. The breathy phonation type has more non-significant differences, with four cases for male data and one for female data.  
Figure 4: Distributions and median of error measures of RMS, NAQ, H1H2, HRF, and ST between the estimated glottal flow and the ground-truth signals for each GIF method evaluated as boxplots with whiskers from 5% to 95%, grouped by phonation type. Columns shows results for the male and female subsets, respectively.
# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 4 Validation and Result Analysis
### The Peak Rates under Different SNRs  
The peak rate of the logical link in the virtual network layer determines the traffic upper limit of the link. Figure 4 shows the peak rate under different SNRs. We can see from Figure 4 that the higher the SNRs are, the higher the traffic upper limit is allowed by the logical link.
## 3 Stability Model Test of the Support System
### Test Equipment  
The testing apparatus consists of three main components: the model box, loading system, and monitoring system. The model box has internal dimensions of 3 m in length,\(1\) m in width, and \(1.5\) m in height. Plexiglass panels are located on both sides of the front of the model box, allowing for convenient observation of foundation deformation. The loading system includes a cylinder, loading plate, reaction frame, and air compressor, as shown in Figure 3.  
The monitoring system includes pore water pressure sensors embedded within the model foundation, displacement sensors for the load plate, miniature thin-film earth pressure gauges, and a digital image correlation (DIC) system and digital photogrammetry analysis (DPA) equipment, as shown in Figure 4.
# Nonlinear Analysis and Closed-Form Solution for Overhead Line Magnetic Energy Harvester Behavior
###### Abstract  
Recently, much attention has been given to the development of various energy harvesting technologies to power remote electronic sensors, data loggers, and communicators that can be installed on smart grid systems. Magnetic energy harvesting is, perhaps, the most straightforward way to capture a significant amount of power from a current-carrying overhead line. Since the harvester is expected to have a small size, the high currents of the distribution system easily saturate its magnetic core. As a result, the operation of the magnetic harvester is highly nonlinear and makes precise analytical modeling difficult. The operation of an overhead line magnetic energy harvester (OLMEH) generating significant DC power output into a constant voltage load was investigated in this paper. The analysis method was based on the Froelich equation to analytically model the nonlinearity of the core's BH characteristic. The main findings of this piecewise nonlinear analysis include a closed-form solution that accounts for both the core and rectifiers' nonlinearities and provides an accurate prediction of OLMEH transfer window length, output current, and harvested power. Continuous and discontinuous operational modes are identified and the mode transition boundary is obtained quantitatively. The theoretical investigation was concluded by comparison with a computer simulation and also verified by the experimental results of a laboratory prototype harvester. A good agreement was found.  
magnetic cores; saturation magnetization; magnetic flux; magnetic energy harvesting +
Footnote †: journal: Applied
## 4 Inner Loop Controller Design
### APD-SMC Law  
The control problem involves designing controllers such that each robot tracks the desired trajectory \(q_{d}(t)=\left[q_{1,d}^{T}(t),\ldots q_{n,d}^{T}(t)\right]^{T}\in\mathbb{R} ^{pn}\), which can be calculated by integrating \(\dot{q}_{d}(t)\) in Equation (2). \(\ddot{q}_{d}(t)\) is defined similarly, and they are all bounded functions. \(e=\left[e_{1}^{T},\ldots e_{n}^{T}\right]^{T}=q_{d}(t)-q(t)\in\mathbb{R}^{pn}\) and \(\dot{e}=\left[e_{1}^{T},\ldots\dot{e}_{n}^{T}\right]^{T}=\dot{q}_{d}(t)-\dot{ q}(t)\in\mathbb{R}^{pn}\) are the tracking error and tracking velocity error, respectively.  
The nominal reference states \(\dot{q}_{r}\) are defined as  
\[\dot{q}_{r}=\dot{q}_{d}+\gamma e\]  
\[\ddot{q}_{r}=\ddot{q}_{d}+\gamma\dot{e} \tag{6}\]  
where \(\dot{q}_{r}=\left[\dot{q}_{1,r}^{T},\ldots\dot{q}_{n,r}^{T}\right]^{T}\), and \(\gamma=diag(\gamma_{1},\gamma_{2}\ldots\gamma_{n})\) which is positive diagonal matrix, is defined as the sliding constant. Then, the sliding surface is  
\[s=\dot{q}_{r}-\dot{q}=\dot{q}_{d}-\dot{q}+\gamma e=\dot{e}+\gamma e \tag{7}\]where \(s=\begin{bmatrix}s_{1}^{T},\ldots s_{n}^{T}\end{bmatrix}^{T}\). Based on Property 3, the following reference torque is described as follows:  
\[\rho_{i}=M_{i}(\mathbf{q}_{i})\ddot{\mathbf{q}}_{i,r}+C_{i}(q_{i},\dot{q}_{i}) \dot{q}_{i,r}+g_{i}(q_{i})=Y_{i}(q_{i},\dot{q}_{i},\dot{q}_{i,r},\ddot{q}_{i,r}) \Theta_{i} \tag{8}\]  
For the coordinated control of multiple Euler-Lagrange systems, the APD-SMC law is proposed as  
\[\tau_{i}=\dot{\rho}_{i}+k_{pi}\dot{e}_{i}+k_{di}\dot{e}_{i}+k_{i}sign(s_{i}) \tag{9}\]  
where \(k_{pi}\) and \(k_{di}\) are the proportional and derivative gains of the PD control, respectively, and \(k_{i}\) is the gain of the robust term. All three gain matrices are selected to be positive definite. \(\dot{\rho}_{i}\) is the estimation of the reference torque \(\rho_{i}\) that will be updated using an adaptive law. Owing to the use of APD-SMC, the need for large control gains of PD-SMC to compensate for the unknown term \(\rho_{i}\) is avoided.  
\[\dot{\hat{\rho}}_{i}=\mu_{i}s_{i}\]  
\[\dot{\rho}_{i}(0)=\dot{\rho}_{i,0} \tag{10}\]  
where \(\mu_{i}\) represents the diagonal matrix adaptation rate and \(\dot{\rho}_{i,0}\) is the initial value of the estimated torque vector.  
Remark 3. It can be seen that the APD-SMC law in Equation (9) is only related to the tracking error \(e_{i}\) and tracking velocity error \(\dot{e}_{i}\). Therefore, the control law is model-free and easy to implement in practice.
## 3 Results and Discussion
### Materials Characterization
#### 3.1.2 SEM Analysis  
As Figure 2a,b shows, the surface of PDMS was extremely uniform and the folds were extremely smooth, which is the reason why the sponge material had such good mechanical properties and elasticity. Additionally, its internal porous structure increased its oil absorption performance to a certain extent.  
As Figure 2c,d shows, the morphology changed significantly with modification with MFA; the smooth surface of the PDMS became rough, which was favorable for hydrophobicity. Additionally, because of the natural porous structure of FA, the MFA@PDMS had a rough, honeycomb porous structure, which undoubtedly increased the hydrophobicity of the sponge surface and the oil absorption ability of the sponge.