esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The main objective of this work was to offer an analytical solution for the response of an existing pile under new pile penetration. First, displacement solutions for the soil under new pile penetration are calculated by the theory of cavity expansion. Then, according to the Winkler model and the Pasternak model, the bending moment and deformation of the existing pile were calculated, respectively. Meanwhile, a 3D numerical model for the response of an adjacent pile under new pile penetration was developed. The validity and accuracy of the analytical models were examined by comparison with the proposed numerical model. Subsequently, several critical factors and pre-drilling technology were discussed for minimizing the impact of new pile penetration. The following are some of the major conclusions reached:  
1. The results of this paper's theoretical analysis and numerical simulation are basically consistent, which indicates that the accuracy of the theoretical solution of this paper is verified.
2. The form of the pile end only affects the deformation near the pile end. The influence of the form of the pile end is small and can be considered as the pile end being free when the depth of the new pile is less than the length of the existing pile. When the new pile penetration is close to or beyond the existing pile end, the form of pile end has a greater impact and needs to be considered carefully.
3. The distance of new piles from existing piles and the diameter of new piles have a greater influence on the deformation of existing piles. With an increase in pile diameter, the existing pile deformation gradually increases. As the distance between an existing pile and a new pile increases, the existing pile deformation decreases significantly.  
Conceptualization, J.C. and P.O.; methodology, J.C. and J.Z.; software, J.Z.; formal analysis, Z.Y.; investigation, J.C.; resources, J.C. and J.Z.; data curation, Z.Y.; writing--original draft preparation, J.C. and P.O.; writing--review and editing, J.Z. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant 52108328).  
Not applicable.  
The data are contained within the article.  
The authors declare no conflict of interest.  
Figure 19: Influence of the pre-drilling depths of the new pile on the bending moment of the used pile.
## 2 Materials and Methods
### Data Pre-Processing  
Pre-processing is an important task to reduce the effect of asymmetries in the spectral data, which may occur due to noise, light scattering, sample texture, and ambient temperature. Pre-processing improves the data quality to enhance the model performance. In this study, eight different types of pre-processing were applied to the spectral data: mean normalization, maximum normalization, range normalization, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky-Golay first derivatives, Savitzky-Golay second derivatives, and data smoothing. Normalization pre-processing fits the spectral data in a similar range, while SNV and MSC remove slope differences and unwanted scatter effectsSavitzky-Golay derivatives improved the spectral resolution by removing the baseline offsets and overlapping peaks. Sometimes, the noise arising from the instruments can be resolved by using a data smoothing filter [22; 23; 24; 25; 26].
###### Abstract  
The global tropospheric zenith delay grid products of VMF1 and VMF3 (Vienna mapping functions) with different resolutions are used to calculate the tropospheric zenith delay of eight IGS (International GNSS Service) stations in China, and the accuracy of the two products under different interpolation methods is analyzed. As a result, the accuracy of utilizing different interpolation methods shows no obvious differences. The interpolation accuracy of the VMF3 grid model is slightly higher than that of the VMF1, and the interpolation accuracy of tropospheric delay is related to the elevation difference of grid points. In addition, according to ERAS (the fifth generation of the Global Climate Information Analysis data set), the atmospheric stratification tropospheric delay is obtained, and a ZTD (the zenith tropospheric delay) height change grid model is constructed using the least squares exponential fitting method. The accuracy of the model is verified using the tropospheric delay product provided by the IGS. Finally, the constructed ZTD height change grid model is used as ZTD height reduction to solve the problem of large tropospheric delay errors in the VMF interpolation when the height change is large. The model accuracy of URUM station improve from 96.47 mm.to 32.97 mm (65.82%).  
troposphere delay; grid model; grid interpolation; ERA  
mapping function), NMF (Niell mapping function) and VMF (Vienna mapping functions), among others. The most commonly used tropospheric model is VMF [4; 5; 6].  
In the high-precision regional tropospheric delay modeling, finding how to use the tropospheric delay of the reference station to obtain the tropospheric delay of the interpolated station is a key problem. Conventional interpolation algorithms mainly include bilinear interpolation (BIL) and inverse distance weighted interpolation (IDW). There is no obvious difference in the outcome of these interpolation algorithms [7]. Unfortunately, the adaptability is poor in areas with large terrain undulations, and the tropospheric delay error can reach 9 cm when the height difference reaches about 1 km [8]. In [9], [PERSON] and [PERSON] et al. compared and analyzed the interpolation effects of different ZTD spatial regression models, and proposed to use the method of height difference constraint to reduce the loss of interpolation accuracy caused by a large height difference. In [10], the authors propose a removal-recovery method based on an empirical model, which can make up for the shortcomings of traditional interpolation methods to a certain extent, but when the height difference is too large, its effect is still not satisfactory.
## 4 All-Optical Switches  
Optical switches can be switched with a variety of agents such as electric fields, strain, another light beam, etc. A comprehensive study of all switching modalities will be beyond the scope of this article. Hence, we will focus only on all-optical switching [13; 14]. We will study the simple case of a non-linear mirror switch shown in Figure 5.  
Figure 4: Switching error probability as a function of current pulse width (i.e., the duration of spin–transfer–torque). The current strength was kept fixed at 10 mA.  
Figure 3: Switching error probability as a function of injected current magnitude. The energy dissipated is proportional to the square of the current. The current was kept on for the entire duration of the simulation, which was 20 ns.  
Consider a ray incident from a linear medium onto a non-linear medium at an angle \(\theta_{l}\). If the angle of incidence exceeds the critical angle for this pair of media, the ray suffers total internal reflection. Otherwise, it is refracted into the non-linear medium. The critical angle is given by the expression:  
\[\theta_{c}=\sin^{-1}\left(\frac{n^{(2)}}{n^{(1)}}\right)=\sin^{-1}\left(\frac{n _{0}^{(2)}-n_{2}^{(2)}I}{n^{(1)}}\right), \tag{11}\]  
where \(n^{(1)}\) is the refractive index of the medium of incidence and \(n^{(2)}\) is the refractive index of the (non-linear) medium of refraction \(\left(n^{(2)}<n^{(1)}\right)\). Since the latter medium is non-linear, its refractive index depends on the intensity of light incident on it, so that: \(n^{(2)}=n_{0}^{(2)}-n_{2}^{(2)}I\), where \(I\) is the light intensity (related to the energy dissipation in switching). The switch works in the following way. Initially, the ray is incident at an angle less than the critical angle \((\theta_{i}<\theta_{c})\). Then, as the intensity is increased and the critical angle decreases according to Equation (11), at some point the angle of incidence exceeds the critical angle \((\theta_{i}\geq\theta_{c})\) and the ray suffers total internal reflection.  
At any incident angle \(\theta_{i}\), the minimum intensity needed to switch is given by the relation:  
\[I_{\min}=\frac{n_{0}^{(2)}-n^{(1)}\sin\theta_{i}}{n_{2}^{(2)}} \tag{12}\]  
The probability of switching will go up rapidly with \(\theta_{i}\) exceeding \(\theta_{c}\), (i.e., with the difference \(\theta_{i}-\theta_{c}\)). Therefore, the probability will go up rapidly with increasing value of the quantity \(\underbrace{\sin^{-1}\left(\frac{n_{0}^{(2)}-n_{2}^{(2)}I_{\min}}{n^{(1)}} \right)}_{\text{fixed}}-\sin^{-1}\left(\frac{n_{0}^{(2)}-n_{2}^{(2)}I}{n^{(1) }}\right)\), which shows that the probability of successful switching increases with increasing intensity \(I\), or increasing energy dissipation. Thus, there is once again a trade-off between energy dissipation and reliability.  
Figure 5: A non-linear optical switch.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 5 Overview of WUR Architectures  
Several hardware surveys have discussed low-power receiver design and tradeoffs in extensive detail [1, 44, 45]. We provide a brief overview of common low-power receiver architectures in this section.
## 4 Analysis of Influencing Factors
### The Influence of Different Formation Conditions
#### 4.6.2 Tunnel Vertical Displacement  
Figure 21a,b show the vertical displacement curve of the outer wall of the tunnel lining and the tunnel's maximum vertical displacement upon completion of the foundation pit excavation under varying stratum conditions. The tunnel's vertical displacement distributions remain consistent across different strata. Settlement predominates on the south side of the tunnel, while uplift predominates on the north side. Settlement of the tunnel in the muddy silty clay stratum surpasses the warning value specified, whereas results for other strata remain below the warning level. According to the tunnel's maximum vertical displacement in each stratum, the influence of stratum type on the uplift value of the tunnel's north side ranks as follows: silty soil > muddy-silty clay > silty clay > silty fine sand. Similarly, the influence of stratum type on the settlement value of the tunnel's south side ranks as follows: muddy-silty clay > silty soil > silty clay > silty fine sand.
## 4 Experimental Methods  
### PA Signal Analysis  
To acquire a pair of PA signals, the object was irradiated using pulsed lasers with a time delay of \(t_{p}=1.0\) ms. The PRF and oscillation of the LS were regulated at a frequency of 5 Hz. To acquire the velocities within one oscillation period, the phase difference \(\varphi\) between the pulse and LS signal was adjusted in the range of \(0^{\circ}\) to \(360^{\circ}\).  
Figure 4a presents a typical pair of the PA signals and the corresponding PD data. The first pulse of irradiation generated the first PA signal. As the object moves during the time delay \(t_{p}\), the second pulse irradiation generated a second PA signal. The timing of each pulse irradiation denoted as \(t_{1}\) and \(t_{2}\) was determined by applying a central-difference scheme to the PD signal recorded at the measurement. The maximum value acquired from the central-difference result was considered as the timings \(t_{1}\) and \(t_{2}\), as shown in Figure 4b. Subsequently, a pair of PA signals \(p_{1}\) and \(p_{2}\) were extracted based on these timings, as depicted in Figure 4c. The time difference between \(t_{1}\) and \(t_{2}\) was calculated as \(t_{p}=t_{2}-t_{1}\) for each measurement. CC analysis was then applied to the extracted pair of PA signals \(p_{1}\) and \(p_{2}\). Figure 4d illustrates CC analysis results, where the peak of the correlation coefficient represented the horizontal position with the highest correlation between PA  
Figure 3: Experimental setup for LDS measurement. The object was set to a cantilevered arm, and the LDS was carefully submerged in water aligned to the optical axis of the pulsed lasers. The sinusoidal linear motion of the object was operated by the LS. The object displacement was measured using the LDS aligned colinear to the irradiation axis of the laser pulses.  
signal pairs. The time shift \(t_{s}\) between the PA signal pair arriving at the UST was calculated based on the correlation peak.  
First, calibration experiments were conducted under stationary conditions of the object. This was performed to investigate the limiting measurement uncertainty of the system. Then, we performed a series of PA velocity measurements of an object in sinusoidal linear motion. The phase difference of the PA measurement was adjusted every \(5^{\circ}\) to cover the whole range from \(\varphi=0^{\circ}\) to \(360^{\circ}\). In both experiments, the pre-selected time delay between pulse irradiations \(t_{p}\) was set to \(t_{p}=1.0\) ms.
## 4 Case Study
### Optimization Experiments  
Three experiments, considering three different scenarios, were put forward to demonstrate the applicability and feasibility of the proposed platform. With these experiments, the goal was to detect and measure the system's availability behavior, concerning modifications in the system configuration or technology level within specific critical equipment. Such  
Figure 9: Extract of results obtained from HP1.  
modifications were represented in the experiments by varying the frequency and timeliness of preventive interventions in critical assets and their effect on system reliability.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 3 Validation of RADIANCE Generated Results against Analytical Solutions
### Energy Balance  
Figure 14a shows a differential element that lies at the base of a box and is enclosed by all the remaining sides of the box. The box is defined as a unit cube.  
Based on the principle of energy conservation, and the corresponding summation rule for view factors, the sum of view factors from the differential surface to other surfaces should be unity [1]. This can be expressed as general relation through Equation (11) and adapted specifically to Figure 14a through Equation (12). As shown though Figure 14b the result of the view factor calculated with RADIANCE is also 1.  
\[\sum_{j}F_{d1-j}=1 \tag{11}\]  
\[F_{d1-1}+F_{d1-2}+F_{d1-3}+F_{d1-4}+F_{d1-5}+F_{d1-6}=1 \tag{12}\]  
Figure 14: View factor between a differential surface, indicated by the purple arrow in (**a**), and surfaces of a box that encloses it. The corresponding calculation is shown in (**b**).  
Figure 13: View factor between a differential planar element and a sphere as shown in (**a**) with the corresponding calculation in (**b**). The sphere considered for the calculation is comprised of 595 polygons. The sphere in (**a**) shows a reduced number of polygons to improve legibility of the figure.  
The four examples that were considered for validation in this section featured the same set of raytracing parameters for _rcontrib_. The relevance of these raytracing parameters, i.e., \"-h -w -I -ad 100,000 -lw 0.00001\" on the calculation results is discussed later in Section 5. The following section provides a high-level description of how RADIANCE can be used to calculate view factors for scenarios involving complex surfaces and obstructions.
# Ultrathin Acoustic Metasurface Holograms with Arbitrary Phase Control
###### Abstract  
Holograms show great potential in optical or acoustical waves applications due to their capability to reconstruct images. In this paper, we propose a novel scheme to realize acoustic holograms based on an ultrathin metasurface with arbitrary phase control ability. Compared with the conventional imaging method, e.g., concave mirror, which has a bulky size and limited imaging effects, the acoustic metasurface comprises a single layer of Helmholtz-like elements that can largely reduce the complexity of production. With this ultrathin reflective metasurface, acoustic holograms are constructed through a subtle structure design for single and multiple focal imaging, while the potential thermoviscous effects are minimized. We further demonstrate that the metasurface has the capability of arbitrary phase control in a certain frequency range, where the reflected phase dispersion is linear. Our proposed ultrathin metasurface holograms would be very useful in numerous applications, such as acoustic sensing, medical imaging, and so on.  
acoustic metasurface; hologram; ultrathin; arbitrary phase control Article
## 1 Introduction
The theoretical analysis and the numerical evaluations reveal that the average transmission loss values are reduced significantly under all propagation phenomena for all water depths in UWSNs when the optimal locations are computed and the communication between sensor nodes is again restablished outside the shadow zone. The average SNR values have also improved significantly and are maintained for all frequencies. The remainder of the paper is organized as follows: In Section 2, we discuss the related work. In Section 3, we analyze our system model. In Section 4, we state the location optimization problems and propose nonlinear programming (NLP) formulations. In Section 5 we present our numerical results. Finally, we draw the conclusions in Section 6.
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 7 On the Pressure Exerted by the Sludge on the Tank  
If \(p(\mathbf{x},t)\) represents the pressure exerted by the sludge on the tank, being coupled with both \(T(\mathbf{x},t)\) and \(W(\mathbf{x},t)\), its computation requires considering both thermodynamic and mechanical effects of the system. We preliminarily observe that \(p(\mathbf{x},t)\) is generally related to the thermodynamic behavior of the material and depends on the density of the slurry, \(\rho(\mathbf{x},t)\), compressibility \(K(T,W)\), as well as \(T(\mathbf{x},t)\) and \(W(\mathbf{x},t)\). Without detracting in generality, we model \(p(\mathbf{x},t)\) by an equation of state describing the behavior of the material:  
\[p(\mathbf{x},t)=p_{0}+K(T(\mathbf{x},t),W(\mathbf{x},t))[\rho(\mathbf{x},t)- \rho_{0}], \tag{62}\]  
where \(p_{0}\) is the reference pressure (referenced to \(T_{0},W_{0}\)), \(\rho_{0}\) is the density of the sludge at \(T_{0}\) and \(W_{0}\), \(K(T(\mathbf{x},t),W(\mathbf{x},t))\) is the compressibility modulus of the sludge. It is worth noting that \(\rho(\mathbf{x},t)\) varies as a function of \(T(\mathbf{x},t)\) and \(W(\mathbf{x},t)\) due to thermal expansion and change in water content, which, to a good approximation, leads to the following formulation:  
\[\rho(\mathbf{x},t)=\rho_{0}[1-\beta_{T}(T(\mathbf{x},t)-T_{0})+\beta_{W}(W( \mathbf{x},t)-W_{0})], \tag{63}\]  
where \(\beta_{T}\) is the coefficient of thermal expansion and \(\beta_{W}\) is the coefficient of change in density with respect to moisture. Similarly, for the compressibility of sludge:  
\[K(T(\mathbf{x},t),W(\mathbf{x},t))=K_{0}[1+\alpha_{T}(T(\mathbf{x},t)-T_{0})+ \alpha_{W}(W(\mathbf{x},t)-W_{0})], \tag{64}\]  
where \(K_{0}\) is the compressibility modulus at reference conditions, and \(\alpha_{T}\) and \(\alpha_{W}\) are coefficients of variation of \(K\) with respect to \(T\) and \(W\). Then, the total pressure, \(p(\mathbf{x},t)\), becomes:  
\[p(\mathbf{x},t)=p_{0}+K_{0}[1+\alpha_{T}(T(\mathbf{x},t)-T_{0})+\] \[\alpha_{W}(W(\mathbf{x},t)-W_{0})]\cdot\rho_{0}[-\beta_{T}(T( \mathbf{x},t)-T_{0})+\beta_{W}(W(\mathbf{x},t)-W_{0})]. \tag{65}\]  
Finally, by expanding and simplifying, we obtain:  
\[p(\mathbf{x},t)=p_{0}+A(T(\mathbf{x},t)-T_{0})+B(W(\mathbf{x},t) -W_{0})\] \[+C(T(\mathbf{x},t)-T_{0})(W(\mathbf{x},t)-W_{0}), \tag{66}\]  
where \(A=K_{0}\rho_{0}\alpha_{T}-K_{0}\rho_{0}\beta_{T}\), \(B=K_{0}\rho_{0}\alpha_{W}+K_{0}\rho_{0}\beta_{W}\), \(C=K_{0}\rho_{0}(\alpha_{T}\beta_{W}+\alpha_{W}\beta_{T})\). Then, once the distributions of \(T(\mathbf{x},t)\) and \(W(\mathbf{x},t)\) have been obtained, \(K(T(\mathbf{x},t,W(\mathbf{x},t))\) is computed and then \(\rho(\mathbf{x},t)\) is quantified as specified above.
# Evaluation of Structural Behavior and Fatigue Performance of a KR-Type Rail Clip
## 5 Conclusions  
In this study, the structural behavior of the KR-type rail clip was analyzed by conducting laboratory tests. The fatigue performance of the rail clip was also evaluated through FEA. The following conclusions can be drawn:  
1. During assembly testing, a high level of initial tensile stress, exceeding the yield stress, acted in some locations where a high stress concentration was expected. Such high initial stress, when combined with the stress range during train passage, indicates that the rail clip could be vulnerable to fatigue cracking.
2. The fatigue test results of the KR-type rail clips showed two different failure modes. The crack initiation points observed from the test matched well with the maximum principal stress concentration segments obtained through FEA. The comparison of natural frequencies of the clips, between the test results and those obtained through FEA, also showed a good correlation.
3. The results obtained through FEA were used to evaluate the fatigue performance of the KR-type rail clip by adopting the modified Goodman fatigue criteria. The results indicated that when the rail vertical displacement due to train operation was 2 mm or less, it was unlikely for fatigue failure to occur.
4. The evaluation of fatigue performance in this study was based on the initial stress and alternating stress acting on the rail clip. Further research, to analyze the impact of other parameters, including the residual stress and decarburized layer of the rail clip, might be required to obtain more conclusive and accurate fatigue evaluation results.  
Conceptualization--S.-H.K.; methodology--H.-B.S. and Y.-C.P.; validation--[PERSON]-B.S. and [PERSON]-[PERSON]; formal analysis--S.-H.K.; investigation--[PERSON]-H.K. and [PERSON]-[PERSON]; resources--H.-B.S.; writing (original draft preparation)--S.-H.K.; writing (review and editing)--[PERSON]-B.S., [PERSON] and [PERSON]-[PERSON]; visualization--S.-H.K. and X.-J.F.; supervision--[PERSON]-B.S.; project administration--H.-B.S.; funding acquisition--[PERSON]-B.S. and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Research Foundation of Korea (NRF). Grant funded by the Korea government (MSIT) (No. 2020R1A4A4079299).  
Not applicable.  
The photograph of fatigue failure was provided by the Hybrid Structural Testing Center at Myongji University.  
The authors declare no conflict of interest.