esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Vertical Shear Behaviors of an Immersion Joint with Steel Shear Keys
## 4 Results and Analysis
### Vertical Shear Behaviors in Compression-Vertical Shear Test
#### 4.1.2 Shear Force-Displacement Curves  
Note that the values of the vertical shear force in Figure 10 have already excluded 70 kN. Similar to the longitudinal compression discussed in Section 4.1.1, the vertical displacement can also be calculated  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step** & **Longitudinal Compressive Load (kN)** & **Transverse Shear Load (kN)** & **Vertical Shear Load (kN)** \\ \hline
1 & 360 & 0 & 0 \\
2 & 360 & 80 & 0 \\
3 & 360 & 80 & 70 \\
4 & 360 & 80 & 120 \\
5 & 360 & 80 & 170 \\... & 360 & 80 &... \\
10 & 360 & 80 & 420 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Loading steps of the compression–bidirectional shear test.  
Figure 9: Longitudinal compression of the joint. (**a**) Data from the axial displacement (AD) gauges subjected to a compressive load of 360 kN; (**b**) compressions under incremental longitudinal compressive loads.  
by the average values of the six VD gauges. The shear force-displacement curves subjected to incremental longitudinal compressive loads are plotted in Figure 10a. In general, Figure 10a shows that the gradients of the shear force-displacement curves increase with increasing longitudinal compressive loads. Furthermore, all the shear force-displacement curves can be divided into two stages.  
Stage 1 is the non-linear development stage. This stage starts from the beginning of the test to a vertical shear force of 200 kN. In this stage, the main behavior of the joint is the compression of the rubber bearings. Due to the non-linear compressive stiffness of the rubber bearings, the shear force-displacement curves in stage 1 also exhibit a non-linear development trend, in which the displacement increases rapidly after the primary load is applied.  
Stage 2 is the quasi-linear development stage. This stage starts after the vertical shear force reaches 200 kN. In this stage, the shear resistance provided by the steel shear keys is gradually enhanced and finally occupies the main shear capacity of the joint. Due to the tight compression, the compressive stiffness of the rubber bearings gradually increases to be equal to the shear capacity of the steel shear keys. Therefore, the displacement increase with respect to the growth of the vertical shear force is slower in stage 2 than in stage 1, and the shear force-displacement curves exhibit a quasi-linear development trend.  
It can be seen that the shear force-displacement curves subjected to incremental longitudinal compressive loads exhibit the same increasing trend. However, the gradient of each curve varies from the minimum compressive load to the maximum compressive load.
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 4 Energy-Efficient and Robust Multipath Routing
### Route Maintenance  
Route failures in CRAHNs occur primarily because of two events: one is the sudden arrival of a PU into the channel being used by the SU pair, and the other is the breakage of formed routes due to node mobility. The two failures are quite different with respect to the source of failure origins.  
Figure 5: RREP packet. S: Source; D: Destination; SN: Sequence Number; HC: Hop Count; NH: Next Hop; CA: Channel Assigned; P-id: Path identifier; TO: Timeout.  
Therefore, the routing protocol must be sufficient to determine the cause and provide an appropriate solution. If the channel being used is restored by the corresponding PU, then the data transmission on the channel is subjected to halt immediately. A new channel is explored to continue the transmission of SUs as mentioned in the channel selection procedure. If the route failure occurs owing to the dynamic behavior of any mobile node, the routing path must be changed. The data routing should shift to the backup path. Failure notification of any route during the process should be sent to all participating nodes via some information flow, which can be in the form of HELLO packets.
# Phase-Coded and Noise-Based Brillouin Optical Correlation-Domain Analysis
###### Abstract  
Correlation-domain analysis has enabled distributed measurements of Brillouin gain spectra along optical fibers with high spatial resolution, up to millimeter-scale. The method relies on the joint modulation of counter-propagating Brillouin pump and signal waves so that their complex envelopes are correlated in select positions only. Brillouin optical correlation-domain analysis was first proposed nearly 20 years ago based on frequency modulation of the two waves. This paper reviews two more recent variants of the concept. In the first, the Brillouin pump and signal waves are co-modulated by high-rate binary phase sequences. The scheme eliminates restricting trade-offs between the spatial resolution and the range of unambiguous measurements, and may also suppress noise due to residual Brillouin interactions outside the correlation peak. Sensor setups based on phase coding addressed 440,000 high-resolution points and showed potential for reaching over 2 million such points. The second approach relies on the amplified spontaneous emission of optical amplifiers, rather than the modulation of an optical carrier, as the source of Brillouin pump and signal waves. Noise-based correlation-domain analysis reaches sub-millimeter spatial resolution. The application of both techniques to tapered micro-fibers and planar waveguides is addressed as well.  
optical fiber sensors; stimulated Brillouin scattering; distributed sensors; correlation-domain analysis; phase sequence coding; structural health monitoring
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Digital Surface Model Generation
#### 4.2.3 Spatial Point Intersection  
From each valid matching result (i.e., disparity) a 3D point can be calculated by spatial point intersection, finally yielding a 3D point cloud irregularly distributed in space. In the first step, for each pixel in the disparity map, the locations in the reference and search input images have to be determined. As indicated in Figure 8 the backward address of the reference image and the backward address of the search image are employed in this step. Then, the spatial point intersection can be performed via an iterative least squares adjustment. The resulting point cloud can be stored in LAS or LAZ format [119] or in a multi-band image. In this paper, the second option is used, where a three-band image of the size of the disparity map then stores East, North and height values (cf. Figure 12a and [84]).
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved Median Filtering Algorithm  
Median filtering is a classical nonlinear filtering method. It sets the gray value of a pixel point to the median gray value of all pixels in the neighborhood window centered on the point, which is very effective for eliminating salt and pepper noise. Traditional median filtering uses a fixed-size filtering window to sorts all the pixels in the window to find the median value, and it then replaces the center pixel value of the window with the median value. Even if the window center value is not noise, it is still replaced, resulting in the details and edge of image to be blurred.  
The algorithm proposed in this paper improves the traditional median filtering algorithm. All pixels of the image are accurately divided into signal points and noise points by the method of two-level detection, and only noise points are processed. The algorithm uses 3 \(\times\) 3 filtering windows. If the median value of the pixels in the window is not the noise value, the center noise is replaced by the median value; otherwise, the center noise is not processed. This is carried out repeatedly with the corresponding 3 \(\times\) 3 window in the whole image until there is no noise in the image or the noise cannot be filtered out with a 3 \(\times\) 3 window. If there are still large noise blocks in the image, the noise points in the noise blocks are replaced by the mean value of the adjacent signal points.  
The boundary pixels of the noisy image are copied and filled out for the purpose of denoising, so that the size of the noisy image is expanded from M \(*\) N to (M + 2) \(*\) (N + 2). When the denoising process is finished, the extra pixels can be deleted to obtain the correct image size.
# Experimental Assessment of RANS Models for Wind Load Estimation over Solar-Panel Arrays
###### Abstract  
This paper reports a comparison between wind-tunnel measurements and numerical simulations to assess the capabilities of Reynolds-Averaged Navier-Stokes models to estimate the wind load over solar-panel arrays. The free airstream impinging on solar-panel arrays creates a complex separated flow at large Reynolds number, which is severely challenging for the current Reynolds-Averaged Navier-Stokes models. The Reynolds-Averaged Navier-Stokes models compared in this article are \(k\)-\(\epsilon\), Shear-Stress Transport \(k\)-\(\omega\), transition and Reynolds Shear Model. Particle Image Velocimetry measurements are performed to investigate the mean flow-velocity and turbulent-kinetic-energy fields. Pressure taps are located in the surface of the solar panel model in order to obtain static pressure measurements. All the Reynolds-Averaged Navier-Stokes models predict accurate average velocity fields when compared with the experimental ones. One of the challenging factor is to predict correctly the thickness of the turbulent wake. In this aspect, Reynolds Shear provides the best results, reproducing the wake shrink observed on the 3 rd panel in the experiment. On the other hand, some other features, most notably the blockage encountered by the flow below the panels, are not correctly reproduced by any of the models. The pressure distributions over the 1 st panel obtained from the different Reynolds-Averaged Navier-Stokes models show good agreement with the pressure measurements. However, for the rest of the panels Reynolds-Averaged Navier-Stokes fidelity is severely challenged. Overall, the Reynolds Shear model provides the best pressure estimation in terms of pressure difference between the front and back sides of the panels.  
RANS; solar-panel array; computational wind engineering Article
## 2 A New Security ANM Framework  
Typically, an ANM framework in distribution networks optimises the operation of the system without considering post contingency (faults) operational states [25]. The ANM framework proposed in this paper ensures a continuity of supply in the distribution network, even after the occurrence of faults in the feeders or substation transformers (satisfying the \(N-1\) security criterion). The proposed ANM framework solves a multi-period D-SCOPF that ensures a continuity of supply by imposing operational constraints in both normal operation (pre-contingency) and abnormal (post-contingency) conditions [26]. The proposed ANM framework is to be operated by the distribution system operator (DSO). This requires a SCADA system with remote controllers. In this way, set points for control actions of the VSCs, DG curtailment, and demand shift will be provided by the centralised controller (ANM framework).  
Figure 1 shows a flowchart of the introduced ANM framework where \(\mathbf{x}\) is the vector of state variables (voltage magnitudes and angles), \(\mathbf{u}\) is the vector of conventional control variables (tap changer operation), and \(\mathbf{w}\) is the vector of the fast control variables (modulation index and phase shift of VSCs). The responses available to the DSO are divided into two time scales: short-term and long-term. Parameter \(\alpha\) in Figure 1 is used to relax the limits of the equipment during the short-term time scale immediately after a contingency (commonly called emergency ratings). Immediately after a contingency, the fast response of the VSCs will provide short-term remedial actions, followed by long-term corrective actions carried out by slower devices such as tap changers. The proposed ANM framework is divided into four main layers and will be explained in the following subsections:  
1. Load and generation forecasting;
2. Steady-state security assessment;3 Distribution security-constrained optimal power flow;
4 Day-ahead optimal scheduling.
# State of the Art of Tactile Micro Coordinate Metrology
## 5 Challenges for Further Development
### Accessibility of 3D Features  
Quite often, the geometrical features of a micro-parts are not easily accessible with a spherical probe on a straight stylus, but limited by the probe diameter for small holes, by the stylus length for high aspect features such as deep holes, or by the direction of the probe stylus. Therefore, smaller probe elements with smaller and better known form deviation, longer styli to access deep holes and smaller probing forces to allow for longer styli need to be developed. To achieve a better accessibility of normally hidden features, probes with multiple, _i.e._, star-shaped styli were built [40], their application turned out to be delicate for standard use, however. Another approach is to implement additional rotational axes on the micro CMM [41], obviously at the cost of accuracy due to uncorrected guide and positional errors of the rotation axes.  
Figure 8: **(left)** Micro ball plate used for performance verification; **(right)** Deviations measured for 25 ball center positions in four different orientations of the ball plates. The evaluation of all relative ball distances results in a 95% confidence length measurement error of \(E_{0}\) = 26 nm + 0.23 \(\times\) 10\({}^{-6}\)\(\times\)\(L\).
## 6 Discussion  
Although slight changes of SWCC parameters have minor effects on the stability of a landslide, they improve the accuracy of landslide reliability calculation. It is very necessary to conduct in-depth research on SWCC parameters. In previous studies, the main focus was on the SWCC model. We used indoor test data to select SWCC model [36]. Compared with the deterministic SWCC model, this paper took into account the uncertain factors caused by soil variability and unstable monitoring data. This paper mainly checked the parameters of SWCC based on the monitoring data of large-scale model tests.  
This paper mainly describes the impacts of monitoring data volume, prior distribution type, and different stages on posterior distribution. Figure 11 and Table 2 show the effect of data volume from 200 to 5000 on the posterior distribution of SWCC model parameters. The left figure represents the prior distribution, and it can be clearly seen that the prior distribution is the same. The figure on the right represents the posterior distribution. As the number of observation samples increases, it has little effect on the posterior distribution of SWCC parameters. It can be seen from Table 2 that the results of the posterior distribution are very stable. Therefore, it is feasible to perform Bayesian updating of parameters using monitoring data. This makes it possible to realize the online SWCC parameter monitoring and analysis system. In the research, this study selected 1000 sets of data for Bayesian updating. Figures 10 and 12 compare the effects of the prior distribution type on the results of the SWCC parameters' posterior distributions. It can be clearly seen that the coefficient of variation is smaller when the prior distribution is lognormal. Therefore, when using monitoring data to perform Bayesian updating of SWCC parameters, it is more reasonable to set the prior distribution to the lognormal distribution to obtain the posterior distribution. Figure 13 and Table 4 show the changes in the posterior distributions of SWCC parameters with different stages. In this figure, it can be seen that the mean value of parameter \(a\) in the SWCC posterior distribution increases from 1.462 to 1.480, while the mean value of parameter \(n\) fluctuates from 3.985 to 3.983 (the fluctuation range is within 0.1). Due to the short monitoring time, the change to the posterior distribution is very subtle. In the follow-up research, the monitoring time could be extended to obtain the SWCC parameter change rule. Figure 14 shows the 95% confidence interval of the posterior distribution of SWCC. The most likely SWCC falls within the 95% confidence interval of its posterior distribution.  
This paper studied the method of using landslide monitoring data to study SWCC parameters. However, due to the influences of many uncertain factors, the simulation model's prediction and the real structural response often have certain errors. Therefore, it is necessary to verify the prediction accuracy of the simulation model in future research.  
Figure 14: The 95% confidence interval of VG obtained from the Bayesian updating.
## 3 Overview of Layered Backhaul (LayBack) Network Architecture
### Layers in LayBack  
We briefly review the layers in the LayBack architecture, which is illustrated in Figure 1, focusing mainly on the context of cellular networks. The end-device layer encompasses the heterogeneous mobile wireless end devices. The radio node layer includes the LTE enBs and Wi-Fi APs. The gateway (GW) layer encompasses the network entities between the radio node layer and the backhaul (core) entities, e.g., entities of the legacy enhanced packet core. For instance, the GW layer may include the gateways of small cell deployments, or the Base Band Units (BBUs) of a cloud radio access network. Similarly, a Cable Modem Termination System (CMTS) [80; 81], which serves as a gateway for radio nodes [82], belongs to the GW layer. The SDN switching layer consists of SDN switches that flexibly interconnect the radio node layer with the backhaul (core) layer. Radio nodes operating in a non-C-RAN environment (such as macro cell enBs) process the baseband signals locally and connect directly to the backhaul (core) layer network gateways via the SDN switching layer. The backhaul (core) network layer comprises technology-specific network elements, such as the Evolved Packet Core (EPC) which supports the connectivity of LTE enBs.
Experimental results show that the compressive strength of the mortar can reach more than 85% of the control composition, while the flexural strength can reach more than 90% of the flexural strength of the control composition. Furthermore, the results show that the method proposed in the study can reduce the cost of binders [33]. In [17], the methods of laser holographic interferometry were used. Powder-activated concrete of the new generation in comparison with the materials of the old and transitional generations were considered as the studied ones. Using laser interferometry, it was found that the introduction of micro-silica, especially in combination with amorphous-active microsilica, significantly postpones the onset of microcracking in cement samples. A specimen based on a cement-sand mortar without fine fillers is characterized by a lower level of cracking, while with an increase in the load, the fracture of the specimen has a block character [17]. In work [7], the resistance of concrete mixtures, activated with alkalis, to aggressive media, which concrete may encounter during operation, was investigated. The results showed that the frost resistance and acid resistance of concrete activated with alkalis increase as the content of slag and activator increases [7]. The authors of [8] studied the possibility of using steel slag, which is a by-product of ferrous metallurgy, as a coarse aggregate in alkali-activated ash-and-slag concrete mixtures. An additional coarse aggregate, steel slag, was included in the optimized mixture by replacing natural coarse aggregates. The fatigue behavior of concrete mixes in bending was investigated [8; 25; 27; 28]. In [19], an approach to determining the parameters of the macro kinetics of concrete setting through the kinetic characteristics of hardening at different temperatures was discussed. The activating nature of concrete hydration processes has been confirmed experimentally. Furthermore, a computer simulation of the formation of concrete strength made it possible to forecast the structure and properties of concrete at the early stages of hardening [19]. The authors of [9] studied the mechanical properties, shrinkage, and heat release of concrete using fly ash with a high calcium content as a binder with two alkaline activators: sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)) and sodium hydroxide (NaOH). Compressive strength, modulus of elasticity, shrinkage, and heat release of concrete were studied depending on the ratio and concentration of alkaline activators [9; 10; 26; 29; 30]. Some of the most effective activators in terms of the degree of mechanical, electrochemical, and electromagnetic effects on the processed materials and in terms of specific energy intensity are electromagnetic devices of the vortex layer. Activation is carried out in installations where the components are processed in a working area made in the form of a pipe with a diameter of 60-150 mm with ferromagnetic particles (needles) placed in it. Under the influence of an external rotating electromagnetic field, the needles move along
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 4 Self-Drilling Anchor Grouting Slope Simulation Analysis
### Analysis of the Supporting Performance of Self-Drilling Anchor Bolts
#### 4.4.1 Analysis of the Displacement Results  
In the slope model, nine displacement monitoring points were selected along the slope surface, and two monitoring points were set near the slope toe to better analyze the displacement of the soil at the toe. The monitoring points were numbered from 1# to 9#, and the arrangement of model monitoring points is shown in Figure 25.  
Numerical simulations were performed using FLAC3D software to analyze the displacement of the slope and self-drilling anchors after reinforcement. The displacement distributions of the slope soil in both the x (horizontal) and z (vertical) directions, along with the displacement changes at each monitoring point, are shown in Figures 26 and 27, respectively.  
soil was considerable, reaching 8.28 cm before the support, with a significant difference in displacement between adjacent points. After the support, the displacement at this point reduced to 2.23 cm, and the displacement difference between adjacent points also decreased. At this point, the maximum displacement of the slope was observed near monitoring point 7, at 4.03 cm.  
Figures 26 and 27 also show that, under the support of the self-drilling anchor, the vertical displacement in the z-direction of the slope soil decreased with the elevation of the monitoring points, eventually tending to zero. Compared to the pre-support state, the change is significant. The maximum vertical displacement decreased from 8.694 cm before the support to 3.627 cm (negative direction of the z-axis). At monitoring point 1#, the vertical displacement decreased from 8.56 cm to 3.59 cm. Additionally, it can be observed that there was a sudden change in slope displacement near monitoring point 6# both before and after the support. However, the difference lies in the fact that before the support, the slope displacement gradually decreased as the location approached the slope toe, while under the self-drilling anchor support, the displacement near monitoring point 6# stabilized. This indicates that the self-drilling anchor successfully reduced the vertical displacement tendency of the slope soil, with a clear restraining effect on the soil. The self-drilling anchor support structure prevented the sliding of the slope soil, thereby effectively enhancing the stability of the slope.  
Figure 27: Displacement at monitoring points.  
Figure 26: Displacement cloud maps of the reinforced slope.