esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Collection and Methods  
### Morphometric Parameters  
The raster and vector data (Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data and geological maps) were collected and analyzed in order to investigate the morphometric features of the study catchments and sub-catchments (Table 1). ArcGIS 10.4 software package was run to process the different data and provide analysis, figures, and results of the study zone (Figure 4). The catchments drainage systems along the studied coastal line were generated from the (SRTM) digital elevation model data. The watershed boundaries, catchments sizes, and streams drainage networks were extracted using different algorithms in the ArcGIS spatial analysis tools (hydrology tools). The catchments were classified into 6 major catchments and 9 sub-catchments (Figure 5).  
Quantitative morphometric analyses were applied on 15 catchments and sub-catchments to investigate the geomorphological properties of the drainage networks. The applied morphometric parameters were divided into four groups of calculations:
# Deep Soft Rock Tunnel Perimeter Rock Control Technology and Research
## 5 Conclusions  
This paper takes the 2500 transport roadway of the 25 th mining area of a mine as the engineering background, analyzes the destabilization mechanism of the roadway perimeter rock, analyzes the deformation of the perimeter rock of the deep roadway, determines the physical and mechanical parameters of the perimeter rock using indoor tests, simulates them using numerical analysis software, and proposes the roadway perimeter rock support scheme. The main conclusions are as follows:  
1. The rupture and delamination of the rock layer are the main reasons for the damage to the roadway roof, and the elastic-plastic deformation of the surrounding rock has been completed shortly after digging in order to stop further deformation of the surrounding rock. This requires a suitable support timing and support program. Allowing the perimeter rock to creep within a certain range and releasing a certain amount of pressure not only results in a better bearing capacity and stronger resistance to perimeter rock deformation, but also allows the rock to better cope with the impact of mining pressure.
2. Using the rock samples taken from the site, the samples were subjected to an electron microscope scanning test and indoor triaxial test to obtain the physical and mechanical parameters of the surrounding rock of the roadway, and to establish a numerical model to calculate the maximum values of the roof subsidence and bottom bulge of the roadway. These values were found to be 66.3 mm and 120 mm, respectively, while the total amount of approximation was found to be about 180 mm, and the amount of approximation of the two gangs was found to be about 165 mm. The joint support program of an anchor rod + anchor cable + 36U steel metal support + netting + matting + backwall filling is proposed.
3. The displacement of the surrounding rock of the roadway is regularly monitored by the digital convergence meter, and the displacement of the surrounding rock of the roadway is obtained after the roadway is excavated and under the influence of mining. The maximum value of roof subsidence is 455 mm, and the maximum value of the two sides of the roadway is 303 mm. There are obvious convergence characteristics, and the support program can effectively control the deformation and damage of the surrounding rock.  
Figure 16: Deformation curves for perimeter rock monitoring.  
**Author Contributions:** G.L.: Data curation, Conceptualization, Writing--original draft. Y.Y.: Methodology, Review and editing, Supervision, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Achieving Enhanced Sound Insulation through Micromembranes-Type Acoustic Metamaterials
## 1 Introduction  
Acoustic metamaterials (AMMs), as a family of macroscopic artificial composites, typically consist of many subwavelength-scaled structures arranged in a specific pattern so that they produce an optimized combination, not available in nature, of two or more responses to acoustic excitation [1]. In recent years, many acousticians have shown great interest in studying acoustic metamaterials [1; 2; 3; 4; 5] and have uncovered many new phenomena different from those associated with traditional acoustic materials [6; 7; 8; 9; 10; 11; 12; 13].  
Acoustic metamaterials have shown attractive features in noise control engineering. In 2015, [PERSON] et al. performed experiments on the transmission loss (TL) of metamaterials arranged in a honeycomb frame [14] and showed that a significant TL could be achieved at low frequencies if the membrane was only 0.2 mm thick. We call such membrane-based metamaterials acoustic micromembranes (A\(\upmu\)Ms). Fellows further investigated [PERSON] et al.'s approach, and found a way to optimize the TL in a honeycomb AMM [15; 16]. In our recent work [17], we showed that the remarkable sound insulation at low frequencies rose from the high equivalent stiffness of the whole composite. When A\(\upmu\)Ms' thickness is doubled, it provides an additional 18 dB of TL compared to traditional materials. When the size of an A\(\upmu\)M is doubled, the TL decreases by 12 dB.  
Although A\(\upmu\)Ms have very good TL at low frequencies and are lightweight, they are inapplicable when extending to large sizes [18]. Although efforts have been taken to design large-scale metamaterials, the low-frequency TLs have not been satisfactory [19; 20; 21]. To solve this problem, in this study, we theoretically investigated an array of square A\(\upmu\)Ms mounted within a lattice frame. If the frames are made hard enough to provide an approximately rigid boundary for every A\(\upmu\)M cell, the TL of the whole assembly can be regarded as the same as that of a single cell. However, in practice, the frames are oftensomehow compliant, and it is thus difficult to demarcate rigidly each cell of the A\(\upmu\)Ms. In this work, by coupling the vibration of the frame and the A\(\upmu\)Ms within the lattice, we show how the global response of the TL of the A\(\upmu\)Ms assembly is related to the number of A\(\upmu\)M cells and to the dimensions of the lattice.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 2 Theoretical Background
### New Combined PO/MST Model  
In [7], the authors have recently introduced the new combined PO/MST model for scattering from tree canopies. This model uses the advantages of PO, namely the low computation complexity and the possibility to account for near field effects, and uses PO to obtain the coherent scattered fields. On the other hand, as the common PO approach is not accounting for any field passing through dielectric objects, this new model utilizes the complex propagation constant \(K\) obtained by MST to account for this effect. It must also be noted that another great advantage of this new model is that it provides smooth transitions between the shadowed and LoS areas behind a canopy.  
Following Equation (3), we may mathematically obtain \(\mathbf{E}_{\text{dir}}\) at \(\mathbf{r}\) without the perfectly absorbing obstacle, as follows [7]:  
\[\mathbf{E}_{\text{dir}}=\mathbf{E}_{\text{dir}}+\mathbf{E}_{\text{block}}- \mathbf{E}_{\text{block}}. \tag{9}\]  
This operation introduces a new field \(-\mathbf{E}_{\text{block}}\) which can simply be obtained as \(\mathbf{E}_{\text{block}}\) for the case of \(\mathbf{\hat{n}}\) pointing inwards to the obstacle in Figure 1. In terms of PO, this makes the surface sample a radiating aperture instead of the blocking element, and we may denote this field as \(\mathbf{E}_{\text{aper}}\)[7].  
Now we can build the basis of the new combined PO/MST model. By keeping \(\mathbf{E}_{\text{B}}\) (3) and \(\mathbf{H}_{\text{B}}\) (4) in the presence of the perfectly absorbing obstacle, only the aperture electric and magnetic fields \(\mathbf{E}_{\text{aper}}\) and \(\mathbf{H}_{\text{aper}}\) need to be modified following the complex propagation constant inside the object of interest. The total fields can be written as:  
\[\mathbf{E}_{\text{tot}}=\mathbf{E}_{\text{B}}+\mathbf{E}_{\text{aper}}=( \mathbf{E}_{\text{dir}}+\mathbf{E}_{\text{block}})+\mathbf{E}_{\text{aper}} \tag{10}\]  
\[\mathbf{H}_{\text{tot}}=\mathbf{H}_{\text{B}}+\mathbf{H}_{\text{aper}}=( \mathbf{H}_{\text{dir}}+\mathbf{H}_{\text{block}})+\mathbf{H}_{\text{aper}}. \tag{11}\]The model is now presented for a cylindrical canopy, see Figure 3. In general, there is no restriction for the canopy shape under the condition that its surface is sampled properly with samples of sides below half the wavelength.  
In Section 2.1, we have seen that PO assumes the outer surface of the object to be illuminated by the incident plane wave. Here, with an advantage, we suppose the inner surface of the canopy to be illuminated (see the green outlines in Figure 3). Under this condition, we directly apply \(K\) within the path \(s_{1}(\mathbf{r}^{\prime})\) through the canopy. After that, we account for the aperture fields using \(\mathbf{\hat{n}}=\mathbf{\hat{n}}_{\text{aper}}\) in Figure 3. The blockage fields can be obtained with \(\mathbf{\hat{n}}=\mathbf{\hat{n}}_{\text{block}}\), as shown in Figure 3. For more details see [7].
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 3 Analysis of Angle Measurement Error
### Angle Measurement Error Caused by Installation Eccentricity  
As shown in Figure 4, the grating disk works in condition when there is installation eccentricity \(e\) but no error motions of the rotating shaft. It means that the axis of rotation \(O\) and the axis average line \(O_{O}\) are at the same position.  
\(O\) represents the axis of rotation, \(O_{1}(O_{2})\) is the grating geometric center, and the trajectory of the grating geometric center is a circle. \(O\) is the center of the circle, and \(e\) is the radius of the circle. Circle \(O_{1}\) and circle \(O_{2}\) represent the grating disk at different positions separately. \(OM\) is the line between the shaft rotation center and the center of the reading head. \(OO_{1}(OO_{2})\) is the line between the shaft rotation center and the grating geometric center. \(OC_{1}(OC_{2})\) is the optical radius when the grating disk works in actual condition (optical radius is a variable in actual condition). \(Z_{O1}(Z_{O2})\) is the grating index line, \(\varphi\) is the angle between \(OO_{2}\) and \(OM\), and \(\theta\) is the angle scanned by the photoelectric sensor. After installing the grating disk on the rotating shaft, there is a fixed angle \(\theta_{0}\) between the grating index line and the eccentric direction, and the relationship between them is described as \(\varphi=\theta-\theta_{0}\).  
The circle \(O_{2}\) is set as an example to describe the model. According to the geometrical relationship, the optical radius can be expressed by Equation (1):  
\[OC=e\cos\varphi+r\cos\beta \tag{1}\]  
Figure 4: Rotary encoder works in condition with installation eccentricity.  
Figure 3: Rotary encoder works in ideal condition.  
where \(\beta=\arcsin\frac{e\sin\varphi}{r}\), so the arc length which the grating disk passes through the reading head can be expressed by Equation (2):  
\[l=\int_{0}^{\varphi}OCd\varphi=\int_{0}^{\varphi}\biggl{[}e\cos\varphi+r\cos( \arcsin\frac{e\sin\varphi}{r})\biggr{]}d\varphi, \tag{2}\]  
where \(r\) is normally larger than 20 mm, \(e\) can be controlled within 0.01 mm, \(e/r\) is no more than 0.001, so \(\arcsin\frac{e\sin\varphi}{r}\approx 0\), from Equation (2), \(l=\int_{0}^{\varphi}(e\cos\varphi+r)d\varphi\). \(\Delta l=l-r\varphi\), so the angle measurement error caused by eccentricity is calculated by Equation (3):  
\[\Delta\theta=\frac{\Delta l}{r}=\frac{l-r\varphi}{r}=\frac{\int_{0}^{\varphi}( e\cos\varphi+r)d\varphi-r\varphi}{r}=\frac{e}{r}\sin\varphi=\frac{e}{r}\sin( \theta-\theta_{0}). \tag{3}\]
## 3 Design of Controller and Nonlinear Disturbance Observer
### Design of Altitude Controller
\begin{array}{c}\alpha_{1}=-k_{11}\dot{\zeta}_{11}-k_{11}\dot{\xi}_{11}- \beta_{\xi,11}sig^{1/2}(\xi_{11})+\dot{x}_{1d}+\zeta_{12},\\ u_{1}=\frac{m}{\cos\phi\cos\theta}(-k_{21}\dot{\xi}_{21}-k_{21}\dot{\xi}_{12}+g+ \dot{\rho}_{2}+\chi_{12}-\dot{\xi}_{11}-\beta_{\xi,12}sig^{1/2}(\xi_{12})\\ -\frac{\dot{\xi}_{11}\eta_{1}}{\xi_{2}\eta_{2}}s_{2}+\zeta_{22}),\end{array}  \tag{32}\]where \(\eta_{k}=(c_{k}+4\epsilon_{k}^{2})\zeta_{k1}-2\epsilon_{k}\zeta_{k2}\), \(c_{k}>0\) and \(\epsilon_{k}>0\), \(k=1,2\), are constants, the virtual control input \(\alpha_{1}\) denotes the recursive input, and \(u_{1}\) denotes the final control input. Using (33) and (34), (29) and (31) can be expressed as follows:  
\[\dot{\zeta}_{11}=-k_{11}\mu_{1}\zeta_{11}+\mu_{1}\zeta_{12}+\mu_{1}\beta_{2}, \tag{35}\]  
\[\dot{\zeta}_{21}=-\mu_{2}k_{21}\zeta_{21}+\mu_{2}\zeta_{22}-\frac{\mu_{1}\eta_ {1}}{\eta_{2}}s_{2}-\mu_{2}\widetilde{\rho}_{2}, \tag{36}\]  
where \(\widetilde{\rho}_{2}=\rho_{2}-\hat{\rho}_{2}\) denotes the estimate error. Considering the above results, the following compact expressions can be obtained:  
\[\dot{\zeta}_{1}=-\mu_{1}\left[ \begin{array}{cc}k_{11}&-1\\ k_{12}&0\end{array} \right]\left[ \begin{array}{c}\zeta_{11}\\ \zeta_{12}\end{array} \right]+\left[ \begin{array}{c}\mu_{1}s_{2}\\ 0\end{array} \right], \tag{37}\]  
\[\dot{\zeta}_{2}=-\mu_{2}\left[ \begin{array}{cc}k_{21}&-1\\ k_{22}&0\end{array} \right]\left[ \begin{array}{c}\zeta_{21}\\ \zeta_{22}\end{array} \right]+\left[ \begin{array}{c}-\frac{\mu_{1}\eta_{1}}{ \eta_{2}}s_{2}\\ 0\end{array} \right]+\left[ \begin{array}{c}\mu_{2}\widetilde{\rho}_{2}\\ 0\end{array} \right]. \tag{38}\]  
We define the Lyapunov function candidate as follows:  
\[V_{1}=\frac{1}{2}\zeta_{1}^{T}P_{1}\zeta_{1}+\frac{1}{2}\zeta_{2}^{T}P_{2} \zeta_{2}, \tag{39}\]  
where \(P_{k}\) denotes positive definite matrices defined as follows:  
\[P_{k}=\left[ \begin{array}{cc}c_{k}+4\epsilon_{k}^{2}&-2\epsilon_{k}\\ -2\epsilon_{k}&1\end{array} \right],\;k=1,2.\]  
Differentiating (39) with respect to time and using (37) and (38) result in the following expression:  
\[ \begin{array}{c}\dot{V}_{1}=\zeta_{1}^{T}P_{1}\dot{\zeta}_{1}+\zeta_{2}^{T} P_{2}\dot{\zeta}_{2}\\ \leq-\mu_{1}\zeta_{1}^{T}Q_{1}\zeta_{1}+\mu_{1}\eta_{1}s_{2}-\mu_{2}\zeta_{2}^{ T}Q_{2}\zeta_{2}+\widetilde{\rho}_{2}\mu_{2}\eta_{2}-\mu_{1}\eta_{1}s_{2}\\ \leq-\sum\limits_{k=1}^{2}\mu_{k}\xi_{k}^{T}Q_{k}\zeta_{k}+\delta_{2},\end{array}  \tag{40}\]  
where \(\eta_{k}=(c_{k}+4\epsilon_{k}^{2})\zeta_{k1}-2\epsilon_{k}\zeta_{k2}\), \(|\widetilde{\rho}_{2}\mu_{2}\eta_{2}|\leq\delta_{2}\), and the positive definite matrix  
\[Q_{k}=\left[ \begin{array}{cc}2k_{k1}(c_{k}+4\epsilon_{k}^{2})-4k_{k1} \epsilon_{k}&(c_{k}+4\epsilon_{k}^{2})-2\epsilon_{k}k_{k1}+k_{k2}\\ c_{k}+4\epsilon_{k}^{2}-2\epsilon_{k}k_{k1}+k_{k2}&4\epsilon_{k}\end{array}  \right],k=1,2.\]
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 5 Discussion
### Future Applications of Planet Imagery
#### 5.3.2 Other Hydrological Applications
Furthermore, the near-real time availability of Planet imagery provides large-scale observations of river ice breakup as it progresses downstream through remote areas and may therefore enable prediction of ice-jam flooding before it occurs, providing warning to communities located along northern rivers. Figure 12: Time series of river ice breakup on the Yukon shown in the near infrared band of PlanetScope imagery. Panels show **(a)** 2 May, **(b)** 6 May, **(c)** 7 May, **(d)** 12 May, **(e)** 12 May (7 min later) and **(f)** 15 May.
## 3 Experiment I: Minimal SBR for Full Sentence-Level Intelligibility
### Interim Discussion  
A listening experiment was conducted to measure listeners' \(\text{SBR}_{min}\) for speech produced by four talkers in a set of background sounds. The results suggest that \(\text{SBR}_{min}\) may vary considerably from one background sound to other. The talker effect is also evident, resulting in different \(\text{SBR}_{min}\) levels even in the presence of the same background sound. All the evidence from this experiment supports the view that a constant \(\text{SBR}\) for speech-background mixing does not guarantee that target speech information can be understood over all background sounds and talkers. One possibility would be to make the \(\text{SBR}\) for the target speech very high. However, the potential problem of adopting a high \(\text{SBR}\) would be that a sound editor's intentions (e.g., creating an atmosphere for an audio scene by adding background sounds) could no longer be fulfilled.  
\(\text{SBR}_{min}\) levels measured in this study were for monaural listening, simulating intelligibility reproduced by mono audio systems or stereo systems with the target speech played diotically. For multi-channel systems in which sound objects can be perceived spatially separated, the \(\text{SBR}_{min}\) may be expected to be lower than in monaural situations because human listeners are able to use different spatial cues such as interaural level and time differences to achieve better intelligibility [5; 6; 7; 8; 28].  
The predicted \(\text{SBR}\) level which led to a BiDWGP score of 0.5 was the closest to the measured \(\text{SBR}_{min}\) in each background sound. The performance of the BiDWGP metric in predicting \(\text{SBR}_{min}\) (\(r^{2}=0.70\)) is not as good as in predicting word recognition rates (\(r^{2}>0.81\)) reported in the previous studies [9; 34; 35]. Although several types of background sounds were also used in those tests for measuring word recognition rate, speech sentences were usually uttered by a single talker. By introducing both the background and talker effects in the current experiment, the BiDWGP metric seems to lose some predictive accuracy after the function fitting for BiDWGP-\(\text{SBR}\) mapping. Nevertheless, compared to predictions by other OIMs (Table 1), BiDWGP led to the lowest RMSE and the highest \(r^{2}\). Thus, a RMSE of 2.4 dB over all tested conditions is considered acceptable as a first approximation for \(\text{SBR}\) adjustment.  
For cases in which the proposed system is only used to examine whether an intended mixing level (\(\text{SBR}\) in Figure 1) can meet the minimal intelligibility requirement, the proposed method is able to automatically fulfil this purpose. This method could also determine the optimal mixing level for a given foreground-background pair. However, the optimal \(\text{SBR}\) is not just about speech intelligibility, it should also provide a comfortable balance between the foreground and background sounds. In the next section, an experiment to determine the preferred \(\text{SBR}\) is outlined.
###### Abstract  
The soil shrink-swell phenomenon produces crack networks and slope instability. However, few studies have involved the continuous shrink-swell process of granite residual soils. The objective of the study is to explore the shrink-swell process of weathered granite soils and its effects on equally development in southern China. The bulk density, soil water content (SWC), shrink-swell ratio (SSR), clay mineral content, and mechanical composition, etc., of soil samples from five soil layers (at depths of 0.3 m, 3.0 m, 7.0 m, 12.0 m, and 16.0 m) along a profile in Yudu County was analyzed. After quantifying the soil properties at different soil depths, we analyzed these data statistically in an effort to identify strong parametric relationships. The results indicated that some properties such as bulk density and shear stress increased with soil depth, while other soil properties, such as plasticity index and liquid limit, were inversely related to depth. Soil cohesion, the angle of internal friction, and shear stress were closely related to the SWC. Every 1% decrease in the SWC resulted in a shear stress reduction of 6.62 kPa. The SSR values exhibited significant variation between the three dry-wet cycles and were closely related to the bulk density values of our kaolin and montmorillonite samples. As an environmental factor, the SWC can trigger changes in internal soil properties such as shear stress and the SSR. Using these data and observations made during our field survey, it can be proposed that continuous shrink-swell variation in deep granite-weathering crust can result in crack formation and gully erosion. It can be inferred that crack development velocity and gully retreat rate may be affected by the soil's shrink-swell process. Consequently, this information provides insight to understanding the mechanism of gully development in southern China.  
water content; clay minerals; dry-wet cycles; shrinking; swelling Article
## 2 Governing Equations
### Realizable k-Epsilon Turbulence Model  
The realizable k-\(\varepsilon\) model differs from the standard k-\(\varepsilon\) model for two reasons: the formulation of the turbulent viscosity and the transport equation of \(\varepsilon\) are modified, because it is derived from an exact equation for the vorticity fluctuations computation [31; 32]. The term \"realizable\" is because the model satisfies certain mathematical constraints on viscous stresses, in accordance with the turbulent flow physics. The transport equations for the turbulent kinetic energy (\(k\)) and its dissipation rate (\(\varepsilon\)) are:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{j}}\big{(} \rho ku_{j}\big{)}=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{ \mu_{t}}{\sigma_{k}}\bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}+G _{b}-\rho\varepsilon-Y_{m}+S_{k} \tag{5}\]  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{j}} (\rho\varepsilon u_{j})=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+ \frac{\mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{ \partial x_{j}}\Bigg{]}+\rho C_{1}S\varepsilon-\rho C_{2}\frac{\varepsilon^{ 2}}{k+\sqrt{\
u\varepsilon}}+C_{1\varepsilon}\frac{\varepsilon}{k}C_{3 \varepsilon}G_{b}+S_{\varepsilon} \tag{6}\]  
where:  
\[C_{1}=max\bigg{[}0.43,\frac{\eta}{\eta+5}\bigg{]},\;\eta=5\frac{k}{\varepsilon},\;S=\sqrt{2S_{ij}S_{ij}} \tag{7}\]  
The eddy viscosity is defined as in the other k-\(\varepsilon\) turbulent models, as:  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\varepsilon} \tag{8}\]  
However, the main difference is in the definition of \(C_{\mu}\), that is calculated with the following formulas:  
\[C_{\mu}=\frac{1}{A_{0}+A_{s}\frac{kU^{*}}{\varepsilon}} \tag{9}\]  
\[U^{*}=\sqrt{S_{ij}S_{ij}+\widehat{\Omega_{ij}}\widehat{\Omega_{ij}}} \tag{10}\]  
where \(A_{0}\) and \(A_{s}\) are constant. The other model constants are: \(C_{1\varepsilon}=1.44\), \(C_{2}=1.9\), \(\sigma_{k}=1.0\) and \(\sigma_{\varepsilon}=1.2\).