esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Theory of Numerical Simulation  
### LES Method  
The flow field of the model is numerically calculated using the LES, and the turbulent fluctuation pressure is extracted from the simulation. In the theory of the LES [34], turbulent vortices are divided into two parts: the large-scale vortex and small-scale vortex. The large-scale vortex provides the major part of the turbulent energy. However, the small-scale vortex only dissipates the turbulent energy. More specifically, a filter function is established in the method, which can filter out the small-scale vortex. The large-scale vortex is introduced into the Navier-Stokes (NS) equation and then solved. The sub-grid stress terms are added to the NS equation to show the small-scale vortex's effect on the flow field. The filtered NS equation is:  
\[\frac{\partial}{\partial t}(\rho\overline{u}_{i})+\frac{\partial}{\partial x_ {i}}(\rho\overline{u}_{i}\overline{u}_{j})=-\frac{\partial\overline{p}}{ \partial x_{i}}+\frac{\partial}{\partial x_{i}}\left(\mu\frac{\partial\overline {u}_{i}}{\partial x_{j}}\right)-\frac{\partial\tau_{ij}}{\partial x_{i}}, \tag{1}\]  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho\overline {u}_{i})=0, \tag{2}\]  
where \(\tau_{ij}=\rho\overline{u_{i}u_{j}}-\rho\overline{u_{i}}\overline{u_{j}}\) is the sub-grid stress term, and \(\tau_{ij}\) is also called the filtered stress tensor. The dynamic sub-grid model proposed by [PERSON] was added to yield Equation (1) as shown in [35], which can be suitably adapted into the local turbulent structure near the wall.  
\[\tau_{ij}-\frac{1}{3}\tau_{kk}\delta_{ij}=-2\mu_{t}\overline{S}_{ij}, \tag{3}\]  
where \(\mu_{t}\) is the coefficient of sub-grid eddy viscosity, \(\delta_{ij}\) is the sub-grid scale Reynolds stress, and \(\overline{S}_{ij}\) is the strain tensor rate, which can be written as:  
\[\mu_{t}=(C_{\mathrm{s}}\Delta)^{2}\big{|}\overline{S}\big{|} \tag{4}\]  
\[\big{|}\overline{S}\big{|}=\sqrt{2\overline{S}_{ij}\overline{S}_{ij}},~{} \overline{S}_{ij}=\frac{1}{2}\left(\frac{\partial\overline{u}_{i}}{\partial x _{j}}+\frac{\partial\overline{u}_{j}}{\partial x_{i}}\right),\Delta=\left( \Delta_{x}\Delta_{y}\Delta_{z}\right)^{1/3}, \tag{5}\]where \(\Delta\) is the scale of the filter, and \(C_{s}\Delta\) is equivalent to the mix length. The dynamic sub-grid model needs to be continuously adjusted to suit different computational processes. The convection field needs to be filtered many times, and the results are as follows:  
\[C_{s}^{2}=\frac{1}{2\Delta^{2}}\frac{\left\langle L_{ij}M_{ij}\right\rangle}{ \left\langle M_{ij}M_{ij}\right\rangle} \tag{6}\]  
\[L_{ij}=\widetilde{u_{i}}\widetilde{u_{j}}-\widetilde{u_{i}}\widetilde{u_{j}} \tag{7}\]  
\[M_{ij}=2\Delta^{2}\left(\alpha^{2}-1\right)\left|\widetilde{S}\right| \widetilde{S}_{ij}, \tag{8}\]  
where \(L_{ij}\) is the resolved stress, and \(M_{ij}\) is the tensor.
## 1 Introduction
Polarization mode dispersion and polarization dependent losses [25; 26; 27; 28; 29; 30] in the OF are well studied, analysis can be made in linear quantum [31; 32], linear classical [33; 34; 35], and nonlinear regimes [36; 37; 38]. It should be noted that quantum approach may reveal new features or even predict new quantum effects. However, the most important is its applicability in case of non-classical light (single photons, squeezed light, etc.). In this paper we use the approach that describes non-unitary dynamics of arbitrary quantum states developed in [39]. This model is applied to the case of multimode phase modulated weak coherent states in linear quantum regime; linearity requires the fact that states with low intensity are used. Lindblad master-equation is the most general type of equations that can describe temporal evolution of an arbitrary quantum system that interacts with the environment. Considered master-equation can be applied to any class of problems. The special case of Lindblad master-equation in Markov-Born approximation [40] is the quantum Liouville equation; it it suitable for further consideration in the paper description of quantum non-unitary evolution of quantum states in OF channels. Physical properties of quantum states dynamics in OF can be easily connected with their information properties by convenient representation of the Liouville equation. This paper is organized as follows. Expression of quantum bit error rate is derived in Section 2; effects of modulation indices mismatch is considered in the Section 2.1, polarization dynamics of quantum signal propagated through optical fiber is considered in Section 2.2, Section 2.3 combines results of previous ones. Numerical simulations and analysis of the dependencies are presented in Section 3. The discussion and recommendations presented in Section 4 conclude the paper.
###### Abstract  
This paper proposes a novel optical time domain reflectometry (OTDR) method based on the digital linear frequency modulation (LFM) pulse, which can achieve a tradeoff between maximum measurable distance and spatial resolution. Direct modulation and detection are adopted at the transmitting and receiving ends, respectively, which is simple in construction and does not require additional optics. The short-time fractional Fourier transform (STFrFT) is introduced for the signal processing and noise filtering. The theoretical analysis of the working principle confirmed that the spatial resolution is determined by the sweep frequency range of the digital LFM signal rather than the pulse width. The influence of the STFrFT window on the peak sidelobe ratio of the reflection peak is also studied. By combining STFrFT and sidelobe suppression, the dynamic range and spatial resolution can be appreciably enhanced simultaneously. In the demo experiments testing the proposed method on a conventional OTDR development board for comparison, a 7-dB improvement in the dynamic range and an approximately 10-times improvement in the spatial resolution are simultaneously achieved.  
fiber optic sensors; optical time domain reflectometry; Rayleigh scattering; digital chirping; short-time fractional Fourier transform +
Footnote †: journal: Applied Sciences
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 2 Data Model
\(\mathbf{B}(k_{h}r)=diag\{b_{0}(k_{h}r),b_{1}(k_{h}r),b_{1}(k_{h}r),b_{1}(k_{h}r ),\cdots,b_{N}(k_{h}r)\}\), where \(b_{n}(k_{h}r)\) is the mode strength [29] defined as \[b_{n}(k_{h}r)=\left\{ \begin{array}{cc}4\pi i^{n}j_{n}(k_{h}r)&\text{open sphere}\\ 4\pi i^{n}j_{n}(k_{h}r)-\frac{\vec{r}_{n}(k_{h}r)}{\sigma^{\prime}_{n}(k_{h}r)} \sigma_{n}(k_{h}r)&\text{rigid sphere}\end{array} \right., \tag{9}\]  
where \(j_{n}(x)\) is spherical Bessel function of the first kind and \(\sigma_{n}(x)\) is spherical Hankel function of the second kind. \({j^{\prime}}_{n}(x)\) and \({\sigma^{\prime}}_{n}(x)\) are their derivatives, respectively  
The model in (2) can be rewritten as  
\[\mathbf{x}(\tau,k_{h})=\mathbf{Y}(\Omega)\mathbf{B}(k_{h}r)\mathbf{Y}^{H}( \Phi)\mathbf{s}(\tau,k_{h})+\mathbf{v}(\tau,k_{h}). \tag{10}\]  
The model can be transformed into the spherical harmonic domain by left-multiplying \(\mathbf{Y}^{H}(\Omega)\). Due to the orthogonality principle of spherical harmonics for uniform or nearly uniform sampling, it can be known that \(\mathbf{Y}^{H}(\Omega)\mathbf{Y}(\Omega)=\mathbf{I}_{\left(N+1\right)^{2}}\)[30], where \(\mathbf{I}_{\left(N+1\right)^{2}}\) is an \(\left(N+1\right)^{2}\times\left(N+1\right)^{2}\) identity matrix. The new model in the spherical harmonic domain is  
\[\mathbf{x}_{nm}(\tau,k_{h})=\mathbf{B}(k_{h}r)\mathbf{Y}^{H}(\Phi)\mathbf{s}( \tau,k_{h})+\mathbf{v}_{nm}(\tau,k_{h}), \tag{11}\]  
where \(\mathbf{x}_{nm}(\tau,k_{h})=\mathbf{Y}^{H}(\Omega)\mathbf{x}(\tau,k_{h})\) and \(\mathbf{v}_{nm}(\tau,k_{h})=\mathbf{Y}^{H}(\Omega)\mathbf{v}(\tau,k_{h})\). Further, (11) can be left multiplied with \(\mathbf{B}^{-1}(k_{h}r)\). \[\overset{\star}{\mathbf{x}}_{nm}(\tau,k_{h})=\mathbf{Y}^{H}(\Phi)\mathbf{s}( \tau,k_{h})+\overset{\star}{\mathbf{v}}_{nm}(\tau,k_{h}), \tag{12}\]  
where \(\overset{\star}{\mathbf{x}}_{nm}(\tau,k_{h})=\mathbf{B}^{-1}(k_{h}r)\mathbf{ x}_{nm}(\tau,k_{h})\), \(\overset{\star}{\mathbf{v}}_{nm}(\tau,k_{h})=\mathbf{B}^{-1}(k_{h}r)\mathbf{ v}_{nm}(\tau,k_{h})\). In this model, the steering matrix \(\mathbf{Y}^{H}(\Phi)\) is irrelevant to the information of the microphones and mode strength.
## 3 Rock Breaking Design Program
### Hpgem  
#### 3.2.1 Schematic Design  
The HPGEM allows rock-breaking tests to be conducted directly on the tunnel face of the project tunnel. Firstly, under the HPGEM rock-breaking scheme, wedge-shaped hollowing rock-breaking testing is carried out to determine the optimal type of hollowing. After the rock-breaking test, the amount of rock breakage was measured on site, and the statistical results are summarized in Table 6. It was found that the wedge-shaped hollowing had formed a cavity at the location of the broken rock, and there was no obvious \"undercutting\" in the cavity (Figure 11). After the large-diameter hollow straight-hole hollowing test, the same cavity was formed at the rock-breaking location, and there was basically no \"undercutting\" at the bottom of the cavity. Therefore, the hollowing results are as expected, which indicates that both types of hollowing tests are successful. Although the effect of both types of hollowing can meet construction requirements, it can be seen from Table 6 that, in this hollowing test, the unit consumption of wedge-shaped hollowing was 1.25 kg/m\({}^{3}\), which is lower than that of the large-diameter hollow hole hollowing of 1.39 kg/m\({}^{3}\). Hence, wedge-shaped hollowing is superior to the large-diameter hollow hole hollowing, and wedge-shaped hollowing was adopted in the subsequent test.  
Figure 10: Simulated and fitted values of vibration velocity for different blasting conditions.  
Subsequently, different expansion tube burial depths and hole spacings were set to optimize the matching of the expansion pipe hole network parameters with the lithology of the rock mass at the tunnel face. First, three groups of high-pressure gas expansion rock-breaking tests were designed, with three top-down arranged boreholes in each group. The spacing of each group of boreholes was more than 2 m, without interfering with each other. The hole spacings of group 1, group 2 and group 3 were 1.0, 1.2 and 1.4 m, respectively. The depth of the holes was 1.5 m, and the buried depth of the expansion pipe was 1.4 m. Secondly, according to the effect of the first three groups of tests, group 4 and group 5 tests were designed, where the depths of holes in these two groups were 1.4 and 1.6 m, respectively; and the buried depths of the expansion pipe were 1.3 m and 1.5 m, respectively. The rest of the parameters are the same as those of the group 2 tests. The results of the first three groups of tests show that the optimal hole spacing for HPGEM in this tunnel is 1.2 m, and the optimal hole depth is 1.4 m. For monitoring the vibration data during the test, the monitoring program adopted the same monitoring basis and monitoring instrument. The monitoring method and monitoring point arrangement are as described in the previous section. The vibration monitoring points are mainly arranged on the ground in front of the tunnel working face. The monitoring points for rock breakage are arranged at intervals of a few meters.
###### Abstract  
The article describes a study of ship delays on the Swinoujscie-Szczecin waterway observed by the VTS operator. The research has led to an understanding of the factors that affect delays of ships calling at the ports of Szczecin and Police, as well as the possibilities of predicting and preventing these delays. This article presents the results of the study on the traffic intensity on the investigated waterway and the process of identifying the port regulation that causes the most frequent delays. Based on the obtained results from the statistical analysis and from using multiple regression, a statistical model has been developed that has the ability to estimate expected delays. Additionally, the model has been expanded to calculate financial losses resulting from delays, taking into account the daily cost of maintaining the studied ships. The study took place during ongoing project \"Modernization of the Swinoujscie-Szczecin waterway to a depth of 12.5 m\" but does not include delays resulting from this project.  
vessel traffic; simulation model; vessels' delays; regression analysis Article
# GPS Multipath Analysis Using Fresnel Zones
## 1 Motivation
However, compared to the reflecting surface, such as open water or field area, the Fresnel zones are still small, and there is no doubt that the desired multipath effects will emerge. In addition to the signal-to-noise ratio, the possibility of utilizing code-, carrier-phase or Doppler observations has been investigated in the context of GNSS-R applications such as sea surface topography, flood monitoring and tsunami detection [31; 32]. Especially in the area of airborne altimetry, the huge number of available GNSS satellites could improve the spatiotemporal resolution compared to current radar altimeter missions [33]. Nevertheless, due to the high accuracy requirements, and to efficiently utilize reflected GNSS signals, these approaches often entail the need for a special antenna setup and receiver design [34]. Furthermore, the data processing is far more complex than for SNR-based GNSS-R applications. The Fresnel zone concept is applied in the same way as for ground-based GNSS-R applications, whereby the Fresnel zone dimensions can reach up to several kilometers and thus, are substantially larger than for ground-based experiments. Contrarily, in positioning applications, multipath effects are the accuracy limiting factor and in these cases, it can be more important to avoid regions where, under a certain satellite constellation, significant multipath can be expected. Hence, it seems to be obvious that the theoretical prerequisites for multipath occurrence should be used for this purpose. In this context, we focus on two main aspects that are analyzed based on dedicated field tests:  
1. Identification of diffracted satellite signals by integrating Fresnel zones into the concept of Obstruction Adaptive Elevation Masks (OAEM). 2. Analyzing the relationship between multipath occurrence and reflector size using Fresnel zones and simulations based on theoretical prerequisites for the special case of a spatially limited horizontal reflector. The remainder of the paper is structured as follows: In Section 2, the theory on multipath and Fresnel zones is presented. Section 3 covers the investigations on diffraction identification with Fresnel zones and OAEMs, and in Section 4, the multipath analysis using Fresnel zones is described. A summary and an outlook on further investigations is given in Section 5.
# A New Method for Evaluating the Bearing Capacity of the Bridge Pile Socketed in the Soft Rock
###### Abstract
[PERSON] et al. carried out a series of field loading tests of 16 large-diameter belled concrete piles and discussed the influence of pile dimensions on vertical bearing capacity [26]. At present, the most convincing method to determine the bearing capacity of bridge pile foundation is the field static load test. However, this method has a long test cycle with a high cost, a sophisticated test process, and specific loading conditions for the test field. In addition, due to the complex geological conditions in mountainous areas, few pile foundation test results are not representative. This has become another inevitable problem of bridge pile foundations in mountainous regions. Therefore, to ensure the safety of structures and bring economic benefits, it is necessary to present a method to reduce the test cost and evaluate the bearing capacity of the pile conveniently. In this paper, a new method to evaluate the bearing capacity of the pile foundation by dynamic testing is presented. The bearing capacity of the pile foundation can be calculated theoretically only by identifying the vertical fundamental frequency of bridge piers. This method is simple, quick, and low-cost. First, based on the Rayleigh energy method and frequency synthesis method, the analytical expression of the constraint stiffness of the pier bottom is derived. Then, the bearing capacity coefficient (i.e., the bearing capacity and stiffness constraints both constitutive) with six types of influence factors are introduced and analyzed. With a convenient dynamic field test of the pier, the vertical fundamental frequency of the pier can be observed, and the constraint stiffness of the pile foundation can be identified with the frequency. Last, the vertical bearing capacity of the pile foundation is evaluated by the fitted bearing capacity coefficient and verified by the finite element method.
## 4 Discussion
### Final Equations  
In this study, the equations were obtained to find geomechanical parameters of the undrained shear strength (\(C_{u}\)), elasticity modulus (\(E\)), and density (\(\rho\)) for soft organic soils, presented in the \"El Garrochal\" sector, that are in Southern Quito. A summary of the derived expressions obtained as a function of the DMT parameters is shown in Table 8; while Table 9 is presented as a summary of equations as a function of the depth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Units** & **Description** & **Equation** \\ \hline \(E\) & MPa & Elasticity Modulus & \(E=0.231\text{e}^{0.326E_{D}}\) & \(I_{D}\leq 1\) \\ \(C_{u}\) & kPa & Undrained Shear Strength & \(C_{u}=0.262\sigma^{\prime}\text{vo}_{D}-0.221\) & \(I_{D}\leq 1\) \\ \(\rho\) & g/cm\({}^{3}\) & Density & \(\rho=0.874+0.453I_{D}\) & \(I_{D}\leq 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of equations obtained as a function of the DMT parameters.  
Figure 28: Comparison between density found by the [PERSON] nomogram, density found by the equation in the previous graph, and density found in the laboratory.  
After reviewing the joint results between the correlations derived in this work and those presented by [PERSON], it was possible to affirm that there was a difference between both. First, the equations that were obtained provided more conservative values and, considering the nature of the material analyzed, this provided confidence regarding their use in engineering projects, which will be developed in the study sector. According to [PERSON], [PERSON], and [PERSON] (2006) [38], [PERSON]'s expressions estimated these parameters very well in inorganic soils. However, organic soils and peat need study given their erratic composition.  
In addition, it is important to mention that Figures 21, 25 and 28 showed that the results obtained by the equations presented in Table 8 presented values closer to the laboratory tests. While the equations in Table 9, which were developed as a function of depth, presented specific values for each meter which did not always adapt to the real laboratory results. However, the equations based on depth present similarity in the results obtained from meter 5 to meter 10.  
Furthermore, it is important to mention that the results presented in the previously mentioned figures showed that the results provided by the Marchetti nomogram in the density estimation were higher than the real ones; therefore, this nomogram overestimated the density of the soils in the study area. Additionally, in Figure 25 we could observe that for the undrained shear strength, the results established based on the original Marchetti equations overestimated the real results in meter 2, 3 and 4, while they were slightly adjusted in the last 5 m.  
Finally, Figure 21 showed that the values provided by the original Marchetti equation for obtaining the modulus of elasticity generated considerably higher results than those obtained in the laboratory, which reaffirmed that the Marchetti equations should be modified according to the specific soils of each region.
## 1 Introduction
In this sense, the proposed method represents a statistical method that calculates the variance of the Lipschitz constant of the observed signal. The algorithm that has been developed is utilized as a filter to extract features for training neural networks, and it is currently integrated in the inferential modelling platform of the unit for Advanced Control and Simulation Solutions within ABB's industry division. Industrial applications in fault detection, in which coherent and incoherent signals are univocally visible, are shown.
# Optimal Base Station Density of Dense Network:
## 5 Numerical Results and Discussion  
The density of SmBS is a key parameter of the network densification. With the analysis, it is observed that SmBS density not only influences the link rate of the heterogeneous network but also impacts the utilization of the channel. In this section, we will first reveal the effect of SmBS density on the link rate and the channel efficiency of the small cell network via simulation. Then, the relation between SmBS density and the throughput of the SmBS will be described under different network conditions. With these results, the insights for the deployment of the heterogeneous network can be obtained.  
In the simulation, we divide the region into meshes of which the area is \(300\) m \(\times\) 300 m. We take the mesh as the unit area in the simulation. Since the area of a mesh is just the coverage of the MaBS in metropolis [32], we place one MaBS in each mesh. MaBS configurations are set based on [32]. The transmit power of MaBS is 20 W. We assume omnidirectional antennas and large-scale channel fading with path loss exponent value of 4 are invoked in both tiers of HetNets [28]. We set the bandwidth of the SmBS tier as 10 MHz and the additive noise is \(10^{-6}\) W (the power density is \(-100\) dBm/Hz). The results are obtained as follows.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 5 Security Analysis and Performance Analysis
### Storage Overhead  
In this section, our PCAC scheme is compared with the scheme proposed by [PERSON] [12] and the RAAC scheme [5], to test the storage overhead on each agency in our PCAC scheme. The comparison results are shown in Table 1. In the table, \(\mid\)*\(\mid\) is the number of elements in the group *, \(L_{p}\) is the average element length in the linear group of order \(p\). \(N_{KS}\) is the number of KSs, \(S\) is the collection of attributes, which is defined at initialization, \(S_{U}\) is the attribute set held by the user. To make the comparison fair, a little change needed to be made on the RAAC model. There exists several attribute authorities (AAs) in the model of RAAC, and we assumed that the number of AAs is the same as the number of KSs in our PCAC scheme.  
As can be seen in Table 1, the PC only stores the symmetric encrypted data, while the symmetric key is encrypted by CKA and distributed stored by KSs. Besides, the PC is only responsible for initializing all the attributes in the system and does not participate in the validation of the attribute set, so the attribute data does not need to be stored in PC. Therefore, the data complexity of our PCAC scheme is less than the RAAC. Since CKA is not completely trusted, SK needs to be assigned to different KSs for distributed storage. At the same time, the storage overhead on CKA is reduced. U and DS in the PCAC scheme only saves a pair of public key and private keys, which are assigned by the PC. This reduces the storage of the attribute set.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 2 Background
### Indoor Search and Rescue (SAR) Operations for Swarms of Drones
#### 2.1.5 Summary  
The Search and Rescue (SAR) scenario is depicted in Figure 2, where the agents maneuver a complex indoor setting to reach a target, partially guiding the search using a beacon signal. Once the location of the target has been determined, the scattered agents realign to form the shortest possible relay link back to the starting point (base station). All unused agents return to the base to prevent over-utilization of resources.