esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Network Resource Management in SDN for Cloud Services
###### Abstract  
With the recent advancements in cloud computing technology, the number of cloud-based services has been gradually increasing. Symmetrically, users are asking for quality of experience (QoE) to be maintained or improved. To do this, it has become necessary to manage network resources more efficiently inside the cloud. Many theoretical studies for improving the users' QoE have been proposed. However, there are few practical solutions due to the lack of symmetry between implementation and theoretical researches. Hence, in this study, we propose a ranking table-based network resource allocation method that dynamically allocates network resources per service flow based on flow information periodically collected from a software defined network (SDN). It dynamically identifies the size of the data transmission for each service flow on the SDN and differentially allocates network resources to each service flow based on this size. As a result, it maintains the maximum QoE for the user by increasing the network utilization. The experimental results show that the proposed method achieves 29.4% higher network efficiency than the general Open Shortest Path First (OSPF) method on average.  
network resource allocation; flow management; ranking table; SDN; cloud services +
Footnote †: journal: Computer Physics Communications
# Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation
## 2 Tensor Preliminaries
### CP Tensor Decomposition  
A tensor \(\mathcal{Y}\in\mathbb{C}^{1_{1}\times{}_{2}\times{}_{3}\cdots{}_{N}}\) is called a rank-one tensor if it can be written as the outer product of vectors  
\[\mathcal{Y}=\mathbf{y}^{(1)}\circ\mathbf{y}^{(2)}\circ\cdots\circ\mathbf{y}^{( \mathbf{N})}, \tag{1}\]  
where \(\mathbf{y}^{(n)}\in\mathbb{C}^{I_{n}\times 1}\), \(\forall n\).  
The canonical polyadic decomposition (CPD), which is also known as CANDECOMP/PARAFAC decomposition, factorizes a tensor into a sum of component rank-one tensors. The CPD of \(\mathcal{Y}\in\mathbb{C}^{I_{1}\times{}_{2}\times{}_{3}\cdots{}_{N}}\) is defined as  
\[\mathcal{Y}=\sum_{r=1}^{R}\mathbf{y}_{r}^{(1)}\circ\mathbf{y}_{r}^{(2)}\circ \cdots\circ\mathbf{y}_{r}^{(\mathbf{N})}, \tag{2}\]  
where \(\mathbf{y}_{r}^{(n)}\in\mathbb{C}^{I_{n}\times 1}\) for \(r=1,\ 2,\cdots R\). The minimum achievable value \(R\) is referred to as the rank of the tensor.
## 4 Conclusions  
Batch-type experiments were conducted to investigate the influence of living and dead roots (decayed under natural conditions for more than 5 years) of poplar (a woody plant species) on water infiltration rate and water storage capacity of soil. Results showed that living and dead roots could increase the infiltration rate in soil and improve the water storage capacity of soil. However, a larger effect of living roots on the infiltration rate and a larger effect of dead roots on the water storage capacity of soil were observed. In addition, smaller pores (30-50 \(\upmu\)m) were observed in living roots and larger pores (100-1,000 \(\upmu\)m) were observed in dead roots. Soil permeability was improved by living roots due to the dominant channel generated on the roots surface; however, water could penetrate into the dead roots, and more water was stored by the pores in the dead roots.  
Writing--original draft preparation, D.Z.; Conducting the experiment, D.Z., Y.D., and L.W.; Writing--review and editing, L.C.; Funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2016 YFC0401405), the National Natural Science Foundation of China (No. 41772245), and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 51621092).  
The English in the manuscript was edited by Springer Nature Author Services.  
The authors declare no conflict of interest.  
Figure 5: The steady-state water flux at the bottom of containers, water storage capacity of soil, and the diameter of internal pores without or with living and dead roots (soil density is 1.21 g cm\({}^{-3}\)).
# An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
## 3 Principle of Multi-Block Local Binary Pattern
### Principle of MB-LBP  
A series of improved LBP algorithms have been proposed such as the LBP uniform pattern and LBP rotation-invariant pattern. All of these LBP algorithms are always focused on a single point. However, different defects have different sizes, and the appropriate scale to describe their texture features is different (larger defects should be described by more points), so different block sizes (with different number of points) should be selected. Therefore, this paper proposed the multi-block LBP (MB-LBP) to describe features of various defects with different sizes [21].  
The specific steps of the MB-LBP are as follows:  
1. Division: The source image is divided into small blocks, and each block contains \(\text{n}\times\text{n}\) pixels (take \(2\times 2\) as an example).
2. Binarization: Calculate the mean gray value of all blocks, then compare the mean gray value of each block with the mean gray value of its neighborhood block. If the mean gray value of the neighborhood block is 50% larger than the center block, then set it as 1, otherwise set it as 0, as shown in Equation (5): \[s(g_{i},g_{c})=\left\{\begin{array}{ll}1,\frac{|g_{i}-g_{c}|}{g_{c}}&>50\% \\ 0,\frac{|g_{i}-g_{c}|}{g_{c}}&\leq 50\%\end{array}\right.\] (5) where \(g_{i}\) is the mean gray value of each neighborhood block and \(g_{c}\) is the mean gray value of the center block.  
Another improvement of the MB-LBP to LBP is that, when calculating \(s(g_{i},g_{c})\), it no longer simply compares the mean value of the neighborhood block with the center block, but compares the percentage of the difference of the mean value with the center block. Therefore, the robustness of the MB-LBP can be enhanced.  
The binarization process is shown in Figure 7.
3. Computation of MB-LBP pattern value: First, randomly choose one of the eight neighborhood blocks as a starting point. It is worth noting that the choice of starting point is random, but the choice of starting point for the entire image should be consistent, for convenience, this paper consistently chose the top left point as the starting point. Then, encode all binarization values clockwise to a binary number as calculated by Equation (6) \[\text{MB}-\text{LBP}_{p}^{R}(x_{c},y_{c})=\sum_{p=0}^{p-1}s(g_{i},g_{c})2^{p}\] (6)
4. Get MB-LBP pattern value of entire image:  
Figure 6: Complete process of the local binary pattern (LBP).  
Calculate the MB-LBP pattern value of each block traversing the entire image, from left to right, top to bottom. For the edge block, its neighborhood block is missing, and it has little effect on the texture feature of defects located inside the image, so THE MB-LBP pattern value of the edge block is ignored.  
In this way, an entire MB-LBP image can be obtained, as shown in Figure 8.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Solving Phase II (Rescue): Signal Routing and Victim Extraction
#### 3.4.3 Routing  
For a simple simulation, we can assume the walls are impenetrable to the signal exchanged between the agents, and that the agent should have a line-of-sight requirement to establish a connection to other agents. Figure 9 shows a scenario where no path is constructed after reaching the target because there is no subgraph connecting the source to the target. Figure 9 (1) shows the state where the target is found (node circled in green); however, when calculating the distances, which is the cost matrix, and finding possible connections, one can see that there is no path found between source and target nodes.  
Figure 8: Flowchart describing the working of the system in phase II. The last stopped node considers only the nodes within its communication range as its neighbors in the calculations. If there are no nodes within its communication range, the iAA simulation is restarted to move the nodes until at least one neighbor appears.  
The exploration continues using the iAA, except for the node at target, as it stops and tries to connect to nearby nodes, as one can see in Figure 9 (2). When a node is near to the stopped node, as seen in Figure 9 (3), it is able to connect to it, and it stops it. Now, the last stopped node starts looking for the nearby nodes. Then, the model continues doing the same with every node connected in the subgraph until one of the nodes is near to the source, as seen in Figure 9 (6). After reaching this state, Dijkstra re-runs, as one is sure that a path will be created after stopping nodes in the right locations.  
Figure 10 shows the situation of the agents before (left panels in both Figure 10a and Figure 10b) and after (corresponding right panels) routing has been performed. In the right panels, the agents marked in red have become part of the developed relay network. The remaining agents can be called back to the base station without affecting the performance of the relay.  
Figure 10: Reverse route set up using the modified Dijkstra approach with implementation of both (**a**) impenetrable and (**b**) penetrable walls. The path length in the case of penetrable walls is much shorter than the former. However, this is heavily dependent on the material of the walls in the search space, which shows it could have a heavy influence on the performance of our technique.  
Figure 9: A visual example showing the proposed approach: in (1) the target is located and the agent stops and starts looking for nearby agents, which (2) continue exploring. In (3) one agent is nearby, in (4) a link to that agent is created, and it stops. This process continues (5) to other agents, using the least depth as the guiding factor until (6) the process ends when the reached agent is near the source, causing the Dijkstra algorithm to terminate.
## 2 Design Parameters of Flywheel Energy Storage System
### Structural Parameters and Material Selection  
The FESS rotor can have different geometries. The most preferred of these structures is the solid cylindrical rotor. The moment of inertia of a solid cylindrical mass is half the moment of inertia of a hollow, circular rotor. However, since it can be easily balanced and the possibility of resonance, explosion and fragmentation at high speeds is low, the solid cylindrical rotor structure is generally preferred. The shape factor K is also a representation of material utilization. Figure 3 shows the \"shape factors\" of rotors with different geometries.  
In this study, a solid cylindrical rotor structure is preferred as a design, and simulations are performed according to this geometry. In order for the system to reach high speeds, attention is also paid to the material selection. The preferred materials are manufacturing steel, titanium alloy and carbon fiber materials, and their hybrid structures are also being examined.  
The material's ability to withstand the centrifugal force exerted on it determines the maximum speed of the rotor. This is the ultimate strength of the material. There is a direct relationship between the mass, centrifugal forces, and radius, as well as the speed. Material selection is a very important stage in FESS design. The ideal materials for flywheels are lightweight and durable. This implies that the density of the material must be lower. For higher speeds, the material must also have high tensile strength. These characteristics are common with composite materials, unlike metals with higher weight, but the cost of metal is lower than that of composites. Composite materials are lightweight yet possess high strength values. These materials exhibit a high specific strength. Rotor speed and the desired energy value to be stored are directly related to the rotor material. The basic selection criteria to be considered here are the application in which the system will be used, the amount of energy to be stored and the total system cost. Within the scope of this study, two types of multi-layered hybrid rotor structures are considered (mild steel, carbon fiber and titanium alloy, carbon fiber) with three different basic materials (mild steel, titanium alloy, carbon fiber).
## 4 Analysis
### Analysis of Forces Induced by Flow around the Vertical Plane  
The acting forces are the lateral force (\(F_{y}\)), rolling moment (\(M_{x}\)), and yawing moment (\(M_{z}\)), as the vertical flow velocity (\(w\)) varies from 0 to 1.6 m/s. Figure 18a-c present the force results in the presence of various vertical flow velocities, while Figure 18d-f illustrate the values of \(\tau\), \(\chi\), and \(\sigma\) at different velocities.  
Figure 18: Forces induced by the flow around a vertical plane at various flow velocities. (**a**) \(F_{z}\) and amplitude of \(F_{y}\), (**b**) amplitude of \(M_{x}\), (**c**) \(M_{y}\) and amplitude of \(M_{z}\), (**d**) the values of \(\tau\), (**e**) the values \(\chi\), (**f**) the values of \(\sigma\).  
From Figure 18a,d, it can be seen that, as the value of \(w\) increases, the average value of \(F_{z}\) and the value of \(F_{y}\) gradually increase, while the value of \(\tau\) continuously reaches about 20%, significantly exceeding 10%. This fact indicates that the lateral force (\(F_{y}\)) exhibits considerable amplitude in motion and should not be neglected.  
From Figure 18b,e, it can be seen that the value of \(\chi\) exceeds 2 as \(w\) exceeds 0.6 m/s. As \(w\) increases, the value of \(\chi\) also increases gradually. This issue demonstrates that the rolling moment (\(M_{x}\)) should not be neglected when \(w\) exceeds 0.6 m/s.  
Figure 18c,f show that, as the value of \(w\) increases, the average value of \(M_{y}\) and the value of \(M_{z}\) increase gradually. In addition, when combined with the values of the rolling moment (\(M_{x}\)) in Figure 18b, it can be seen that the amplitude trend of \(M_{z}\) matches that of \(M_{x}\). By observing the plotted results of \(\sigma\), it can be concluded that the minimum and maximum values of this factor are obtained as 71.77% and 161.97%, respectively. This reveals that, with the gradual increase in the speed \(w\), the amplitude of the yawing moment (\(M_{z}\)) will eventually exceed the average value of the pitching moment (\(M_{y}\)). Therefore, the yawing moment (\(M_{z}\)) cannot be neglected. It is also worth mentioning that \(M_{y}\) possesses a negative value and, in the figure, only numerical values are compared for convenience without considering the direction.  
From the analysis, it can be concluded that, although the submarines are symmetrical about the vertical axis, the generation and detachment of vortices possess a special effect on the lateral force (\(F_{y}\)) and the yawing moment (\(M_{z}\)), which is applied to the horizontal plane of the submarine. In addition, when the vertical velocity (\(w\)) exceeds 0.6 m/s, it also exhibits a noticeable influence on the rolling moment \(M_{x}\), and these effects cannot be ignored at all. In addition, it can be inferred that the trend of the \(M_{z}\) amplitude during the vertical motion of the submarine is closely related to the \(M_{x}\).
# Investigation on Unsteady Flow Characteristics of a SCO\({}_{2}\) Centrifugal Compressor
## 4 Results and Discussion
### Power and Efficiency of the Designed Turbine  
The process of analyzing the power and efficiency of the designed centrifugal compressor from the numerical simulation result is able to estimate whether the SCO\({}_{2}\) centrifugal compressor satisfies the design requirement. In this section, we compare the torque of steady result and unsteady time-averaged result. Meanwhile, the input power, pressure ratio, and isentropic efficiency are calculated and compared with the design condition.  
The comparison of the single blade torque is performed in Figure 14.  
The torque of a single blade in unsteady simulation has a periodic variation each time that the impeller rotates past a diffuser passage. The time-averaged unsteady result is 212.92 N\(\cdot\)m, which is slightly lower than the steady result 216.52 N\(\cdot\)m. Since the unsteady computation can more accurately simulate the aerodynamic performance, the value 212.92 N\(\cdot\)m is more reliable.  
The pressure ratio, input power, and isentropic efficiency are significant parameters when measuring the performance of a designed centrifugal compressor. Isentropic efficiency is defined as follows:  
\[\eta_{\text{is}}=\frac{h_{2s}-h_{1}}{h_{2}-h_{1}} \tag{15}\]  
where \(h\) represents enthalpy, and the subscript \(i\) stands for isentropic parameter. The subscript 1 and 2 respectively represent the inlet and outlet of compressor.  
Figure 14: Torque of a single blade in the SCO\({}_{2}\) compressor.  
In Table 5, we calculate the input power based on the value of torque mentioned above. The pressure ratio, input power and isentropic efficiency of design condition, steady CFD result and unsteady CFD result are compared.  
It can be concluded from Table 5 that the input power meets the design requirement. Nevertheless, the isentropic efficiency resulted from the CFD process is inferior to that of the design. The reason lies in the inaccurate computation of flow loss in the 1D viscous design. The profiles of the diffuser and the impeller have been ignored while the loss coefficient of leakage and impeller friction is mainly from empirical values. The 3D numerical investigation can more precisely simulate the flow process in this SCO\({}_{2}\) centrifugal compressor, especially in an unsteady simulation which has taken the influence of rotation into account.
# Development of Passive Fire Protection Mortars
## 1 Introduction
Lightweight expanded perlite has low thermal conductivity (0.04-0.06 W/mK), relatively high melting point (1260-1343 \({}^{\circ}\)C) and low density (loose: 50-400 kg/m\({}^{3}\)) [31]. These characteristics make perlite a material with excellent insulating properties. Vermiculite consists of a micalike mineral containing a shiny flake (the phyllosilicate group). It is produced at ambient conditions from the weathering/hydrothermal alteration of phlogoptic or biotite [30]. Similar to perlite, when vermiculite particles are subjected to high temperatures (from 650 to 950 \({}^{\circ}\)C), they expand, presenting a density of 80 to 120 kg/m\({}^{3}\), a melting point between 1240 to 1430 \({}^{\circ}\)C and a thermal conductivity between 0.04 to 0.12 W/mK [32]. Moreover, due to their highly porous structure, these materials absorb moisture in varying degrees (depending on their type), which when combined with the low thermal conductivity, extends their durability during the fire. Perlite may be a better supplement than vermiculite, not only due to its thermal conductivity but also to the high retraction effect of vermiculite [33]. As they are materials with good thermal properties, they can be used in the development of plaster or cement mortars as a passive fire protection solution in steel structures. In addition to cement, plaster can also be used as a binder and combined with perlite and/or vermiculite to develop mortars for passive fire protection. Compared to cement, gypsum is much cheaper, easier to produce, and provides a more effective thermal barrier because it has a lower thermal conductivity than cement. It also contributes to the energy loss from fire due to its endothermic dehydration process [34; 35]. Studies have shown that perlite-Portland cement and perlite-gypsum coatings are the most effective plasters as fire barriers and in retarding the conduction of high temperatures across their thickness among different kinds of coatings, such as traditional-cement plaster, vermiculite cement/gypsum-based mortar, intumescent coating, calcium silicate board, and LECA-cement plaster [36; 37; 38]. Therefore, this paper presents the development of different gypsum or cement-based mortars as passive fire protection materials, the evaluation of the influence of aggregate size, and the addition of silica micro- and nanoparticles on their thermal performance and assessment of fire resistance to the protected short steel columns.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 3 Localization Analysis of Double Arrays  
### Localization Principle  
Although the single array has poor range-detection ability, its good directional ability ensures that the direction of the sound source is accurately determined. In order to improve the range-detection ability, a second array was added to the setup by means of intersecting the azimuth lines.  
The array in Figure 1 remained positioned as shown in the figure and is referred to as Array 1. The second array, with identical characteristics, was added to the X-Y plane as Array 2. The centre of Array 2 is located at \(O_{1}\) (\(L\), 0). The angle between the line \(OT\) (sound source \(T\) to origin \(O\)) and the axis-X is referred to as \(\varphi_{1}\), while the angle between \(O_{1}T\) and X is \(\varphi_{2}\). The time delay when the sound signal reaches the sensors in Array 2 is \(\tau_{1i}{}^{\prime}\). The range differences are \(d_{1i}{}^{\prime}\) (\(i=2,3,4,5\)), so \(d_{1i}{}^{\prime}=c\times\tau_{1i}{}^{\prime}\). The geometry of the double-array setup is shown in Figure 4.  
Figure 4: Model of double array.  
Figure 3: Distributions of the range error of \(R\) and \(\sigma_{\tau}\).  
Since the structure of both arrays is the same, the form of the azimuth formula is the same and the relevant expressions follow from Equation (3) as:  
\[\left\{\begin{array}{l}tan\varphi_{1}\approx\frac{(\tau_{15}-\tau_{13})}{(\tau_ {14}-\tau_{12})}\\ tan\varphi_{2}\approx\frac{(\tau_{15}t^{\prime}-\tau_{13}t^{\prime})}{(\tau_{14 }t^{\prime}-\tau_{12}t^{\prime})}\end{array}\right. \tag{21}\]  
From the geometric relationship,  
\[\left\{\begin{array}{l}k_{1}=\frac{y}{x}=tan\varphi_{1}\\ k_{2}=\frac{y}{x-L}=tan\varphi_{2}\end{array}\right. \tag{22}\]  
The simplification of Equation (22) is:  
\[\left\{\begin{array}{l}x=\frac{Lk_{2}}{k_{2}-k_{1}}\\ y=\frac{Lk_{1}k_{2}}{k_{2}-k_{1}}\end{array}\right. \tag{23}\]  
\[\left\{\begin{array}{l}\varphi=\arctan(k_{1})\\ R=\frac{Lk_{2}\sqrt{1+k^{2}_{1}}}{k_{2}-k_{1}}\end{array}\right. \tag{24}\]  
Equations (23) and (24) represent two alternative expressions for the localization of sound source. In these two expressions, the variables are array distance \(L\) and slopes \(k_{1}\) and \(k_{2}\). The slopes can be inferred from the time delay at each one of the two sensors by means of Equation (21). In the experiments, localization was obtained from time delay \(\tau_{1i^{\prime}}\) and array distance \(L\).