esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Model
### Friction Power Loss Analysis Model
#### 2.3.3 Oil Stirring Friction Power Loss  
The Rumbarger empirical formula is adopted to calculate the oil stirring friction power loss caused by the effective surface area A of the roller, cage, and inner ring raceway of the roller bearing, respectively, as follows [7]:  
\[\mathrm{H_{d}}=\xi\mathrm{f_{v}}\mathrm{A}\omega^{3}\mathrm{r^{3}}/8 \tag{14}\]  
\[\mathrm{f_{v}}=\left\{\begin{array}{ll}16/\mathrm{R_{e}}&\mathrm{Re}<2500, \mathrm{T_{a}}<41\\ 3(\mathrm{R_{e}}/2500)^{0.856}(16/\mathrm{R_{e}})&\mathrm{Re}\leq 2500\\ 1.3(\mathrm{T_{a}}/41)^{0.539}(16/\mathrm{R_{e}})&\mathrm{T_{a}}\geq 41\\ \end{array}\right. \tag{15}\]  
\[\mathrm{R_{e}}=\omega\mathrm{rC}/\mathrm{v} \tag{16}\]  
where \(\xi\) is the density of the air oil mixture. \(\mathrm{f_{v}}\) is the viscous friction coefficient. \(\mathrm{r}\) is the reference radius. \(\mathrm{R_{e}}\) is the Reynolds number. \(\mathrm{T_{a}}\) is the Taylor number.  
(1) By introducing the values of the cage A and \(\omega_{\mathrm{c}}\) into Equations (14), (15), and (16), the friction power loss of oil stirring for the cage \(\mathrm{H_{cdrag}}\) can be calculated as:  
\[\mathrm{H_{cdrag}}=\pi\xi\mathrm{f_{vc}}(\omega_{\mathrm{cm}}-\omega_{\mathrm{ cm}}\times\mathrm{s_{f}})^{3}\mathrm{I_{CL}D_{CL}}^{4}/40 \tag{17}\]  
(2) By introducing the values of the inner ring raceway A into Equations (14), (15), and (16), the friction power loss of oil stirring for the inner ring \(\mathrm{H_{idrag}}\) can be given as:  
\[\mathrm{H_{idrag}}=\pi[\mathrm{Z}-(2\mathrm{N}+1)]\xi\mathrm{f_{vi}}\omega_{ \mathrm{i}}^{3}\mathrm{D_{i}}^{4}\mathrm{I_{i}}/192\mathrm{Z} \tag{18}\]  
(3) The friction power loss of oil stirring for the roller \(\mathrm{H_{rdrag}}\) can be given as:  
\[\mathrm{H_{rdrag}}=\mathrm{D_{m}}(\omega_{\mathrm{cm}}-\omega_{\mathrm{cm}} \times\mathrm{s_{f}})\mathrm{F_{v}}\mathrm{Z}/2 \tag{19}\]  
\[\mathrm{F_{v}}=707.54\mathrm{c_{v}}\xi\mathrm{I_{e}}\mathrm{D_{r}}[\mathrm{D_ {m}}(\omega_{\mathrm{cm}}-\omega_{\mathrm{cm}}\times\mathrm{s_{f}})]^{1.95}/16g \tag{20}\]  
In Equation (20), g is acceleration due to gravity, \(\mathrm{C_{v}}\) is the drag coefficient, and \(\mathrm{\eta}\) is the kinematic viscosity.  
The formula for calculating \(\xi\) is as follows [21]:  
\[\xi=57.8\times 10^{5}\frac{\mathrm{w}^{0.37}}{\mathrm{nD_{m}^{1.7}}} \tag{21}\]  
where \(\mathrm{D_{CL}}\) and \(\mathrm{I_{CL}}\) are the cage land diameter and width, respectively. \(\mathrm{D_{i}}\) and \(\mathrm{I_{i}}\) are the bore diameter and width of the bearing inner ring, respectively.
# Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study
## 4 SDN-Based Cloud Computing Resource Management in Energy Efficiency
Additionally, the local power controller is responsible on imposing server power threshold, both modules are server resides agents. Evaluation is conducted in testbed. The results show that a power assignment applied properly in such a way that it prevents UPSs overloading. As well, a three-phase framework is proposed in order to manage virtual resources. Profiling and profile updating, which aim to process data and obtain information from and about the tasks, VM and PM [38]. Task classification to three classes that are long, normal and tiny. Then, the application assignment of these classified tasks to appropriate VMs is conducted. Finally, successive virtual machine placement is done by utilizing the pm that are most energy efficient. Three different scales that are large, medium and small datacenter are evaluated with the proposed work based on number of VM s and PM quantity gaps. As well a power saving is measured which result in increased by at most 12% compared to baseline. Moreover, the VM placement algorithm based on genetic algorithm was developed in [39]. The controller in the proposed system takes into consideration cost and computing requirements in order to determine the suitable VM selection type, which is performed through selection manager module. Additionally, the controller will determine the suitable placement via host selection manager module considering the objective of reducing cost and power consumption. However, in order to achieve that, an adaptive genetic algorithm is proposed which perform VM consolidation in lowest number of servers as possible. The proposed work is evaluated in CloudSimSDN with synthetic and real workloads. The power consumption of network devices, power consumption of servers, average response time of both workloads, traffic load, and hourly cost are the evaluation metrics. Results show that proposed algorithm outperforms in reducing power consumption, response time and hourly cost. Table 3 summarizes above-mentioned power optimization contributions in terms of major feature of algorithms, main tasks, the environments where the algorithm targets, which evaluation tool is used for testing and the dataset, evaluated metrics and, finally, the major contribution of the algorithm based on its published results. Moreover, Table 4 shows measured metrics with associated algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref. ** & **Major Feature** & **Main Tasks** & **Envi. ** & **Dataset** & **Evaluation Tool** & **Measured Metrics** & **Contribution** \\ \hline \multirow{6}{*}{[21]} & & Overbooking controller: VM & & & & & & & \\  & placement \& flow consolidation & & & & & & \\  & plasma overbooking ratio and & & & & & & &
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 2 Materials and Methods
### Calibration Procedure  
The procedure of calibration for the elliptical paraboloid array is shown in Figure 7.  
Step one: The displacement coefficient \(c_{1}\) is calibrated. The displacement coefficient \(c_{1}\) refers to the actual displacement (\(\upmu\)m) change corresponding to a change of a 1 \(\upmu\)m length of the spot on the CCD camera. The calibration results for the displacement coefficient are shown in Figure 8. We verified that the displacement coefficient is about 3.5045 from the linear fitting equation, with a correlation coefficient of 0.9999.  
Figure 6: The pitch between two elliptical paraboloids.  
Figure 7: Calibration procedure.  
Step two: The angle coefficient \(c_{2}\) is calibrated. The angle coefficient \(c_{2}\) is described as the angular (arcsec) change associated with the 1 \(\upmu\)m spot position on the CCD camera. The experimental results for angle coefficient calibration are shown in Figure 9. Figure 9 indicates that the result of the angle coefficient is approximately 4.975 from the linear fitting equation, with a correlation coefficient of 0.9999.  
Step three: Calculate the relative angles between elliptical paraboloids, including the pitch angle and yaw angle, using the optical slope sensor.  
Step four: Consider the geometric relationship between the vertices and measuring points of the elliptical paraboloids, which is the basic principle of the whole calibration method.  
Step five: Conduct calibration experiments. The quill of CMM moves 50 mm each time to reach the position of the next elliptical paraboloid; meanwhile, the light spot central position is recorded.  
Step six: Analyze and compare calibration results. The calibration results require analysis and comparison to prove their correctness, especially in practical applications.  
Figure 8: Calibration results in the X direction.  
Figure 9: Calibration results in the Y direction.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 3 Routing Implementation
### Encoding and Decoding Implementation
More specifically, we first find the minimum packet length \(mi\) of \(b_{j}[x]\), which can be encoded in the input queue. Then, we select \(mi\) packets from each \(b_{j}[x]\) and put these packets in \(d_{j}[x]\). The node encodes \(d_{j}[x]\) and generates \(d_{m}[x]\) (i.e., \(d_{m}[x]=\oplus d_{j}[x]\quad(1\leq j\leq t)\)). To this end, push \(d_{m}[x]\) and \(\sum_{j=1}^{t}a_{j}[x]\) into \(U_{m}[x]\) according to Equation (9). ```
1:\(b_{j}[x]\) code_o, code_f
2:\(U_{m}[x]\)
3:while code_o \(\
eq\) T do
4:\(U_{m}[x]=\sum_{j=1}^{t}b_{j}[x]\)
5:endwhile
6:while code_o \(=\) T do
7:if flow_n \(<\) code_f then
8:\(U_{m}[x]=0\)
9:Return Expire_time \(=\) 2 timeslots
10:endif
11:if flow_n \(=\) code_f then
12: Statistic \(mi\)= min \(b_{j}[x]\)
13:for\(n=1,n<mi,n++\)do
14: Select \(mi\) packet from each \(b_{j}[x]\), and put these packets in \(d_{j}[x]\)
15:\(d_{m}[x]=\oplus d_{j}[x]\)
16: Push \(d_{m}[x]\) into \(U_{m}[x]\)
17: Push \(\sum_{j=1}^{t}a_{j}[x]\) into \(U_{m}[x]\)
18:endfor
19:endif
20:endwhile
21:return\(U_{m}[x]\)
```  
**Algorithm 1** Encoding algorithm in each timeslot. The decoding implementation is simple and the reverse process of encoding. The node uses the encoded and original packets from the buffer pool for proper decoding. Each packet has its own packet ID, which is stored in a hash table. The table is collected every few seconds. When a node wants to recover an encoded packet consisting of \(j\) original packets, the node goes through the IDs of the \(j\) original packets one by one and retrieves the corresponding packet from its packet pool if possible. To this end, the encoded packet XORs the \(j-1\) original packets, which can recover an original packet. Notice that the lost encoded packet and original packets should be retransmitted to help decode. Moreover, each packet in the buffer pool is assigned with a long enough packet timeout.
## 4 Basic Correlations for Reference Conditions
### Equivalent Values of Incidence and Deviation Angles  
The variables of \(i_{\text{eq}}\) and \(\delta_{\text{eq}}\) herein can be gained by a typical correlation methodology developed by [PERSON] [7] for conventional blades, which has been extensively employed by [PERSON] et al. [9], [PERSON] [18], and [PERSON] et al. [19]. The method can be expressed implicitly by Equations (10) and (11):  
\[i_{\text{eq}}=f_{\text{ref}}\big{(}K_{\text{imod}}\varphi,\kappa_{1}+i_{\text{ eq}},\varphi_{\text{eq}},t_{\text{max}}/C\big{)} \tag{10}\]  
\[\delta_{\text{eq}}=g_{\text{ref}}\big{(}K_{\text{$\delta$mod}}\varphi,\kappa_{ 1},\varphi_{\text{eq}},t_{\text{max}}/C\big{)}, \tag{11}\]  
where extra correction functions, namely \(\text{K}_{\text{$i$ mod}}\) and \(\text{K}_{\text{$\delta$ mod}}\), are added to the correlations to adapt to the NACA 65 profile with a circular-arc camber line. In addition, the orientation parameter is changed to blade inlet metal angles from (the original) inflow angles.  
The correction functions \(\text{K}_{\text{$i$ mod}}\) and \(\text{K}_{\text{$\delta$ mod}}\) have the same format with three empirical constant coefficients, as expressed in Equations (12) and (13), which mainly consider the effects of solidity on the basic correlations. The constant coefficients are tabulated in Table 5.  
\[\text{K}_{\text{imod}}=\left(\mu_{1}\exp(\sigma/\mu_{2})+\mu_{3}\right)_{i} \tag{12}\]  
\[\text{K}_{\text{$\delta$mod}}=\left(\mu_{1}\exp(\sigma/\mu_{2})+\mu_{3}\right) _{\delta}. \tag{13}\]  
To validate the conventional blade correlations, Figure 9 gives the comparisons of reference minimum-loss incidence and deviation angles with CFD database. It can be seen that the correlation curves are in excellent agreement with the symbols, which allows for accurate prediction of \(i_{\text{eq}}\) and \(\delta_{\text{eq}}\) for the basic correlations.
# Weighting of Multi-GNSS Observations in Real-Time Precise Point Positioning
## 2 Data and Methods
### Functional Model  
GNSS data processing is done using the in-house developed GNSS-WARP software [28], in the simulated real-time mode, in order to reproduce the same conditions for each weighting scheme. We define the combined GPS + GLONASS + Galileo + BeiDou observation model as:  
\[P_{IF}^{s}+c\delta t^{s}-\rho_{r}^{s}-m_{h}^{s}\cdot Z_{h}-\Delta_{P}^{s}=\frac {X_{r}-X^{s}}{\rho_{r}^{s}}\delta X_{r}+\frac{Y_{r}-Y^{s}}{\rho_{r}^{s}}\delta Y _{r}+\frac{Z_{r}-Z^{s}}{\rho_{r}^{s}}\delta Z_{r}+c\delta t_{r,S}+m_{w}^{s}\cdot Z _{w} \tag{1}\]  
\[L_{IF}^{s}+c\delta t^{s}-\rho_{r}^{s}-m_{h}^{s}\cdot Z_{h}-\Delta_{L}^{s}= \frac{X_{r}-X^{s}}{\rho_{r}^{s}}\delta X_{r}+\frac{Y_{r}-Y^{s}}{\rho_{r}^{s}} \delta Y_{r}+\frac{Z_{r}-Z^{s}}{\rho_{r}^{s}}\delta Z_{r}+c\delta t_{r,S}+ \lambda_{IF}^{s}\cdot B_{IF}^{s}+m_{w}^{s}\cdot Z_{w} \tag{2}\]  
where: \(S\) denotes the GNSS system and s denotes the satellite number; \(P_{IF}^{s}\) and \(L_{IF}^{s}\) are iso-free combinations of pseudorange and carrier-phase measurements, respectively; \(c\) is the speed of the light; \(\delta t^{s}\) is satellite clock correction; \(\rho_{r}^{s}\) is the geometric distance between receiver and satellite; \(m_{h}^{s}\) and \(m_{w}^{s}\) are the hydrostatic mapping function and wet mapping function, respectively; \(Z_{h}\) and \(Z_{w}\) are the zenith hydrostatic delay and zenith wet delay, respectively; \(\Delta_{P}^{s}\) and \(\Delta_{L}^{s}\) are satellite, receiver, and site displacement effect corrections [37] for pseudoranges and carrier-phase measurements, respectively; \(X_{r}\), \(Y_{r}\), and \(Z_{r}\) are the receiver a priori coordinates; \(X^{s}\), \(Y^{s}\), and \(Z^{s}\) are the satellite coordinates; \(\delta X_{r}\), \(\delta Y_{r}\), and \(\delta Z_{r}\) are the receiver position increments relative to the a priori position; \(\delta t_{r,S}\) denotes receiver clock error; \(\lambda_{IF}^{s}\) is the iso-free wavelength; \(B_{IF}^{s}\) is the ionosphere-free ambiguity (bias) parameter.  
All of the multi-GNSS observations are processed together in one common least square estimator. The details of the processing strategy are presented in Table 2.
## 1 Introduction
The Gaofen-4 (GF-4) satellite is a geosynchronous orbit optical satellite with an orbital height of 36,000 km and a high resolution of 50 m, which has the advantages of realizing real-time cryptic observation of the marine environment [25; 26]. GF-4 can observe disaster events such as algal blooms and red tides through pointing control to provide fast and reliable optical remote-sensing data [27]. The GF-4 satellite is the Earth observation satellite with the highest geostationary orbit resolution in the world. The electronic maritime surveillance satellite constellation basically meets the needs of ship surveillance in terms of coverage and time resolution [28]. It not only has continuous surveillance capabilities and large-scale coverage capability, but also has a geostationary orbit (GEO) high-resolution optical detection with fast response capability and higher positioning accuracy (better than 200 m) [29]. It can realize continuous monitoring of key targets, and make up for the problems of low time resolution and small coverage of traditional reconnaissance satellites. Meanwhile, it will also launch a series of new application areas, including the ability to move across the surface of the Earth based on video shooting methods, target detection and monitoring [30], long-term evolution monitoring of various natural elements of the Earth, etc. Furthermore, it has a quick response capability, and the delivery time from user's application to geospatial information can be reduced to a few minutes. GEO high-resolution optical imaging satellite is a new generation of optical remote-sensing satellite [31]. Its outstanding features can be summarized in two words, one is \"moving target surveillance\" and the other is \"real-time surveillance\". It raises the satellite reconnaissance function to the surveillance function, which can realize the dramatic change of satellite application. In addition, GEO high-score satellites can enhance the value of information applications based on new product types [29]. However, there is no suitable empirical model based on GF-4, a high-resolution satellite in geosynchronous orbit, for Chla inversion in the coastal waters such as Bohai Sea. Therefore, it is urgent for us to develop Chla inversion algorithms for GF-4. In this paper, the concentration distribution and change details of Chla is analyzed based on the panchromatic multispectral sensor (PMS) data of the newly launched high-resolution GF-4 satellite in synchronous orbit. Based on GF-4 satellite PMS data and field measurement data, an empirical algorithm model of Chla inversion is established. The advantages of geostationary orbit satellite for continuous observation are used to analyze the detailed change of Chla in a short time (within 10 min) in the Bohai Sea. The structure of this paper is organized as follows. Section 2 describes the study area, satellite date and data processing. Section 3 describes the detailed process of establishing the inversion model of Chla concentration. The discussion and conclusions are summarized in Sections 4 and 5.
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 2 Classification of Fungi, Bacteria, and Microalgae Species and Their Remediation Performance
### Wood Rot Fungi  
Wood rot fungi are classified as _Basidiumycota (Basidiumycetes)_[46]. These species of fungus play an essential role in the bioremediation of organic contaminants in addition to their role in wood rooting. No preconditioning is necessary before the transformation of pollutants [47]. Therefore, they have extensive flexibility and the capacity to degrade tissues. The wood-degrading fungi species are distinguished as strong competitors like _T. versicolor, Phanerozoate_ sp., _Pleurotus_ ssp., etc., and weak competitors like _Ganoderma aplanatum_ and _Dichomitus squalens_ based on their capacity to colonise new environments. According to their mode of action in wood tissues, they can be classified as brown rot fungi and white rot fungi [48]. The total absorption of spilled explosive, organochlorine insecticides, polychlorinated biphenyls (PCBs), petroleum hydrocarbons, and PAHs are achieved by white-rot fungi (_Polyporus_ sp. and _Planareochaate_) [49; 50]. In a study by [PERSON] and [PERSON] (2008), it was found that _Lentiinus squravsoulus_ can increase the carbon content, bioavailable phosphorus, and organic matter of soils contaminated with motor oil [51]. The Fenton reaction discovered that _Gloeophyllun trabeum, Fomitopsis pinicola, and Daedaledic kinsii_ effectively degraded Dichloro-Diphenyl-Trichloroethane (DDT) [52].  
Numerous fungi have been shown to degrade PAHs, including, _Agrocybe dura, Agrocybe praccox, Gymnopilus luteofilous, Irpex laceus, Mycenagaleri caluta, Trantes ochraca, Stropharia aeruginosa, Stropharia rugosonnulata, Phanerchaete velutina, and Physisporinus rivolosus_[53]. According to [PERSON] et al. (2016) [54], the fungal consortium _Apergillus nomius, Rhizomucr variabilis, Aspergillus flavus, and Trichoderma asperellum_ played a vital role in the breakdown of pyrene and benzo [\(\alpha\)] pyrene phenanthrene. Thus, bioremediation of contaminated resources is highly dependent on fungal colonies. Therefore, further research could be done to comprehend the precise mechanisms and processes employed to enhance the performer's inherent capacity.
# Design and Analysis of Systematic Batched Network Codes
## 2 Ordinary Batched Network Coding
### Decoding Algorithms
#### 2.3.1 Two-Step Decoding  
Suppose that the number of inactive packets during encoding is 0, so that \(d_{i}^{B}=0\) for all batches. We first discuss the two-step decoding approach. The first step recovers a fraction \(\eta\geq K/K^{\prime}\) of precoded packets using a belief propagation (BP) algorithm, which repeats the following operations:1. A batch \(i\) is said to be decodable if \(d_{i}^{A}=\text{rank}(\mathbf{G}_{i}\mathbf{H}_{i}^{(L)})\); solve a decodable batch by Gaussian elimination;
2. Substitute the decoded (precoded) packets into other undecoded batches and update the corresponding batch degree and generator matrix.  
The BP decoding algorithm has a low computation cost that does not depend on the total number of message packets \(K\). The second step decodes the precoded packets to recover the message packets, which is expected to be successful if the first step recovers at least \(\eta\) fractions of all the precoded packets.  
Assume that the ranks of batch transfer matrices at the destination node \(\text{rank}(\mathbf{H}_{i}^{(L)})\) are i.i.d and follow the distribution \(\mathbf{h}=(h_{0},h_{1},\ldots,h_{M})\). We call \(\mathbf{E}[\mathbf{h}]=\sum_{i=1}^{M}ih_{i}\) the expected rank. According to the theory of BATS codes [23], it is possible to design a degree distribution \(\mathbf{\Psi}\) for a given rank distribution \(\mathbf{h}\) such that when \(K\) is large, the BP decoding can recover a given \(\eta\) fraction of the precoded packets with a high probability when the coding rate \(K/n\) is larger, but very close to \(\mathbf{E}[\mathbf{h}]\). In other words, we only need slightly more than \(K/\mathbf{E}[\mathbf{h}]\) batches to recover the \(K\) message packets.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
###### Abstract  
Voice signals acquired by a microphone array often include considerable noise and mutual interference, seriously degrading the accuracy and speed of speech separation. Traditional beamforming is simple to implement, but its source interference suppression is not adequate. In contrast, independent component analysis (ICA) can improve separation, but imposes an iterative and time-consuming process to calculate the separation matrix. As a supporting method, principle component analysis (PCA) contributes to reduce the dimension, retrieve fast results, and disregard false sound sources. Considering the sparsity of frequency components in a mixed signal, we propose an adaptive fast speech separation algorithm based on multiple sound source localization as preprocessing to select between beamforming and frequency domain ICA according to different mixing conditions per frequency bin. First, a fast positioning algorithm allows calculating the maximum number of components per frequency bin of a mixed speech signal to prevent the occurrence of false sound sources. Then, PCA reduces the dimension to adaptively adjust the weight of beamforming and ICA for speech separation. Subsequently, the ICA separation matrix is initialized based on the sound source localization to notably reduce the iteration time and mitigate permutation ambiguity. Simulation and experimental results verify the effectiveness and speedup of the proposed algorithm.  
speech separation; beamforming; principle component analysis; independent component analysis +
Footnote †: journal: Applied Sciences