esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 3 Antenna Implementation and Measurement Results
### Envelope Correlation Coefficient (ECC)  
The Envelope Correlation Coefficient (ECC) is a crucial parameter for evaluating MIMO antenna systems. It quantifies the correlation between the signals received or transmitted by different antenna elements in a MIMO system. ECC values range from 0 to 1, where lower values indicate better performance. Typically, an ECC value below 0.5 is considered acceptable for MIMO antenna systems. This threshold indicates that the antenna elements are sufficiently independent, effectively providing spatial diversity, which leads to improved performance in terms of data rate and reliability. In practice, ECC is often approximated using S-parameters (3). Figure 20 shows the ECC value of the MIMO antenna with slits is less than 0.04 within all required bands. Obviously, the antenna elements are sufficiently independent, providing spatial diversity, high data rates, and reliability.  
\[ECC\approx\frac{\left|S_{11}^{*}S_{12}+S_{21}^{*}S_{22}\right|^{2}}{\left(1- \left|S_{11}\right|^{2}-\left|S_{21}\right|^{2}\right)\left(1-\left|S_{22} \right|^{2}-\left|S_{12}\right|^{2}\right)} \tag{3}\]  
Figure 19: The measured and simulated radiation patterns of the MIMO antenna with slits at 5.45 GHz, 6.32 GHz, and 7.06 GHz.  
Figure 20: The measured and simulated ECC values of the MIMO antennas with and without slits.
## 4 Experiments and Results
### Implementation Details  
Each model was trained using PyTorch 1.10.0 on the NVIDIA GeForce RTX 3090 GPU. For all networks, the batch size was 4. In the comparative experiment, the learning rate for the SETR [33], TransU-Net [34] and our model was 0.0002 and for the other models was 0.0001. The iterations of all models are 10,000 iterations (27 epochs). After each epoch, we store the model with the best validation loss by using checkpoints.  
In order to solve the problem of poorly trained deep convolutional neural network models due to small amounts of data, we first trained the network on ImageNet [38] and then fine-tuned the model on a small-scale dataset. In this way, we significantly reduced the time needed to train the model and achieve better results, initializing the weights of the convolutional layer with the pre-trained weights of our CNN backbone. For the training, we used a combinational average of pixel-wise cross-entropy loss and dice loss.
## 6 Facilities for Scientific Observation Instruments
### Network Mass Storage  
#### 6.3.1 Introduction  
The large volumes of data generated by the Antarctic scientific observing apparatus cannot all be sent back to the remote monitor due to the restrictions of the communication speed and flow. The scientific instruments can save part of local data itself [35]. Meanwhile, AAOISE provides mass storage based on Ethernet, with capacity of about 6 TB available and 21 TB extensible. The network storage system responds to scientific instruments at any time for data storage through high-speed Ethernet during the unattended period. During annual regular maintenance, the network storage system's hard disks should be unloaded from the management system and the new formatted ones will be loaded in. The old disks with data will be brought back to the remote monitor center.  
Figure 7: Interfaces for the science instruments.  
AAOISE's storage devices consist of some NASs under the control of PCM. The centralized network storage system based on controlled NAS has superiority against the usual computer local storage system. It has higher data reliability and larger storage space than local storage. Each NAS works in the B/S mode and acts itself as the server, offering ample application interfaces to the clients. Its storage space is manageable. It supports the redundancy storage scheme which can save data backups once part of disk error or disk array damage occurs.  
It is of great significance how to manage the network mass storage system and give full play to its advantages to make it a self-maintainable automatic system.
# Identification of C-Band Radio Frequency Interferences from Sentinel-1 Data
## 3 RFI Detection and Estimation
An example of identification of low power RFI is shown in Figure 6. The V and H power spectra for the 12 short strips, each formed by the nine rank pulses at the beginning of each burst are shown in a linear scale, normalized with respect to S1 thermal noise floor. The multi-looked, whitened spectra are quite homogeneous, and slight deviations may be appreciated, like those encircled. The availability of multiple pulses-per-burst allow to distinguish between pulsed and continuous RFI. As for the detection, the two sets of histograms show that KL divergence was capable to detect those affected by RFI (upper plot) by the others.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 2 Interference Alignment
### General IA Techniques  
Current IA techniques along with signal dimensions, and corresponding references are listed in Table 2. General IA techniques are concisely prsented in the subsequent paragraphs.  
Linear IA is a fundamental technique of IA where the alignment of signal spaces is performed based on linear precoding schemes. Distributed IA is centred on the local channel knowledge rather than global channel knowledge. In subspace IA, the interferences are aligned to multidimensional subspace as opposed to a single dimension. Blind IA is a method that majority of the IA results are based on the assumption of perfect, and occasionally, global channel state information (CSI). In ergodic IA the CSI is partitioned into complimentary pairings for a wide class of channel distributions over which the interference can be aligned so that individual users are able to achieve marginally over half of its interference-free ergodic capacity at any SNR [25]. Retrospective IA techniques refer to the IA schemes that make use of delayed CSI knowledge at the transmitter. Lattice alignment adopts lattice codes in an IA network with the lattices structured in a way in which the interfering signals at an interfered receiver arrive on identical lattice, and the wanted signal stands separately [26]. IA and cancelation (IAC) is a technique developed when neither IA nor cancelation applies alone.  
Opportunistic IA (OIA) is used to represent the IA-based opportunistic scheduling methods. The IA-based opportunistic scheduling methods aim to improve the performance of wireless communication networks by exploiting the channel fluctuations achieved through applying user selection or antenna selection [27]. Asymptotic IA is an opportunistic technique that exploits the presence of complementary channel states in equal magnitudes to achieve linear IA [28].
# Investigating the Reliability of Nonlinear Static Procedures
## 2 Basics of Nonlinear Static Procedures Adopted in Standards
The following expressions clarify these issues and the specific criteria adopted in this paper to compute these entities:  
\[\zeta=k\cdot\frac{63.7\cdot\left(V_{y}^{*}\cdot\text{d}_{\text{max}}^{*}-V_{ \text{max}}^{*}\cdot\text{d}_{y}^{*}\right)}{V_{\text{max}}^{*}\cdot\text{d} _{\text{max}}^{*}}+5 \tag{8}\]  
\[\eta=\sqrt{\frac{10}{5+\xi}}\geq 0.55 \tag{9}\]  
\[S_{a}(T^{*})=\eta\cdot S_{a_{E}}(T^{*}) \tag{10}\]  
\[S_{d}(T^{*})=\left(\frac{T^{*}}{2\pi}\right)^{2}\cdot S_{a}(T^{*}) \tag{11}\]  
where \(\xi\) is the damping coefficient, \(\eta\) is the factor that alters the elastic spectrum for conventional viscous damping coefficients \(\xi\) different from 5%, and \(S_{a}(T^{*})\) and \(S_{d}(T^{*})\) are the acceleration spectral value and displacement spectral value at period \(T^{*}\). The coefficient k considers the dissipative capacity of the structure and, in particular, the characteristics of the hysteresis cycle. As recommended in the NTC18 [8] and consistently with [9; 10], the following values can be assumed, depending on the structural typology:  
* Structures with high dissipative capacity: k = 1;
* Structures with moderate dissipative capacity: k = 0.66;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **N2 Methods and CSM for d\({}^{*}\)max Calculation** & **Equation** \\ \hline N2, [PERSON] & & \\ et al. (2000) & \(d_{max}^{*}=\left\{ \begin{array}{ll}d_{max}^{*}\\ \frac{\mathcal{L}_{max}^{*}}{R}\left[1+(R-1)\cdot\left(\frac{T}{T_{c}}\right) \right]\geq d_{max}^{*}\end{array} \right.\) & \(R<1\) and \(T^{*}\geq T_{c}\) & (4) \\ N2, [PERSON] & & & \\  et al. (2017) & & & \\  et al. Structures with low dissipative capacity: k = 0.33. In this paper, these values of k are tested while varying the configurations analyzed for the examined case study, in order to assess the sensitivity of the achievable results. The expressions summarized in Table 3 refer to the computation of the maximum displacement demand \(\mathrm{d}^{*}\mathrm{max}\) expected under a given earthquake. However, these expressions may be effectively inverted in order to assess the maximum intensity measure (IM) of the earthquake that produces the attainment of a displacement demand associated with a given LS. In this paper, these expressions are used in such a way by imposing, for example, that \(\mathrm{d}^{*}\mathrm{max}\) is equal to the ultimate displacement associated with the NC LS attainment (i.e., \(\mathrm{d}^{*}\mathrm{max}=\mathrm{d}^{*}_{u,NC}\)) to estimate the maximum value of the PGA compatible with NL (PGANC).
## 4 Numerical Examples
### Crack Growth under Quasi-Static Loading
#### 4.1.2 Compression Test of a Regularly Dispersed Pores Microstructure  
In this example, a microstructure built of plaster with holes spaced at regular intervals was considered. [PERSON] et al. and [PERSON] and [PERSON] [43; 44] have investigated this example numerically and experimentally. Figure 9 shows the compression test geometry of a plate with 23 holes of diameter d = 0.2 mm. The specimen's dimensions are 2 mm and 3 mm.  
The material properties were adopted from [43]: E = 12 GPa, \(\
u\) = 0.3 and G\({}_{\text{c}}\) = 1.4 \(\times\) 10\({}^{-6}\) kN/mm. Monotonic increments of compressive displacement \(\Delta\)u = 1.5 \(\times\) 10\({}^{-4}\) mm were prescribed. The length scale parameter was chosen as \(\eta\) = 0.03 mm.  
Figure 8: Numerical crack trajectories for quasi-static tensile tests for: (**a**) h = 5 mm; (**b**) h = 6 mm; (c) h = 9 mm.  
Figure 7: Geometry of the specimen and adopted boundary conditions: tensile test.  
Experimentally, [PERSON] and [PERSON] [44] found that the cracks initiate and propagate vertically from the holes. [PERSON] et al. [43] obtained, in addition to the vertical cracks observed in the tests, an initiation of cracks from the left and right sides of the holes, which is unphysical, due to high compression stress in these regions. Figure 10 shows the crack path simulation given by our models. This demonstrates the ability of the developed model to initiate cracks from a microstructure that is not damaged, with a correctly predicted crack trajectory after nucleation.
## 5 Conclusions  
The conducted experimental studies have shown that (1) the radiated hydroacoustic waves with a frequency of 22 Hz, when propagating along the shelf of medium depth, excite seismoacoustic waves at a similar frequency in the seabed and in the surf zone; (2) the radiated hydroacoustic energy is not localized in the \"bottom-air\" channel but propagates in the conditions of the deep sea and the sea of medium depth according to laws close to spherical; (3) with a decrease in the sea depth, the share of transformed energy increases.  
Based on the results of two experiments (excluding the results of the second experiment at stations 1 and 4 and also the results of the first experiment at station 3), we can state that the hydroacoustic energy between the radiator and the laser strainmeter is distributed according to a law close to spherical.  
To accurately determine the patterns of hydroacoustic energy transformation into seismoacoustic energy, it is necessary to study the spatial distribution of hydroacoustic energy along the \"radiator-coastal laser strainmeter\" route throughout the entire water column. These studies can be most effectively carried out using autonomous unmanned underwater vehicles that serve as carriers of highly efficient hydroacoustic receiving systems [18]. Additionally, it is necessary to install bottom seismoacoustic stations at control points on the seabed to determine the main characteristics of the generated seismoacoustic waves propagating in their zones.  
Project administration, writing--review and editing, G.D. and S.D.; visualization, M.B. and M.I.; software, S.B. and V.C.; data curation, V.O., A.S. and A.P.; methodology, I.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the partial financial support from grant No. 075-15-2024-642 \"Study of the processes and patterns of occurrence, development and transformation of catastrophic phenomena in the oceans and on continents using seismoacoustic monitoring methods\".  
Not applicable.  
**Data Availability Statement:** Third-party data were used. Restrictions apply to the availability of these data. All information about these data and the possibility of obtaining them can be found at [EMAIL_ADDRESS].  
We would like to express our deep gratitude to all employees of the Physics of Geospheres laboratory.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Novel Alternating \(\mu\)-Law Companding Algorithm for PAPR Reduction in OFDM Systems
## 4 Proposed Scheme
### Sequential \(\mu\)-Law Companding Method (SULC)  
Herein, we will describe the SULC methodology and outline the procedural steps in Algorithm 1. These consist of two stages.  
In the first stage: (initial stage)  
Initially, assign the input signal \(\mathbf{x}\) to \(\mathbf{x}^{\prime}\) by the following method:  
\[\mathbf{x}^{\prime}=[x_{0},x_{1},\dots,x_{N-1}], \tag{18}\]  
where \(\mathbf{x}=[x_{0},x_{1},\dots,x_{N-1}]\) is the OFDM signal. Next, set the index value \(n\) to zero and assign the \(\mathbf{0}\) to the position vector \(\mathbf{p}\), which is utilized for auxiliary purposes to signal recovery at the receiver end.  
Following this step, compute the PAPR for \(\mathbf{x}\) and denote it by \(PAPR_{\mathbf{x}}\), representing the initial PAPR value.  
In the second stage:  
We were first performing \(\mu\)-law companding to \(x[n]\) individually, and then the result was updated to the corresponding \(x^{\prime}[n]\) within \(\mathbf{x}^{\prime}\). Following this, compute the PAPR of \(\mathbf{x}^{\prime}\) and denote by \(PAPR_{\mathbf{x}^{\prime}}\). Compare \(PAPR_{\mathbf{x}^{\prime}}\) with \(PAPR_{\mathbf{x}}\). If \(PAPR_{\mathbf{x}^{\prime}}\) is lower than \(PAPR_{\mathbf{x}}\), then refresh the position flag component \(p[n]\) to \(1\) and update the value of \(PAPR_{\mathbf{x}}\) to \(PAPR_{\mathbf{x}^{\prime}}\). Conversely, if \(PAPR_{\mathbf{x}^{\prime}}\) is not lower than \(PAPR_{\mathbf{x}}\), recover \(x^{\prime}[n]\) to its state before performing \(\mu\)-law companding until all tone components, \(x[n]\), have been through the \(\mu\)-law companding evaluation process to accept or reject individually. In other words, we perform companding selectively, not all of them unconditionally. Finally, assign the \(\mathbf{x}^{\prime}\) values to \(\mathbf{y}_{SULC}\), which is equivalent to the output signal generated by the SULC. In other words, \(\mathbf{y}_{SULC}\) denotes the transmitted signal that has reduced the PAPR.  
```
The first stage: (initial stage) Step 1  Set \(\mathbf{x}^{\prime}:=\mathbf{x}\), \(n:=0\) and position vector \(\mathbf{p}:=\mathbf{0}\). Step 2  Compute the PAPR of \(\mathbf{x}\), denoted as \(PAPR_{\mathbf{x}}\). The second stage: Step 1  For \(n=0:N-1\) \(x^{\prime}[n]=sgn(x[n])\dfrac{log(1+\mu|x[n]|)}{log(1+\mu)}\)  Compute the PAPR of \(\mathbf{x}^{\prime}\), denoted as \(PAPR_{\mathbf{x}^{\prime}}\)  If \(PAPR_{\mathbf{x}^{\prime}}<PAPR_{\mathbf{x}}\) then  Set \(p[n]\) to \(1\)  Update \(PAPR_{\mathbf{x}}\) to \(PAPR_{\mathbf{x}^{\prime}}\)  Else  Recover \(x^{\prime}[n]\) from \(x[n]\)  End If  End For Step 2  Set \(\mathbf{y}_{SULC}:=\mathbf{x}^{\prime}\)
```  
**Algorithm 1** The SULC algorithm
## 3 Materials and Methods
### KG Reasoning Based on Predicate Decomposition  
We perform the splitting of polysemous predicates through Equation (5), and obtain new datasets \(\mathcal{K}^{{}^{\prime}}_{train},\mathcal{K}^{{}^{\prime}}_{valid}\), which have \(N^{\prime}\) predicates. It is easy to know that \(N^{\prime}=|\mathcal{R}|+\sum_{a\in\mathcal{R}_{\rho}}(|split_{a}|-1)\).  
We use \(\mathcal{K}^{{}^{\prime}}_{train}\) and \(\mathcal{K}^{{}^{\prime}}_{valid}\) as training and validation sets to train the KG reasoning model. We adopt the same training method as the original paper of the baseline model to verify the effectiveness of the PDEC framework. For example, for the TransE model, minimize the following hinge loss:  
\[\mathcal{L}=\sum_{(h,r,t)\in\mathcal{K}^{{}^{\prime}}_{train}}\sum_{(h^{\prime },r,t^{\prime})\in\mathcal{K}^{{}^{\prime}}_{train}}\left[\gamma+Dist(\vec{h}+ \vec{r},\vec{t})-Dist(\vec{h}^{+}+\vec{r},\vec{t}^{{}^{\prime}})\right]_{+} \tag{6}\]  
where \(Dist(\vec{h}+\vec{r},\vec{t})\) denotes the dissimilarity measure, which we take to be either the \(L_{1}\) or the \(L_{2}\)-norm; \(\left[x\right]_{+}\) denotes the positive part of \(x\); \(\gamma>0\) is a margin hyperparameter; and \(\bar{\mathcal{K}}^{{}^{\prime}}_{train}\) is the negative sample training set generated from \(\mathcal{K}^{{}^{\prime}}_{train}\) in a 3:1 ratio as follows:  
\[\bar{\mathcal{K}}^{{}^{\prime}}_{train}= \left\{(h^{\prime},r,t^{\prime})|(h,r,t)\in\mathcal{K}^{{}^{ \prime}}_{train},\right. \tag{7}\] \[\left.\left(h^{\prime},r,t^{\prime})\
otin\mathcal{K}^{{}^{ \prime}}_{train},h^{\prime}\
eq h\lor t^{\prime}\
eq t\right\}\right.\]  
Let the trained model be \(\Theta\). When using the model \(\Theta\) to perform KGC tasks on the test set \(\mathcal{K}_{test}\), follow the rules:  
\[\Theta_{k}(h_{i},r_{a},t_{j})=\bigvee_{r_{a,x}\in split_{a}}\Theta_{k}(h_{i},r _{a,x},t_{j}) \tag{8}\]  
where \(\Theta_{k}(h_{i},r_{a},t_{j})\) is a logical function, representing the judgment of the model \(\Theta\) on whether triplets \(t_{j}\) is the top \(k\) prediction of the query \((h_{i},r_{a},?)\), or whether triplets \(h_{i}\) is the top \(k\) prediction of the query \((?,r_{a},t_{j})\).  
We continue to use the example of the predicate Person-Language to explain the reason for this approach. Assume that PDEC decomposes Person-Language into two predicates, Person-NativeLanguage and Person-ForeignLanguage, representing the relationships between persons and their native languages and other languages, respectively. If the model \(\theta\) determines that (A, Person-NativeLanguage, English) is reasonable (indicating that the native language of person A is English), then in all cases, English must be the language spoken by A. Therefore, (A, Person-Language, English) is reasonable. Similarly, if (A, Person-ForeignLanguage, English) is reasonable, then (A, Person-Language, English) is also reasonable. In essence, the new predicates induced by PDEC are subordinate to the original predicate. This design enables PDEC to achieve performance improvement in predicting links on KGs.
# Numerical Simulation of Intrachamber Processes in the Power Plant
## 2 Materials and Methods  
### Problem Statement and Mathematical Model  
The conjugate problem of heat exchange in CC PP with a cylindrical slot channel and a four-nozzle cover (Figure 1) is considered. The turbulent spatial flow of the compressed heat-conducting gas is described by the following system of conservation equations [23]:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\rho u_{i}}{\partial x_{i}}=0 \tag{1}\]  
\[\frac{\partial\rho u_{i}}{\partial t}+\frac{\partial\rho u_{i}u_{j}}{\partial x _{j}}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x_{j}} \left(\mu\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{i}}\right)-\frac{2}{3}\mu\frac{\partial u_{k}}{\partial x_{k}} \delta_{ij}\right)+F_{i} \tag{2}\]  
\[\frac{\partial\rho E}{\partial t}+\frac{\partial\rho Eu_{j}}{\partial x_{j}}= -\frac{\partial pu_{j}}{\partial x_{j}}+\frac{\partial u_{i}\tau_{ij}}{ \partial x_{j}}+\frac{\partial q_{j}}{\partial x_{j}}+F_{i}u_{i} \tag{3}\]  
\[c\rho\frac{\partial T}{\partial t}=\lambda\
abla^{2}T, \tag{4}\]\[p=\rho RT \tag{5}\]  
where \(\rho\) is the gas density, \(p\) is the pressure, \(u_{i}\) is the velocity components, \(F_{i}\) is the external volume force, \(T\) is the temperature, \(q_{j}\) is the component of heat flow density vector, R is the specific gas constant; \(\mu\) is the dynamic viscosity, \(\lambda\) is the heat conductivity factor, \(E=c_{\
u}T+0.5u_{i}^{2}\) is the total specific energy, \(G=E+p/\rho=c_{p}T+0.5u_{i}^{2}=h+0.5u_{i}^{2}\) is the total specific enthalpy; \(\tau_{ij}=2\mu S_{ij}-\frac{2}{3}\mu\frac{\partial u_{i}}{\partial x_{k}}\delta _{ij}\) is the viscous stress tensor; \(S_{ij}=\frac{1}{2}\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\right)\) is the strain velocity tensor. In the literature, there is no substantiation for the application of turbulence models for flows in the flow paths of the combustion chamber of the power plant, and there is no experimental confirmation of the model correctness. As a consequence, the implementation of the mode with a significant difference in the transfer of individual components of the Reynolds stress tensor based on the analysis of physical models for the flow in flow paths of PP was taken into account. Initial equations are averaged accordingly [25], values are given as a sum \(\theta=\widetilde{\theta}+\theta^{\prime\prime}\), where \(\widetilde{\theta}=\overline{\rho\theta}/\overline{\rho}\) and \(\overline{\rho\theta}\), \(\overline{\rho}\) are the averaged parameters according to the Reynolds procedure. Thus, \(\theta^{\prime\prime}\) includes both turbulent and density fluctuation.
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 2 AMI Architecture and Last-Mile Communication
### Multi-Rate, and Multi-Regional (MR-) FSK  
MR-FSK is the commonly commercialized PHY layer [15] that provides low complex implementation, good power efficiency due to the constant envelop of the signal and multi-regional operation support. In addition, MR-FSK must be filtered to achieve regulation requirements of each region, such as GFSK. The forward error correction (FEC) encoder is optionally processed for a PHY service data unit (PSDU). It also provides various data rates between 5 and 400 kbps. In this article, a 2 FSK PHY layer with various data rates is studied [6; 15]. The data rates and frequency bands in different countries are shown in Table 2[7].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (MHz)**} & **Data Rate** & **Channel Spacing** & \multirow{2}{*}{**Alias**} & **Transmit Power** \\  & **(kbps)** & **(kHz)** & & **(dBm)** \\ \hline \multirow{4}{*}{433.92} & 50 & 200 & 2 FSK-433-50 & 10 \\  & 100 & 400 & 2 FSK-433-100 & 10 \\  & 200 & 400 & 2 FSK-433-200 & 10 \\ \hline \multirow{4}{*}{443} & 50 & 200 & 2 FSK-443-50 & 10 \\  & 100 & 400 & 2 FSK-443-100 & 10 \\  & 200 & 400 & 2 FSK-443-200 & 10 \\ \hline \multirow{4}{*}{448} & 50 & 200 & 2 FSK-448-50 & 10 \\  & 100 & 400 & 2 FSK-448-100 & 10 \\  & 200 & 400 & 2 FSK-448-200 & 10 \\ \hline \multirow{4}{*}{923} & 50 & 200 & 2 FSK-923-50 & 20 \\  & 100 & 400 & 2 FSK-923-100 & 20 \\  & 150 & 400 & 2 FSK-923-150 & 20 \\  & 200 & 400 & 2 FSK-923-200 & 20 \\ \hline \multirow{4}{*}{2440} & 50 & 200 & 2 FSK-2440-50 & 5 \\  & 150 & 400 & 2 FSK-2440-150 & 5 \\ \cline{1-1}  & 200 & 400 & 2 FSK-2440-200 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The studied data rates and frequency bands of 2 FSK PHY layer in Thailand.