esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Inverse Problem  
The Bayesian approach in statistics aims to decrease uncertainty in inference and decision-making problems by utilizing all available information. As new data emerge, they are integrated with prior data to support statistical processes. In Bayesian theory, this information is expressed in terms of probability, frequently depicted by a probability density function. The Bayes theorem establishes a formal method for combining new information with prior information [35; 36; 37; 38; 39].  
\[\pi_{Posterior}(P)=\pi(P|Y)=\frac{\pi_{Prior}(P)\pi(Y|P)}{\pi(Y)} \tag{17}\]  
where \(\pi_{Posterior}(P)\) is the posterior probability density; \(\pi_{Prior}(P)\) is the a priori density of the parameters, that is, the encoded information for the parameters available before the measurements; \(\pi(Y|P)\) is the likelihood function, which expresses the probability density of the \(Y\) measurements given the \(P\) parameters; \(\pi(Y)\) is the marginal probability density of the measurements, which plays the role of a normalization constant.  
In several applications, one is more interested in the form presented in Equation (18) of the Bayes equation, where the marginal probability distribution of the measures is considered irrelevant, since what is expected is the optimization of the posterior distribution, which basically depends on the numerator of Formula (17) [30; 40; 41]:  
\[\pi_{Posterior}(P|\mathbf{Y})\propto\pi_{Prior}(P)\pi(\mathbf{Y}|P) \tag{18}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\theta=\frac{C}{C_{*}}\) & \(\eta=\frac{z}{L}\) & \(\tau=\frac{t}{t_{ref}}\) & \(Pe=\frac{\mu L}{D_{\alpha}}\) & \(Q=\frac{q}{q_{r}}\) \\ \hline \(K_{s}=k_{s}t_{ref}\) & \(K_{L}=k_{L}C_{e}\) & \(q_{r}=\frac{C_{e}}{\rho g}\) & \(Q^{*}=\frac{q_{s}}{q_{r}}\) & \(t_{ref}=\frac{L}{u}\) \\ \hline \end{tabular}
\end{table}
Table 1: Dimensionless parameters.  
One of the ways to determine the posterior density, \(\pi_{Posterior}(P)\), is through sampling techniques, such as the Monte Carlo methods via Markov Chain. One of the most used algorithms for the implementation of the Monte Carlo method with Markov chains is the Metropolis-Hastings algorithm. In this, from the initial value for the parameter, through the transition kernel, a new candidate parameter value is generated, which is evaluated before either being accepted or not using the ratio between the posterior probability densities of the current and candidate states. Thus, the Metropolis-Hastings algorithm describes a category of Monte Carlo methods that construct a Markov Chain in stages through the random sampling of the posterior distribution described by the Bayes relation [42].
## Materials and Methods
### Crop Growth Data  
Paddy rice growth simulation at the field scale was performed using ground-based remote sensing input data obtained at the CNU site in 2011, 2012, and 2013 and the Buan and Gimje sites in 2014. At the CNU site, 30-day-old rice seedlings (cv. [PERSON] in 2011, cv. [PERSON] in 2012, and cv. [PERSON] in 2013) were mechanically transplanted in paddy fields on 6 June 2011 (day of year (DOY) 157), 28 May 2012 (DOY 148), and 20 May 2013 (DOY 140). At the Buan and Gimje sites, 30-day-old rice seedlings (cv. [PERSON] at Buan and cv. [PERSON] and Sindongin at Gimje) were transplanted on 28 May 2014 (DOY 148). Nitrogen (N), phosphate (P), and potassium (K) fertilizers were applied in amounts corresponding to 90 N, 50 P, and 60 K kg-ha\({}^{-1}\), respectively.  
Data collected were canopy reflectance of plants, using an MSR16R multispectral radiometer (CROPSCAN Inc., Rochester, Minnesota, USA), and LAI using an LI-2200 Plant Canopy Analyser (LI-COR Inc., Lincoln, Nebraska, USA). These data were measured with four replications on DOY 188, 194, 201, 207, 215, 223, 229, 236, 243, 250, 257, and 265 in 2011; on DOY 173, 184, 193, 201, 207, 213, 226, 237, 243, 249, 262, and 276 in 2012; and on DOY 167, 172, 182, 190, 200, 206, 220, 231, and 241 in 2013 at the CNU site. Data at both the Buan and Gimje sites were measured on DOY 165, 177, 195, 203, 223, 238, 258, and 276 in 2014. The yield was estimated by multiplying four yield components, which were measured three times in the sample plots, based on random sampling at maturity. The yield components were panic number per m\({}^{2}\), spikelet number per panicle, percentage of filled grain, and the weight of 1000 grains. Weather data at the CNU site were measured using an automated weather station (WS-GP1, Delta-T Devices, Cambridge, UK). Climate data for simulation for the Buan and Gimje sites were obtained from the Korea meteorological weather stations ([[https://data.kma.go.kr/cmnu/main.do](https://data.kma.go.kr/cmnu/main.do)]([https://data.kma.go.kr/cmnu/main.do](https://data.kma.go.kr/cmnu/main.do)), accessed on 6 October 2018).  
Four vegetation indices (VIs) (Table 1) were calculated using the reflectance data as input for evaluation of the crop model formulated in this study. The four VIs were empirically chosen from structural indices available and effective for the growth determination of plant canopies using wavebands (i.e., 560, 660, and 800 nm) of multispectral optical sensors in most satellites. In these datasets, both VIs and LAI are observable. However, data of only one point is available.
_J. Sens. Actuator Netw._**2014**, \(4\), 274-296; doi:10.3390/jsan3040274  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan  
_Article_  
**Towards a Fair and Efficient Packet Scheduling Scheme in Inter-Flow Network Coding**  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 1**  
\({}^{1}\) National University of Singapore, #02-02-10, AMI Lab, IDMI, 21 Heng Mui Keng Terrace,  
Singapore 119613; E-Mail: [PERSON]IL_ADDRESS]  
\({}^{2}\) Institute for Infocomm Research, 1 Fusionopolis Way, #20-10 Connexis North Tower,  
Singapore 138632; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +65-9052-5291.  
External Editor: [PERSON]  
_Received: 23 June 2014; in revised form: 29 September 2014 / Accepted: 22 October 2014 / Published: 4 November 2014_  
**Abstract:** Network coding techniques are usually applied upon network-layer protocols to improve throughput in wireless networks. In scenarios with multiple unicast sessions, fairness is also an important factor. Therefore, a network coding-aware packet-scheduling algorithm is required. A packet-scheduling algorithm determines which packet to send next from a node's packet backlog. Existing protocols mostly employ a basic round-robin scheduling algorithm to give \"equal\" opportunities to different packet flows. In fact, this \"equal\"-opportunity scheduling is neither fair, nor efficient. This paper intends to accentuate the importance of a coding-aware scheduling scheme. With a good scheduling scheme, we can gain more control over the per-flow throughput and fairness. Specifically, we first formulate a static scheduling problem and propose an algorithm to find the optimal scheduling scheme. We then extend the technique to a dynamic setting and, later, to practical routing protocols. Results show that the algorithm is comparatively scalable, and it can improve the throughput gain when the network is not severely saturated. The fairness among flows is drastically improved as a result of this scheduling scheme.  
**Keywords:** network coding; packet scheduling; fairness; weighted clique cover
## 2 Materials and Methods
### Software and Versions  
All numerical simulations and control algorithm implementations were performed using MATLAB (version R2021a, MathWorks Inc., Natick, MA, USA). Figures were generated using MATLAB's built-in plotting functions.
###### Abstract  
Accurate parameter estimation is essential for modeling the statistical characteristics of ocean clutter. Common parameter estimation methods in generalized Pareto distribution models have limitations, such as restricted parameter ranges, lack of closed-form expressions, and low estimation accuracy. In this study, the particle swarm optimization (PSO) algorithm is used to solve the non-closed-form parameter estimation equations of the generalized Pareto distribution. The goodness-of-fit experiments show that the PSO algorithm effectively solves the non-closed parameter estimation problem and enhances the robustness of fitting the generalized Pareto distribution to heavy-tailed oceanic clutter data. In addition, a new parameter estimation method for the generalized Pareto distribution is proposed in this study. By using the difference between the statistical histogram of the data and the probability density function/cumulative distribution function of the generalized Pareto distribution as the target, an adaptive function with weighted coefficients is constructed to estimate the distribution parameters. A hybrid PSO (HPSO) algorithm is used to search for the best position of the fitness function to achieve the best parameter estimation of the generalized Pareto distribution. Simulation analysis shows that the HPSO algorithm outperforms the PSO algorithm in solving the parameter optimization task of the generalized Pareto distribution. A comparison with other traditional parameter estimation methods for generalized Pareto distribution shows that the HPSOHPSO algorithm exhibits strong parameter estimation performance, is efficient and stable, and is not limited by the parameter range.  
sea clutter; generalized Pareto distributed; hybridized particles; particle swarm optimization; parameter estimation; estimated performance
# MedicalSeg: A Medical GUI Application for Image Segmentation Management
## 3 Methods
### Image Processing Tools
#### 3.3.2 Gaussian and Median Filters  
A Gaussian filter is a low-pass spatial filter used for reducing noise, high-frequency components, and smoothing regions of an image [27]. In this sense, it is similar to the mean filter, but it uses a different kernel that has the shape of the function 'Gaussian distribution' to define the weights inside the kernel, which are used to compute the weighted average of the neighbouring points (pixels) in an image. Moreover, a median filter is a smoothing filter and it removes speckle noise and impulsive noise from the image, especially impulse and salt and pepper noise. As the median filter is applied onto an image, each pixel is replaced with the median value of its neighbours. One of the main advantages of the median filter is that it also preserves the edges present in the image. Furthermore, it does not introduce new pixel values since it only re-use existing pixel values from the window.
## 2 Materials and Methods
### Phenolic Nor-Triterpenes  
The natural phenolic nor-triterpenes **1** (6-oxopritstimerol) [20; 26], **3** (7,8-dihydro-6-oxoiguesterol, canrol) [27], **16** (6-oxotingenol) [20], **17** (3-O-methyl-6-oxotingenol) [20], and **20** (6-oxoiguesterol) [20] were isolated from the root bark of _Mayteus canariensis_, and compounds **2** (7-hydroxy-6-oxopritstimerol) [21; 28], **4** (blepharoolol) [21], **5** (7-hydroxyblepharolod) [27], **10** (blepharotriol) [21], **12** (zeylasteral) [27; 29], **13** (demethylzeylasteral) [21; 29], **14** (zeylaterone) [27; 29], **15** (demethylzeylasterone) [21; 29], **22** (7-  
Figure 1: Most frequently reported antimicrobial telastroloids from Celastraceae species.  
oxobleharodol) [21], and **25** (6-deoxyblepharodol) [21] from _Maytenus blepharodes_. Derivatives **11** (2,3-O-dimethoxyblepharodol) [21], **18** (2,3-O-dimethyl-6-oxotingenol) [20], **19** (2,3-O-diacetoxy-6-oxotingenol) [20], **21** (2,3-O-dimethyl-6-oxoiguesterol) [20], **23** (2-O-methoxy-7-oxoblepharodol) [21], **24** (pristimerol) [21; 30], and **26** (8-epi-6-deoxyblepharodol) [21] were obtained following the methodology previously described [20; 21]. The structures of these compounds are depicted in Figure 2. Moreover, the semisynthesis of derivate **6**, not previously described, was achieved by acetylation of pristimerin, a main quinone-methide triterpene isolated from the root bark of _Maytenus_ species [27]. In addition, derivatives **7-9** were prepared by catalytic reduction and further acetylation of pristimerin. Derivative **7** is a known synthetic compound [31] whose \({}^{1}\)H and \({}^{13}\)C NMR data have not been previously assigned, whereas derivatives **8** and **9** are reported herein for the first time (Scheme 1, Figures S1-S4.).
## 3 Results
### Pressure and Speed Developed in High-Resolution Collapse  
For 3 different stand-offs (0.0, 0.6 and 1.0), pressure histories at the center of the surface (i.e., green circle in Figure 1) are plotted in Figure 6.  
Figure 5: Cavity shapes for different stand-offs (\(dL/R_{0}=40\) and \(P_{\infty}=5\) MPa). Each shape shows the cavity outline at a different time.  
For \(\gamma=0\), the pressure shows a single maximum of roughly 1200 MPa. This pressure is generated by a water hammer impact of the collapsing cavity on the surface. Since the symmetry is preserved, see Section 3.2, the collapse ends when the cavity impacts on the surface generating a high-pressure shock wave (Figure 7a). After the impact, the pressure at wall decreases as the shock moves into the liquid (Figure 7b,c).  
When \(\gamma=0.6\) and \(1\) the pressure shows a double peak. As with \(\gamma=0\), the first peak is generated by the water hammer impact of the cavity with the surface (Figures 8a and 9a). After the impact, the jet splits into two high-speed/low-pressure lateral jets (Figure 8b) that will later impact with the sides of the cavity generating collapsing circles and a pair of \"side\" pressure waves (Figures 8c and 9b).  
The collapse ends when the fluid fills the circles. When this happens a third pair of pressure waves is generated (Figures 8d and 9c). The waves will later merge at the center resulting in a second pressure peak (Figures 8e and 9d). Figure 10 shows a schematic representation of a wall-attached cavity collapse with the three pairs shock formation for \(\gamma=0.6\). It can be difficult to clearly identify these hydrodynamics patterns if the simulation is run at lower resolution (e.g., [PERSON] et al. 2019).  
Figure 6: Pressure trend for different stand-offs (\(dL/R_{0}\)=133 and \(P_{\infty}=50\) MPa).  
Figure 7: Pressure and velocity field in the liquid and the spatial pressure trend at the wall (\(dL/R_{0}=133\), \(\gamma=0\) and \(P_{\infty}=50\) MPa).  
Figure 8: Pressure and velocity field in the liquid and the spatial pressure trend at the wall (\(dL/R_{0}=133\), \(\gamma=0.6\) and \(P_{\infty}=50\) MPa).  
Figure 9: Pressure and velocity field in the liquid and the spatial pressure trend at the wall (\(dL/R_{0}=133\), \(\gamma=1\) and \(P_{\infty}=50\) MPa).  
As shown in Figure 6, when \(\gamma=1\) the first peak is higher compared to the first peak of \(\gamma=0.6\). Before hitting the surface, the jet travels a longer distance for \(\gamma=1\) rather than \(\gamma=0.6\). Therefore, it is accelerated by the pressure gradient for longer resulting in a higher water hammer pressure at the wall [8,51,54].  
However, for the second peak, the behavior is reversed, and the peak is higher for \(\gamma=0.6\). The water between the collapsing rings and the wall acts as a shield mitigating the shock wave [26,55]. When \(\gamma=1\) the distance between the rings and the wall is higher than \(\gamma=0.6\) resulting in a greater mitigation effect and lower pressure peak.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 3 Design of _uw_-Cellular Network
### Network Architecture  
In this paper, design a large-scale network based on the cellular network architecture called _uw_-Cellular network. The _uw_-Cellular network is a combination of several star topology networks of different levels which provides scalability and robustness. This network architecture is friendly to those scenarios that consist of multiple mobile network nodes. In this network, more terminal nodes can join the network easily without needing to change other configurations. In addition, failure of a single terminal node does not affect the entire network.  
Figure 2 shows the topology of _uw_-Cellular network. There are three types of nodes in this network, the Gateway node, the Base Station (BS) nodes, and the Terminal nodes. The Gateway node have the responsibility to interconnect the network nodes above and under the water surface by means of satellite or radio communications. Several static nodes in the network act as the BS nodes of the _uw_-Cellular. Each BS node can cover an area, which called cell, by hydroacoustic. All cells together provide hydroacoustic coverage over larger geographical areas. In particular, the BS nodes can be connected using wired or wireless methods depending on the different application scenarios. Each BS node acts as an access point and provides access and forwarding services for all Terminal nodes within its coverage area. The Terminal nodes, which can be both fixed and mobile, access to the network by establishing a hydro acoustic connection with the BS nodes. On the one hand, when the terminal nodes are within the communication range of a particular BS node, they can interoperate directly with the shore-based center via the backbone of the network. On the other hand, if the terminal node does not have direct access to the BS nodes, usually refers to the mobile node such as AUV, it will find a more reliable and less expensive path to interact with the shore-based center based on dynamic routing strategies. Similarly, when the shore-based center needs to send instructions, it needs to find the designated node or cluster via the cellular network. In addition, the AUVs need to interact directly with each other in order to perform collaborative tasks efficiently within the dynamically changing network topology. The _uw_-Cellular network give subscribers advanced features over alternative solutions, including increased capacity, small battery power usage and a larger geographical coverage area.  
Figure 2: Topology of _uw_-Cellular network.
## 1 Introduction
Firstly, the generated binary code by the random number generator (\(RNG\)) can represent the biometric data for each user. Subsequently, we adopt the biometrics mapping network to learn the mapping relationship between the biometric data and the binary code during enrollment, which can preserve the recognition accuracy and prevent the information leakage of biometric data. Then, a random permutation module is designed to shuffle the elements of the binary code for generating the distinctive bio-keys without retraining the biometrics mapping network, which keeps the generated bio-key revocable. Next, we construct the fuzzy commitment module to encode the random binary code for generating the auxiliary data without revealing any biometric information. The bio-key is decoded from query biometric data with the help of the auxiliary data, which enhances its stability and security. Finally, the proposed scheme is applied to the AES encryption scenario for verifying its availability and practicality on our local computer. In this work, we use face image as the biometric trait to demonstrate our proposed approach. In summary, the contributions of our paper are summarized as follows:  
1. We design a biometrics mapping network based on the DNN framework to obtain the random binary code from biometric data, which prevents information leakage and maintains the accuracy performance under intra-user variations. 2. We propose a revocable bio-key protection approach by utilizing a random permutation module, which can powerfully guarantee the revocability and protect the privacy of bio-key. 3. We construct a fuzzy commitment architecture through an error-correcting technique, which can generate stable bio-keys with the help of auxiliary data, and avoid the exposure of bio-key and biometric data during enrollment. 4. We conduct extended experiments on three benchmark datasets, and the results show that our model not only effectively improves the accuracy performance but also enhances the security and privacy of the biometric authentication system. 5. Furthermore, we validate our bio-key generation model in the AES encryption application, which can reliably generate the bio-keys with different lengths to meet practical encryption requirements on our local computer. The rest of this paper is organized as follows. Section 2 reviews related work. Section 3 presents the proposed approach of bio-key generation in detail. Section 4 discusses our experimental results. Finally, we conclude in Section 5.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 3 Proposed Algorithm
### Initial Digital Beamforming Matrix Design  
Perform singular-value decomposition (SVD) on channel matrix \(\mathbf{H}\):  
\[\mathbf{H}=\mathbf{U}\mathbf{S}\mathbf{V}, \tag{11}\]  
where \(\mathbf{U}\) is the \(\mathbf{N_{\mathrm{r}}}\times\mathbf{N_{\mathrm{r}}}\) order matrix, \(\mathbf{S}\) is the \(\mathbf{N_{\mathrm{r}}}\times\mathbf{N_{\mathrm{t}}}\) order singular value matrix, and \(\mathbf{V}\) is the \(\mathbf{N_{\mathrm{t}}}\times\mathbf{N_{\mathrm{t}}}\) order matrix. Take front \(\mathbf{N_{\mathrm{s}}}\) column of matrix \(\mathbf{V}\) as a pure digital beamforming matrix \(\mathbf{F_{\mathrm{opt}}}:\mathbf{F_{\mathrm{opt}}}=\mathbf{V}(:,[1:\mathbf{N_{\mathrm{s}}}])\).  
In the hybrid beamforming structure, since there is a two-stage beamforming matrix, the traditional MMSE algorithm cannot be directly used, so the equivalent matrix is used to obtain the initial digital matrix:  
\[\mathbf{y}=\mathbf{H_{\mathrm{eff}}}\mathbf{F_{\mathrm{BB}}}\mathbf{x}+\mathbf{n} \tag{12}\]  
where \(\mathbf{H_{\mathrm{eff}}}=\mathbf{H}\mathbf{F_{\mathrm{RF}}}\) and singular-value decomposition of equivalent channel matrix \(\mathbf{H_{\mathrm{eff}}}=\mathbf{U_{\mathrm{eff}}}\)\(\mathbf{S_{\mathrm{eff}}}\), then:  
\[\mathbf{F_{\mathrm{BB}_{\mathrm{int}}}}=\mathbf{V_{\mathrm{eff}}}[:,[1:,N_{\mathrm{s}}]] \tag{13}\]  
In order to make the performance of the algorithm closer to that of the pure digital beamforming matrix, let:  
\[\mathbf{F_{\mathrm{RF}}}=\mathbf{F_{\mathrm{opt}}}\mathbf{F_{\mathrm{BB}_{\mathrm{int}}}} \mathbf{(F_{\mathrm{BB}_{\mathrm{int}}}}\mathbf{F_{\mathrm{BB}_{\mathrm{int}}}})^{*}= \mathbf{F_{\mathrm{opt}}}\mathbf{(}\mathbf{F_{\mathrm{BB}_{\mathrm{int}}}}\mathbf{)}^{\dagger} \tag{14}\]  
The argument can be obtained as:  
\[\arg(\mathbf{F_{\mathrm{RF}}})=\arg\mathbf{(}\mathbf{F_{\mathrm{opt}}}\mathbf{(}\mathbf{F_{\mathrm{ BB}_{\mathrm{int}}}}\mathbf{)}^{\dagger}\mathbf{)} \tag{15}\]  
An analog beamforming matrix that satisfies the constant-modulus requirement can be obtained from Equation (15).