esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 TR-FrFT Method for LFM Signal Detection in Underwater Multi-Path Environment
### TR-FrFT Method
First, let the received signal pass through a TR system and acquire Figure 7. Second, calculating the optimal order of \(z(t)\), we can obatin 0.9782 and we plot the result of TR-FrFT method in the optimal order fractional Fourier domain in Figure 8. The difference shown in Table 3 between the maximum spurious peak and the main peak is 9.82 dB. Compared to FrFT, the difference is improved 9.01 dB. Obverse that the received signal processed by TR-FrFT achieves focusing in the optimal order fractional Fourier domain. Compared to the FrFT method, TR-FrFT has the ability to resist multi-path effects and a higher SNR. Figure 5: The waveform of received signal. Figure 6: The result of received signal processed by the optimal order FrFT.
## 4 Simulation Experiments: Security Analysis and Comparison
### Chosen-ciphertext Attack  
The chosen-ciphertext attack [50] allows the attacker to select a ciphertext and observe its corresponding plaintext. The objective of this attack is to obtain information about the decryption algorithm or secret key. Our proposed algorithm ensures that attackers are unable to extract any information about the secret key through the ciphertext-plaintext pairs. In the proposed scheme explained in Section 3, the secret keys consist of parameters, namely \(p,b,G,s,q\), and \(B\). Each ciphertext-plaintext pair in this scheme has distinct elements for all components of the key. For example, when dealing with a prime value \(p\), there are numerous possible options for \(b\), and for each chosen \(b\), the resulting ciphertext is entirely different. Consequently, the attacker cannot extract any information about the secret key from any given ciphertext-plaintext pair. Hence, the proposed scheme ensures security against the chosen-ciphertext attack.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 2 5G Testbed Overview
### Fpga  
One of the main challenges faced by embedded system developers is the diversity of external I/O interfacing requirements, which might be Ethernet, optical, analog conversion or Gigabit serial [10]. Thus, to enable data transfer between the embedded system and other hardware devices, the creation of external I/O interface is needed. Nowadays, FPGA come with one or more expansion slots for  
Figure 2: Block diagram of the proposed 5G mmWave testbed.  
daughter boards (mezzanine module), which can increase the I/O possibilities. Such expansion slots allow FPGA designers to not rely solely on each FPGA I/Os and decrease system development costs. Without it, designers would need to change FPGA development board whenever system enhancements require additional interfaces [11].  
To overcome this, the VITA 57 FPGA Mezzanine Card (FMC) connectivity ANSI/VITA standard was developed in 2008, to provide a modular standard mezzanine I/O interfacing solution to a FPGA located on a carrier board [11]. This brought several benefits such as maximum data throughput of 40 Gbps, low latency, reduced design complexity, and reduction of system cost [10].  
Such standard foresees two types of expansion slots, also called FMC, which are usually populated on FPGA commercial solutions (LPC and HPC). While the first FMC solution has 160 pins, the second one has 400 available pins. Moreover, the Low Pin Count (LPC) connector provides 68/34 user-defined signals for single-ended and differential signals, respectively, as well as one serial transceiver pair, clocks, Joint Test Access Group (JTAG), and Inter-Integrated Circuit (I2C) interfaces. The High Pin Count (HPC) maintains precedent mentioned functionalities while providing twice the user-defined signals of the LPC connector also with 10 serial transceiver pairs.  
At the time of this work, the most recent and powerful _Xilinx_ FPGA (with more logic resources) available on the market is the Virtex FPGA's family, namely Ultrascale [9]. For example, the XCVU9P-L2 FLGA2104E chip provides more than 2000 k logic cells [12]. However, it is relatively expensive, and thus it was decided to acquire the Virtex-7 VC707 evaluation kit instead [13] (see Figure 3). This board is characterized as highly-flexible, having a high-speed serial XC7 VX485T-2 FFG1761C chip-set, and a good compromise between cost and available logical resources.
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
## 3 Proposed En-LDM Scheme  
### System Model  
Under the two-layer LDM signal scenario, due to the fact that the antenna gain and installation height are not always able to best match the conditions of business planning, a large range of EL holes may still occur within the effective coverage range of CL, resulting in inconsistent coverage ranges of different services. Therefore, it is of importance to improve the reception performance of the EL fixed service while maintaining the reception performance of the original CL mobile service. To achieve that goal, the En-LDM scheme  
Figure 1: Block diagram of the traditional LDM system transceiver: (a) two-layer LDM transmitter; (b) two-layer LDM receiver.  
in the following is proposed by multiplexing part of the data of the fixed service stream in the upper layer of the LDM.  
Figure 2 depicts the block diagram of the En-LDM transceiver. In Figure 2, in addition to the modules defined in the traditional LDM scheme, the additional modules in whole colored shadow are added to deal with fixed services. In the En-LDM scheme, the CL of the LDM signal transmits both mobile services and fixed services, while the EL only bears fixed services. The fixed service transmitted by CL consists of punching bits, while the fixed service carried by EL is composed of the bits left after punching. Specifically, according to the puncturing method, the data used for the fixed service flow are divided into two parts: EL1 and EL2. EL1 is assigned to the lower layer of the LDM, while EL2 is used for mobile traffic, whose data symbols are sent on top of the LDM signal in an FDM manner. In this case, the SNR of EL2 is much higher than that of EL1, which will facilitate the correctness of decoding.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
###### Abstract  
Bat algorithm, as an optimization strategy of the observation matrix, has been widely used. Observation matrix has a direct impact on the reconstructed signal accuracy as a projection transformation matrix, and it has been widely used in various algorithms. However, for the traditional experimental process, randomly generated observation matrices often result in a larger reconstruction error and unstable reconstruction results. Therefore, it is a challenge to retain more feature information of the original signal and reduce reconstruction error. To obtain a more accurate reconstruction signal and less memory space, it is important to select an effective compression and reconstruction strategy. To solve this problem, an adaptive bat algorithm is proposed to optimize the observation matrix in this paper. For the adaptive bat algorithm, we design a dynamic adjustment strategy of the optimal radius to improve its global convergence ability. The results of our simulation experiments verify that, compared with other algorithms, it can effectively reduce the reconstruction error and has stronger robustness.  
adaptive bat algorithm; observation matrix; reconstruction error; signal reconstruction +
Footnote †: journal: Applied Physics Letters
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology  
The methodology proposed to analyze the performance of ad hoc routing protocols in IoST networks is summarized in Figure 1.  
First, in the proposed methodology, a \"base experiment\" is defined considering a relevant case of use of ad hoc IoST networking. Then, orbits of potential nodes participating in the network are generated considering actual satellite orbits. With this information, a Monte Carlo simulation of the ad hoc IoST network was performed using our ST-INETMANET simulation testbed developed explicitly for this purpose. The resulting network performance metrics were then formatted to apply the 2k factorial analysis [61].  
For this contribution, the proposed methodology was applied to find the most relevant factors affecting the search and maintenance of communication routes in ad hoc IoST networks when proactive and reactive routing approaches are used. In the following subsections, we provide the details of the experiments implemented to perform this analysis.  
Figure 1: Flowchart of the proposed methodology to analyze the performance of ad hoc routing protocols in IoT networks.
# Properties and Functionality of Cereal Non-Starch Polysaccharides in Breadmaking
## 2 Definition and Occurrence of Cereal NSPs
Type I arabinoagalactans are more common in nature than type II. The two types are distinguished from each other by their molecular structure. Arabinogalactan of type II is characterized by a more extended structure, as the galactan chains, in which galactose residues are linked by 1\(\rightarrow\)3 glycosidic bonds, are attached to short galactan chains linked by 1\(\rightarrow\)6 glycosidic bonds, which in turn are linked by further ranching in the form of arabinose residues, linked by (1\(\rightarrow\)3) and/or (1\(\rightarrow\)5) glycosidic bonds. In type I arabinoagalactans, the molecule is built from a galactan chain with arabinose residues attached via (1\(\rightarrow\)4) glycosidic bonds [48-50]. Xyloglucans are the general name for linear NSPs consisting of chains of glucans made up of glucose residues linked by \(\beta\)-(1-4) glycosidic bonds that are substituted with xylose. The content of xyloglucans in quinoa and amaranth grains was about 30% of the insoluble fraction of dietary fiber [2,51,52]. So far, the content of xyloglucans in other cereals has not been analyzed. Pectic polysaccharides are composed of galacturonic acid molecules linked by \(\alpha\)-(1\(\rightarrow\)4) glycosidic bonds, most often with attached rhamnose residues, but also galactose, arabinose, xylose, fucose, or glucuronic acid. The average content of pectic polysaccharides in wheat, corn, and rice grains is 0.24-0.25% [53]. In quinoa grain, it is 4.68%, while in barley grain, it is 2.41% [54]. Some carboxyl groups in the galacturonic acid chain occur as methyl esters, and two types of pectins are distinguished: high methoxyl and low methoxyl. Additionally, protopectin is also present [55-57]. High methoxyl pectins contain >50% methanol-esterified carboxyl groups, while low methoxyl pectins contain <50% methanol-esterified carboxyl groups [58]. In cereal grains, high methoxyl pectins account for 28.7-36.4% of the total pectin content, and low methoxyl pectins account for 30.9-35.2% of the total pectin content. The remaining pectin content consists of protopecting, which are water-insoluble pectin substances (32.3-36.5%) [59,60]. \begin{table}
\begin{tabular}{c c c c} \hline
**Non-Starch Polysaccharides** & **Source** & **Content [\%]** & **References** \\ \hline \multicolumn{3}{c}{Wheat grain} & 4.1–9 & [5] \\ Endosperm of durum wheat grain & 1.5–1.8 & [14] \\ Endosperm of wheat grain & 0.8–0.9 & [10] \\ Whole wheat flour & 3–5 & [46] \\ Endosperm of barley grain & 1.2–1.3 & [10] \\ Barley bran & 10.5 & [8] \\ Barley flour & 2.9–4.8 & [11] \\ Arabinoxylans & Whole ve grain & 7.6 & [3] \\ Whole ve grain & 8.6 & [7] \\ Whole ve grain & 7.1–12.2 & [9] \\ Endosperm of rye grain & 2.2–2.4 & [10] \\ Endosperm of oat grain & 1.2 & [4] \\ Out bran & 5.2 & [4] \\ Whole oat grain & 11.6 & [13] \\ Corn grain & 4.8–5.6 & [6] \\ Rice grain & 2.97–6.84 & [12] \\ \hline \end{tabular}
\end{table} 
Table 1: Content of non-starch polysaccharides in cereal grains.
# Performance Improvement by FRFT-OFDM for Visible Light Communication and Positioning Systems
## 2 Principle
### Fractional Fourier Transform
\begin{split}\hat{\mathbf{y}}&=\mathbf{F}_{p}\mathbf{\hat{x}}~{ }=~{}[\text{Re}(\mathbf{F}_{p})+j\text{Im}(\mathbf{F}_{p})]\cdot[\text{Re}(\mathbf{\hat{x} })+j\text{Im}(\mathbf{\hat{x}})]\\ &=[\text{Re}(\mathbf{F}_{p})\cdot\text{Re}(\mathbf{\hat{x}})-\text{Im}( \mathbf{F}_{p})\cdot\text{Im}(\mathbf{\hat{x}})]+j[\text{Re}(\mathbf{F}_{p})\cdot\text{Im }(\mathbf{\hat{x}})+\text{Im}(\mathbf{F}_{p})\cdot\text{Re}(\mathbf{\hat{x}})]\end{split}  \tag{9}\]  
Next, we separate the real and imaginary parts of the complex vector \(\hat{\mathbf{y}}\). \[ \begin{split}\mathbf{y}&=\left[\begin{array}{c}\text{ Re}(\mathbf{F}_{p})\cdot\text{Re}(\mathbf{\hat{x}})-\text{Im}(\mathbf{F}_{p})\cdot\text{Im}( \mathbf{\hat{x}})\\ \text{Re}(\mathbf{F}_{p})\cdot\text{Im}(\mathbf{\hat{x}})+\text{Im}(\mathbf{F}_{p})\cdot \text{Re}(\mathbf{\hat{x}})\end{array}\right]\\ &=\left[\begin{array}{cc}\text{Re}(\mathbf{F}_{p})&-\text{Im}(\mathbf{F}_{p})\\ \text{Im}(\mathbf{\hat{x}})\end{array}\right]\cdot\left[\begin{array}{c}\text{ Re}(\mathbf{\hat{x}})\\ \text{Im}(\mathbf{\hat{x}})\end{array}\right]\\ &=\mathbf{R}_{p}\mathbf{x}\end{split}  \tag{10}\]  
\(\mathbf{R}_{p}\) is the RPFRFT matrix given by  
\[ \begin{split}\mathbf{R}_{p}~{}=~{}\left[\begin{array}{cc}\text{Re}( \mathbf{F}_{p})&-\text{Im}(\mathbf{F}_{p})\\ \text{Im}(\mathbf{F}_{p})&\text{Re}(\mathbf{F}_{p})\end{array}\right]\end{split}  \tag{11}\]  
And, the inverse RPFRFT (RPIFRFT) matrix can be expressed as  
\[ \begin{split}\mathbf{R}_{-p}~{}=~{}\left[\begin{array}{cc}\text{Re}( \mathbf{F}_{-p})&-\text{Im}(\mathbf{F}_{-p})\\ \text{Im}(\mathbf{F}_{-p})&\text{Re}(\mathbf{F}_{-p})\end{array}\right]\end{split}  \tag{12}\]
# A 2D Lithospheric Magnetic Anomaly Field over Egypt Using Gradient Data of Swarm Mission
## Data Set and Methodology
### Modeling Techniques
The resolution matrix (\(\mathbf{R}\)) of the weighted damped least-squares fit method is given by Equation (5):  
\[\mathbf{R}=\mathbf{A}^{T}\mathbf{W}_{d}\mathbf{A}/\Big{(}\mathbf{A}^{T}\mathbf{W}_{d}\mathbf{A}+\alpha^{2 }\mathbf{I}\Big{)} \tag{5}\]  
Figure 2 illustrates the relationship between the norm of the model coefficients (\(\mathbf{m}^{T}\mathbf{m}\)) and the standard deviation of the misfit of the N-S gradient [32; 33]. The misfit has been calculated according to [17]. The black, red, blue, and green lines correspond to the trade-off curves of \(\
abla\mathbf{B}_{x}\), \(\
abla\mathbf{B}_{y}\), \(\
abla\mathbf{B}_{z}\) and \(\
abla\mathbf{B}_{t}\), respectively, where \(\
abla\mathbf{B}_{t}\) is the gradient of the total lithospheric magnetic field. The values of the damping parameters for each trade-off curve are in the order \(10^{-12}\), \(10^{-10}\), \(10^{-9}\), \(10^{-8}\), \(10^{-7}\), \(10^{-6}\), \(10^{-5}\), \(10^{-4}\), \(10^{-3}\), \(10^{-2}\), \(10^{-1}\), \(10^{0}\), \(10^{1}\), \(10^{2}\). We have chosen the damping parameter \(\alpha^{2}=10^{-4}\), which locates at the knee of the trade-off curves [32; 33]. Figure 3 shows the diagonal elements of the resolution matrix R with respect to the parameter numbers of \(\
abla\mathbf{B}_{t}\) of the N-S gradient data at the best damping parameter (\(10^{-4}\)). It shows that the diagonal element of the resolution matrix approaches unity, which demonstrates the great description of the model for the data. The increase of R at higher orders could be interpreted in terms of the dominance of the noise at higher orders of expansion [27; 33]. Figure 3: The diagonal elements of the resolution matrix at damping parameter \(\alpha^{2}=10^{-4}\) with respect to the parameter numbers. Figure 2: The trade-off curve of the north–south \(\
abla\mathbf{B}_{x}\), \(\
abla\mathbf{B}_{y}\), \(\
abla\mathbf{B}_{z}\), and \(\
abla\mathbf{B}_{t}\). The variation of the norm (\(\mathbf{m}^{\text{T}}\text{m}\)) with respect to the standard deviation (\(\mathbf{\sigma}\)) of the misfit shows that the knee of the curve is located at the damping parameter, equaling \(10^{-4}\). In order to truncate the model to a certain degree, we have studied the spectrum of the model [\(R_{n}=(n+1)\sum_{m=0}^{n}(a_{n}^{m})^{2}\)] as shown in Figure 4a. It shows that the spectrum increases from degree 0 to degree 7 and then decreases again from 7 to 60. Figure 4b is similar to Figure 4a but on a logarithmic scale. The horizontal dashed line in Figure 4b corresponds to the spectrum of value equal to zero at degree 56. According to [15], the initial degrees from 0 to 7 correspond to fields from a source region other than the lithospheric magnetic field, while the degrees from 7 to 56 correspond to the lithospheric magnetic field. Therefore, we have expanded our data from degree 7 up to 56, and subsequently, the vector model coefficients become of length 1625.
## 3 Material and Methods
### Methods  
#### 3.2.1 AnFIS  
In 1993, [PERSON] proposed the ANFIS method. The parameters of ANFIS have been optimized within the framework of the adaptive neural network. An adaptive network is a network structure made up of nodes and the links to which these nodes are connected. The adaptive network forces the output of each node to depend on the parameters of that node. On the other hand, the learning algorithm determines how these parameters should be changed in order to minimize the defined error measure [13].  
In ordinary neural networks, nodes have the same functionality, and nodes in neighboring layers are completely interconnected. However, in neural fuzzy systems, the nodes have different functionality, and the nodes in the neighboring layers are not completely connected to each other. These differences are usually compatible with a special part of the fuzzy system. Modern neural fuzzy systems are generally feed-forward and multilayered. The ANFIS model includes a fuzzy system like Sugeno and uses backpropagation learning [14].
# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
###### Abstract  
In recent years, the risk related to oil spill accidents has significantly increased due to a global growth in offshore extraction and oil maritime transport. To ensure sea safety, the implementation of a monitoring system able to provide real-time coverage of large areas and a timely alarm in case of accidents is of major importance. Satellite remote sensing, thanks to its inherent peculiarities, has become an essential component in such a system. Recently, the general Robust Satellite Technique (RST) approach has been successfully applied to oil spill detection (RST-OIL) using optical band satellite data. In this paper, an advanced configuration of RST-OIL is presented, and we aim to extend its applicability to a larger set of observation conditions, referring, in particular, to those in the presence of severe sun glint effects that generate some detection limits to the RST-OIL standard algorithm. To test such a configuration, the DeepWater Horizon platform accident from April 2010 was selected as a test case. We analyzed a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images that are usually significantly affected by sun glint in the Gulf of Mexico area. The accuracy of the achieved results was evaluated for comparison with a well-established satellite methodology based on microwave data, which confirms the potential of the proposed approach in identifying the oil presence on the scene with good accuracy and reliability, even in these severe conditions.  
oil spill; MODIS; optical band; near infrared; sun glint; DeepWater Horizon; RST-OIL Article Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions [PERSON] et al. (2019), [PERSON] (2019), [PERSON] (2019), [PERSON] (2019) and [PERSON] (2019)
## 2 Materials and Methods
### Input Data  
#### 2.2.1 Daymet Data  
Daymet weather data were obtained from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC) Daymet (Version 2) [16]. The dataset was selected due to its relatively fine spatial resolution of \(1\times 1\) km. The dataset provides a grid of mosaicked gridded estimates of daily weather parameters. Parameters are generated using a network of ground observation sites to provide input data which is then interpolated and extrapolated using Daymet model algorithms. Daily data for respective years of the selected AmeriFlux study sites (referred to as \"field years\") were extracted from the various sites using the \"Single Pixel Extraction\" tool ([[https://daymet.ornl.gov/dataaccess.html#SinglePixel](https://daymet.ornl.gov/dataaccess.html#SinglePixel)]([https://daymet.ornl.gov/dataaccess.html#SinglePixel](https://daymet.ornl.gov/dataaccess.html#SinglePixel))) with specified latitude, longitude, and date range. Minimum temperature (Tmin, \({}^{\circ}\)C), maximum temperature (Tmax, \({}^{\circ}\)C), incoming shortwave radiation (Rg, J m\({}^{-2}\) d\({}^{-1}\)), average vapor pressure (P, kPa), snow-water equivalent (kg m\({}^{-2}\)), precipitation (mm), and day length (s) were  
Figure 2: Climographs from cities near the AmeriFlux sites. US-Ne1, US-Ne2, and US-Ne3 are located at the University of Nebraska’s Eastern Nebraska Research and Extension Center near Mead, NE; US-Br1 and US-Br2 located approximately 8 km southwest of Ames, Iowa; US-Bo1 is located approximately 12 km south of Champaign, Illinois and US-Ro1 is located near Rosemount, Minnesota which is located approximately 24 km south of Saint Paul, Minnesota. (Source: 1981–2010 normals, usclimatedata.com).  
downloaded in CSV format in which average daily temperature, vapor pressure deficit, and incoming PAR were calculated in Microsoft Excel. Average daily temperature (T\({}_{\text{avg}}\)) was calculated as:  
\[\text{Tavg}=(\text{Tmin}+\text{Tmax})/2 \tag{1}\]  
Vapor pressure deficit (VPD) was calculated using vapor pressure, P, as:  
\[\text{VPD}=(0.61078\cdot\exp[(17.269)(\text{Tavg})])/(\text{Tavg}+237.8)-\text{P} \tag{2}\]  
Incoming PARin (umol m\({}^{-2}\) d\({}^{-1}\)) was calculated from incoming shortwave radiation (Rg) as:  
\[\text{PARin}=\text{Rg}\cdot 2.07 \tag{3}\]  
The constant 2.07 is computed as the product of the fraction of total shortwave at the top of the atmosphere, 0.45 Rg, J m\({}^{-2}\)d\({}^{-1}\)[17] and approximate energy conversion over the PAR range, 4.6 umol J\({}^{-1}\)[18].
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 3 Choice of System Components and Programming
### Bespoke Network  
An architecture divided into five layers is presented, ranging from the AWSs to the internal VM. The network of AWSs captures and sends data to a central node, from where users can consult the data. This data transfer is carried out using LoRaWAN technology, a long-range, low energy consumption open-source protocol [63]. In addition, it provides high data transmission security by encrypting packets at the network and application level, which means that the measuring devices can only be read by authorized servers that have registered the devices with their corresponding keys (Figure 1).