esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Efficiency Analysis
### Efficiency Comparison of Several Communication Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Network Layer Communication Method**} & **2 min** & **5 min** & **10 min** & **30 min** & **60 min** \\ \hline \multirow{3}{*}{IP protocol} & Million packets & 4.26 & 10.52 & 21.28 & 63.47 & 128.51 \\  & Amount of data transmitted/Mb & 49,809.46 & 122,837.52 & 248,540.61 & 741,347.66 & 1,501,003.01 \\  & Throughout capacity/Mbps & 415.08 & 409.46 & 414.23 & 411.86 & 416.95 \\ \hline \multirow{3}{*}{IP Packet signature} & Million packets & 0.06 & 0.14 & 0.29 & 0.87 & 1.72 \\  & Amount of data transmitted/Mb & 641.96 & 1579.89 & 3217.72 & 9574.15 & 18,903.31 \\  & Throughout capacity/Mbps & 5.35 & 5.27 & 5.36 & 5.32 & 5.25 \\ \hline \multirow{3}{*}{AH protocol} & Million packets & 1.25 & 3.12 & 6.2 & 18.42 & 36.98 \\  & Amount of data transmitted/Mb & 14,354.48 & 35,786.94 & 71,181.33 & 211,615.71 & 424,800.51 \\  & Throughout capacity/Mbps & 119.62 & 119.29 & 118.64 & 117.56 & 118 \\ \hline \multirow{3}{*}{ESP protocol} & Million packets & 1.23 & 3.08 & 6.14 & 18.48 & 36.89 \\  & Amount of data transmitted/Mb & 14,151.8 & 35,376.74 & 70,483.29 & 212,260.47 & 423,755.83 \\  & Throughout capacity/Mbps & 117.93 & 117.92 & 117.47 & 117.92 & 117.71 \\ \hline \multirow{3}{*}{MHC method} & Million packets & 1.31 & 3.3 & 6.58 & 19.79 & 39.45 \\  & Amount of data transmitted/Mb & 14,849.35 & 37,430.08 & 74,798.15 & 224,780.88 & 448,085.5 \\ \cline{1-1}  & Throughout capacity/Mbps & 123.74 & 124.77 & 124.66 & 124.88 & 124.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results of transmission efficiency of five communication methods. The SA established by the AH protocol needs to set the life cycle and renegotiate and update the SA policy parameters before the end of the cycle. The shorter the life cycle, the higher the security of the parameters, but the negotiation process will affect the transmission efficiency. It can be seen from the test results shown in Table 4 that, under the above experimental conditions, the time used to transmit data of the same length in the MHC mode is shorter than that in the AH protocol communication mode. By calculating the average throughput of each test group, we found that the MHC method was more efficient than the AH protocol in each comparison group, and the larger the transmitted data, the more pronounced the gap. Specifically, taking the AH protocol communication method with a life cycle of 2 min as an example, when transmitting data with a length of 1G, the average throughput of the MHC method is 2.79% higher than the average throughput of the AH protocol, but when transmitting data with a length of 50G, this ratio increases to 5.77%.
# TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space
## 4 System Development and Evaluation
### Implementation
then used to convert the \(x\) and \(y\) values from the joystick into digital values, which can be read via SPI (serial peripheral interface). Edge computing is utilised by TrackInk by running the main process flow within the Raspberry Pi device, converting the TLE sets into positional data which is further transformed into azimuth and elevation figures that can be sent to servo motors. The \"skyfield\" library is utilised to convert TLE sets into positional data and to read relative positions of other celestial bodies. This system allows for GPS coordinates (from the GPS sensor) to be input into the library to get the relative position of the given satellite or celestial body. This position is then rotated using standard rotational matrices [70] relative to the rotations from the IMU sensor readings. Additional rotations are performed to get the corresponding servo positions. An output from this program can be seen in Figure 5, detailing the values for the Moon, Sun, Mars, and the ISS. Satellite and celestial body data were acquired from servers hosting databases. These servers are accessible via HTTPS protocols using a standard internet connection. These figures are then used by the processing unit to determine the relative position of the objects. System information is sent to ThingSpeak via a WiFi connection for cloud-based data analysis. Each desired data point is sent to this server via an HTTPS PUT API call to be further used in graphical figures within the ThingSpeak interfaces. Figure 4: Flow of information, and interactions and communication between various components in the proposed framework.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 2 Overview of Radio Wave Propagation for 6G Systems  
In this section, previous studies for the radio propagation channel at different candidate frequencies of the 0.1-1 THz band for the future 6G wireless communication system are reviewed.
## 3 Results
### ELM Results  
Two different sets of ELM simulations of US-LA3 were run from years 2016-2023. The first one used a constant vegetation coverage where the site was assumed to be 50% _Spartina_ and 50% _Juncus_. The second one used the HLS-classification results listed in Table 2 to prescribe the yearly vegetation patch-type distribution. Our analysis focused on comparing the results from the two ELM simulations regarding aerenchyma flux (plant-mediated methane transport), which is one of the three components of total methane flux, and the most sensitive to vegetation type. Figure 5 shows that, when using HLS classification for vegetation cover to run the model, the aerenchyma flux shows high interannual variability, with high fluxes in years 2016-2017, followed by a significant decrease around years 2018-2021, and further followed by an increase in the following years.  
In order to assess the impact of HLS data on the ELM's methane estimations, we evaluated both simulations against the observed methane flux at the Eddy Covariance flux tower at US-LA3. The EC tower provides the data of the total methane flux over years 2021-2022 at a half-hourly timescale. As presented in Figure 6, model validation results (using a linear regression between the observed and modeled total methane flux) showed a slight improvement in R\({}^{2}\), while a more significant improvement is observed in the Root Mean Square Error (RMSE) of the regression, which was decreased by 7.5% when using HLS-classified vegetation cover.  
Figure 4: Results of US-LA3 within-wetland eco-hydrological patch-type classification using the NDVI-based classification method for years 2016–2023 using HLS data.
# New Vector-Space Embeddings for Recommender Systems
## 2 Related Work
### Matrix Factorization  
The matrix factorization approach is based on building a user-item interaction matrix, for example user-item rating or purchase events matrix. The matrix is often organized such that one dimension represents users while the second dimension represents items. Usually, user-item matrices are sparse as a user is likely to rate or purchase a limited number of items. The approach further decomposes this matrix into the product of two matrices of lower dimensionality. This can be accomplished using dimensionality reduction techniques such as Singular Value Decomposition (SVD) or Principal Component Analysis (PCA). The goal of the approach is to discover the latent features underlying the interactions between users and items. The method can be briefly outlined as follows:  
Consider \(R\) to be the ratings matrix, which includes the users' ratings for items. The goal is to find the two matrices \(P\) and \(Q\) such that \(R\approx P\times Q^{T}\). This way, user-item interactions can be modeled as inner products. Each user is associated with a vector \(p\) while each item is associated with a vector \(q\). The inner product between \(q_{i}\) and \(p_{u}\) captures user \(u\)'s taste/interest for item \(i\). The dimensionality reduction problem is often augmented by a regularization factor, in order to reduce overfitting. Even though the SVD method can obtain an exact solution, due to the computational complexity, stochastic gradient descent is more often used to solve the problem [17; 18; 19].  
There have been some variants and improvements of the standard matrix factorization approach. Only a few examples are given below just as an illustration. In [20], the matrix factorization is enhanced by using social networks' information through incorporating social regularization. The social regularization is developed in such a way that a user's taste is close to the user's friends' average taste.  
In [21], an Enhanced SVD (ESVD) matrix factorization model is proposed. The model combines classical matrix factorization techniques with active learning in order to perform ratings completion. Moreover, an iterative multi-layer ESVD model is proposed to improve the accuracy of prediction.  
In [22], a new regularization method is proposed for matrix factorization. The method is called the elastic net, such that both ridge and lasso regularization methods are combined in a linear way. Moreover, stochastic gradient descent is applied to fit the model. The goal of the model is predicting the users' ratings for unseen movies.
## 2 DAE for Feature Line Spectrum Enhanced Extraction
### Introduction to Line Spectrum Enhancement Method  
The specific algorithm of our method is as follows:  
Suppose we have a divided subset of samples, \(\left\{(x_{l}{}^{(1)},y^{(1)}),\ldots,(x_{l}{}^{(m_{l})},y^{(m_{l})})\right\}\), which contains \(m\) samples. For a single sample, \((x,y)\), the cost function is as follows  
\[J(W,b,x,y)=\frac{1}{2}\big{\|}h_{W,b}(x)-y\big{\|}^{2} \tag{3}\]  
Give a dataset consisting of a subset of divided samples, containing \(m\) samples, and define the overall cost function as:  
\[\begin{array}{l}J(W,b)=\left[\frac{1}{2}\sum\limits_{i=1}^{m}J(W,b;x^{(i)},y^ {(i)})\right]+\frac{\lambda}{2}\sum\limits_{l=1}^{n_{l}-1}\sum\limits_{i=1}^{ s_{l}}\sum\limits_{j=1}^{s_{l+1}}{(W_{ji}^{(l)})}^{2}\\ =\left[\frac{1}{2}\sum\limits_{i=1}^{m}\left(\frac{1}{2}\Big{\|}h_{W,b}(x^{(i) })-y^{(i)}\Big{\|}^{2}\right)\right]+\frac{\lambda}{2}\sum\limits_{l=1}^{m_{l} -1}\sum\limits_{i=1}^{s_{l}}\sum\limits_{j=1}^{s_{l+1}}{(W_{ji}^{(l)})}^{2} \end{array} \tag{4}\]  
Figure 1: Principle of the autoencoder networks for noise removal.  
where \(W\) and \(b\) are neural network parameters, \(\lambda\) is the weight decay coefficient, \(n_{l}\) is network layers' number, \(s_{l}\) is nodes' number in the network at the layer \(l_{th}\), and \(W_{ji}^{(l)}\) denotes the connection weight of unit \(i_{th}\) at the layer \(l_{th}\) to unit \(j_{th}\) at the layer \((l+1)_{th}\).  
Our goal is to compute the parameters with the gradient descent algorithm to find the minimum of the function \(J(W,b)\). Each parameter is first initialized to a very small and near-zero random value, then each iteration of the gradient descent method updates the parameters \(W,b\) according to the following function:  
\[W_{ij}^{(l)}=W_{ij}^{(l)}-\alpha\frac{\partial}{\partial W_{ij}^{(l)}}J(W,b) \tag{5}\]  
\[b_{i}^{(l)}=b_{i}^{(l)}-\alpha\frac{\partial}{\partial b_{i}^{(l)}}J(W,b) \tag{6}\]  
The process is:  
1A forward propagation calculation is performed to calculate the activation values for each layer of the network using initialization parameters.  
2 For each output unit in the layer \(n_{l}\)th, the residuals are calculated according to Equation (7):  
\[\begin{split}&\delta_{i}^{(n_{l})}=\frac{\partial}{\partial z_{i} ^{(n_{l})}}\frac{1}{2}\big{\|}y-h_{W,b}(x)\big{\|}^{2}\\ &=-(y_{i}-a_{i}^{(n_{l})})\cdot f^{\prime}(z_{i}^{(n_{l})})\end{split} \tag{7}\]  
where \(z_{i}^{(n_{l})}\) denotes the input weighted sum of the \(i_{th}\) cell of the layer \(n_{l}\)th.  
3 For each layer of \(l=n_{l}-1,n_{l}-2,n_{l}-3,\ldots,2\), the residuals of the \(i_{th}\) unit of the layer \(l_{th}\) are calculated according to Equation (8):  
\[\delta_{i}^{(l)}=(\sum_{j=1}^{s_{l+1}}W_{ji}^{(l)}\delta_{j}^{(l+1)})f^{\prime }(z_{i}^{(l)}) \tag{8}\]  
4 Calculate the required partial derivatives as in Equations (9) and (10):  
\[\frac{\partial}{\partial W_{ij}^{(l)}}J(W,b;x,y)=a_{j}^{(l)}\delta_{i}^{(l+1)} \tag{9}\]  
\[\frac{\partial}{\partial b_{i}^{(l)}}J(W,b;x,y)=\delta_{i}^{(l+1)} \tag{10}\]  
5 Repeat the above steps until the cost function is less than the given value.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map
### Uncertainties Analysis  
Every step of CML-based rainfall retrieval may introduce errors. In order to further improve the accuracy of rainfall measurement, it is necessary to analyze the sources of errors and their impacts.
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 4 Models Including Blockchain Technology
### BS Model: SataaS Channel with Blockchain  
The market demand derivation for the SataaS channel is similar to that in the NS model. However, by capturing the features of the BS model, the SO needs to pay an additional blockchain infrastructure cost, assumed to be fixed cost \(B\). This fixed cost mainly includes the costs of satellite operations, blockchain network maintenance, and security measures to ensure continuous service availability [44]. Therefore, we can model the blockchain-embedded SataaS channel market demand and the SO's profit as follows:  
\[\begin{split} D_{s}^{B}&=1-p_{s}^{B}-\beta\times s \\ \pi_{s}^{B}&=p_{s}^{B}\times(1-p_{s}^{B}-\beta\times s )-C-B\end{split} \tag{12}\]  
By solving this equation, the optimal price in the SataaS channel is \({p_{s}^{B}}^{*}=\frac{1-\beta s}{2}\), and the profit is \({\pi_{s}^{B}}^{*}=\frac{(1-\beta s)^{2}}{4}\).  
Similar to the results for the NS model, both the price and the SO's profit decrease as the loss of data risk increases. Nevertheless, the difference from the NS model is that the price and profit in the BS model also increase as the coefficient \(\beta\) decreases. This is attributed to the adoption of blockchain technology, which reduces the risk of data loss. [[https://www.codemotion.com/magazine/backend/blockchain/blockchain-data-security/](https://www.codemotion.com/magazine/backend/blockchain/blockchain-data-security/)]([https://www.codemotion.com/magazine/backend/blockchain/blockchain-data-security/](https://www.codemotion.com/magazine/backend/blockchain/blockchain-data-security/)) (accessed on 3 November 2023). In addition, the SO has an additional fixed minus term \(B\) which is the cost of employing BCT.
## 3 Results and Discussion  
### Characterization of the Magnetic Nanoparticles by Fourier-Transform Infrared Spectroscopy  
The MNPs samples were characterized by Fourier-Transform Infrared Spectroscopy (FT-IR), the results were shown in Figure 2. It can be seen that all the MNPs have the absorbance peaks around 3400 cm\({}^{-1}\) and 1630 cm\({}^{-1}\), which was contributed by the hydroxyl of water in the MNPs. All the MNPs have a stretching vibration peak around 580 nm for Fe-O bond. Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-NH\({}_{2}\) have stretching vibration peaks around 1092 for Si-O bond. Due to the amidogen in Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) was interfered by the hydroxyl of water, it is hard to identify the amidogen in the MNPs. Thus, a further step for the quantitative determination of amidogen was performed by ninhydrin test [24]. The results showed that the amidogenin Fe\({}_{3}\)O\({}_{4}\)@NH\({}_{2}\) and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-NH\({}_{2}\) was 0.64 and 0.512 mg/g, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **Standard Curves** & **Detection** & **Linear Range** & **Correlation** \\  & & **Wavelength (nm)** & **(mg/mL)** & **Coefficient (R\({}^{2}\))** \\ \hline Congo red & \(Y=13.459X+0.0398\)a  & 498 & 0.01–0.16 & 0.9994 \\ Methyl blue & \(Y=24.708X-0.0282\)a  & 577 & 0.01–0.16 & 0.9993 \\ Methyl violet & \(Y=35.356X-0.0261\)a  & 570 & 0.005–0.08 & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detection conditions and standard curves for analysis of dyes.
## 1 Introduction
### Multiband Microwave Radiometers
\begin{table}
\begin{tabular}{c c} \hline \hline
**Application** & **Frequency (GHz)** \\ \hline Clouds water content & 21, 37, 90 \\ Ice classification & 10, 18, 37 \\ Sea oil spills tracking & 6, 6, 37 \\ Rain over soil & 18, 37, 55, 90, 180 \\ Rain over the ocean & 10, 18, 21, 37 \\ Sea ice concentration & 18, 37, 90 \\ Sea surface temperature & 6.6, 10, 18, 21, 37 \\ Sea surface wind speed & 10, 18 \\ Snow coating & 6.6, 10, 18, 37, 90 \\ Soil moisture & 1.4, 6.6 \\ Atmospheric temperature profiles & 21, 37, 55, 90, 180 \\ Atmospheric water vapor & 21, 37, 90, 180 \\ Vegetation Water Content & 1.4 \\ Land surface temperature & 7, 10 \\ Biomass & 7, 10, 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Suitable frequency bands for most prominent MWR applications. Selected frequency bands for each application have been derived from [1; 2]. a much better spatial resolution, specially when the coherent reflection process dominates over the incoherent one [8]. Therefore, both techniques can be used together to improve the spatial resolution of the overall measurement since they are sensitive to the same geophysical parameters, for instance in the case of soil moisture [9]. The combination of MWR and GNSS-R data has also been used to improve sea surface salinity retrievals, by correcting the sea state impact on the brightness temperature using GNSS-R observables [10]. Another example of data fusion with a similar approach is the combination of MWR measurements with optical sensor data. In [11] a downscaling approach to improve the spatial resolution of soil moisture estimates obtained by the SMOS (Soil Moisture and Ocean Salinity) mission with the use of higher resolution visible/infrared (VIS/IR) satellite data is presented. The results of this study show a strong correlation between VIS/IR satellite data and soil moisture status.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 4 Performance Evaluation  
This section presents the analysis of \(P_{\text{max}}\) for both macro and small cell deployments using OL/TPC. The performance of the system was quantified using the interference generated in the network and the received SIR level at the serving eNodeB. The analysis started with the uniform distribution of macro/small cells followed by the HetNet deployment scenarios which were uniformly distributed small cells within a macro cell, small cells near the macro cell eNodeB, and small cells on the cell edge within a macro cell. Let us consider each deployment scenario one by one.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 5 Evaluation and Discussion
### Varied Number of Targets, Uniformly Distributed  
In this section, we evaluate the performance of the HEED, FTG, and NU-FTG for a varied number of targets that are uniformly distributed. The parameters for the simulation are configured as follows:  
* \(N_{tgt}=200,\ 400,\ \ldots,\ or\ 2000\),
* \(\Delta T=0\), and
* \(P_{r_{1}}=P_{r_{2}}=P_{r_{3}}=P_{r_{4}}=0\%\).  
We executed three experiments for HEED, FTG, and NU-FTG, respectively. An experiment consists of 10 sequences with the different number of targets: 200, 400, \(\ldots,\) or 2000. Each sequence consists of 10 runs with the different random seeds: 1, 2, \(\ldots,\) or 10. Each run continues for 300 s. Every second, the number of tracks in the global SIAP are collected. The result of a run with a random seed is \(N_{AVG}^{seed}\), which stands for the average number of tracks in the global SIAP. Finally, the result of a sequence is the average of \(N_{AVG}^{1\sim 10}\).  
Figure 4a presents the average number of tracks in the global SIAP. The performance of the NU-FTG is similar to the performance of HEED and FTG when the number of deployed targets are less  
Figure 3: The definition of the surveillance area for evaluation: (**a**) surveillance area consisting of 9 \(\times\) 9 cells; and (**b**) surveillance area is divided into four regions.  
than 1200. However, the NU-FTG performs better than HEED and FTG when the number of deployed targets are 1200 or more.  
The HEED was designed to distribute the traffic of nodes across cluster heads (local fusion nodes) by balancing their number of children. In these experiments, targets and radars are uniformly distributed, and the expected number of tracks of each radar is identical. As a result, the HEED can generate a balanced fusion tree. Thus, the performance of the HEED is similar to the performance of the FTG, and the NU-FTG when the number of deployed targets is less than 1200.  
The performance of the HEED and FTG become worse than the performance of NU-FTG as the number of deployed targets increases. The performance degradation of the HEED and FTG might be induced by the fixed number of local fusion nodes. Figure 4b shows that the number of local fusion nodes of the HEED and FTG are fixed. Thus, the performance of a fusion process is limited by the fixed number of local fusion nodes when the HEED or FTG is applied. On the other hand, the number of local fusion nodes of the NU-FTG increases as the number of deployed targets increases.
## 2 Materials and Methods
### Statistical Analysis  
Phytoplankton responses in terms of size and taxonomic composition to HA addition were analyzed by repeated measures analysis of variance (RM-ANOVA) using the statisticalanalysis program R software (ver. 6). Phytoplankton cell count data were transformed using log(x + 1) before statistical analysis.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 4 IBC Time Analysis Method
### Correlation Coefficient  
The correlation coefficient, a statistical metric, quantifies the strength and direction of a two-variable linear relationship. It returns a numerical value indicating how closely the variables' data points align with a straight line. The correlation coefficient is calculated using the variable means and total squared deviations. It goes from \(-1.00\) to \(+1.00\), with the sign indicating the direction of the link and the absolute value indicating the intensity of the relationship. Positive correlations suggest that changes in one variable cause changes in the other, whereas negative correlations indicate an opposite link.  
Equation (3) gives the equation for obtaining the correlation coefficient between two variables, x and y:  
\[\mathrm{r}=\frac{\sum[(\mathrm{x_{i}}-\mathrm{x})(\mathrm{y_{i}}-\mathrm{y})] }{\sqrt{\sum(\mathrm{x_{i}}-\mathrm{x})^{2}*\sum(\mathrm{y_{i}}-\mathrm{y})^{ 2}}} \tag{3}\]If \(x_{i}\) and \(y_{i}\) are individual data points of \(x\) and \(y\), \(\overline{x}\) and \(\overline{y}\) are their respective means. Total squared deviations \(\sum(x_{i}-x)^{2}\) are calculated by comparing the distances between each data point and the variable's mean.  
Correlation coefficients between \(\pm 0.4\) and \(\pm 0.7\) are considered moderately correlated. It is important to note that correlation coefficients only measure linear links and may not capture all forms of relationships or demonstrate causality between variables. The correlation coefficient is widely used in many domains, including economics, social sciences, and finance, to investigate and comprehend the links between diverse variables and their impact on one another.