esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Optimized Energy Scheduling of Rural Microgrid
## 3 Rural Microgrid Modeling
### Load Unit Modeling
#### 3.3.2 Model of Agricultural Loads  
It is assumed that the agricultural loads in rural microgrid is mainly composed of greenhouses. In a typical greenhouse, energy consumption is related to temperature, humidity, light and carbon dioxide (CO\({}_{2}\)) concentration [29; 30]. The greenhouse model is shown in Figure 3. The electrical equipment used to control the above four indicators includes the temperature control system (heaters and coolers), irrigation system, lighting system, and CO\({}_{2}\) generators.  
The total electricity consumption of the greenhouse is the sum of the electricity consumption of all electrical equipment. The calculation formula is as follows:  
\[E_{qgri}=\Delta h\sum_{h\ =\ 1}^{H}\ (P_{tem}(h)\ +\ P_{lig}(h)\ +\ P_{irri}(h)\ +\ P_{carb}(h)), \tag{14}\]  
Among them, the temperature and light intensity in the greenhouse are greatly affected by the external environmental. Therefore, there is a close relationship between environmental factors and greenhouse power consumption. Finding the factors affecting agricultural power consumption and analyzing the relationship between load characteristics and power consumption is an important step to achieving optimal scheduling.
# [PERSON] SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks
## 4 Theoretical Framework
### The Proposed Methodology  
In this paper, we propose an anomaly-based IDS for the identification of attack messages on an in-vehicle CAN bus. Firstly, we implemented a supervised XYF Kohonen approach to classify attack and normal messages sent on the CAN bus, obtaining good performance in detecting attack messages. The XYF Kohonen network is a power tool for intrusion detection, but it achieves its best performance when combined with other approaches, in particular, clustering algorithms [18]. The K-means algorithm is a clustering algorithm [50], which helps to achieve local clustering and shows high performance in dealing with big datasets. Previous works show how the SOM network coupled with the K-means clustering algorithm achieves better local optimal clustering results, improving the detection rate [51]. Thus, secondly, we implemented a hybrid approach combining the supervised XYF Kohonen network with the K-means clustering algorithm in order to improve the performance of the model in correctly identifying the attack messages injected on the CAN bus. In this approach, the clustering problem is not solved by the XYF network that only calculates the codebook vectors assigned to each neuron. The codebook vectors are, then, used as an input of the K-means algorithmfor local clustering in order to achieve clustering results. This approach enables solving a lack of the XYF network in correctly classifying some observations, improving the detection accuracy and reducing the false negative rate. This measure has great importance in attack detection for in-vehicle CAN buses since undetected attacks can undermine vehicle safety.
## 6 Results
### Latency  
The latency experienced by a client on retrieving information from a server is one of the most important parameters used to evaluate the goodness of the server implementation. Low latency values can significantly enhance user experience and benefit those applications that work in real-time. We define latency as the time elapsed from the moment the client sends a request until the moment it receives the response.  
Figure 6 shows the latency for each tested solution. Each point on the graph represents the average latency value of 100 successful request/response transactions. Payload size ranges from 10 to 1,200 bytes with increments of 50 bytes. The client sends a new request after receiving a response to the request previously sent. Figure 7 shows only a portion of the latency trend, excluding that obtained in HTTP/TCP. In this way, the differences between the other implementations can be better appreciated.  
As expected, HTTP/TCP has the worst performance, the main reason for which is found in the latency introduced by the three-way handshake used by TCP to establish and close the connection.  
Figure 6: Latency evolution according to the payload size. The performance of HTTP increases significantly if a TCP persistent connection is used.  
However, the negative impact of the handshake can be reduced using the same TCP connection for various HTTP transactions. The performance obtained by HTTP persistent confirms this aspect. As may be seen in Figure 6, a server that uses HTTP persistent is able to lower the latency and to make its trend closer to those of the fastest solutions.  
The lowest latency trend is obtained by the HTTP/UDP solution. This solution implements a bare HTTP server able only to reply to GET requests by querying for the provided resource. HTTP/UDP does not implement any reliability mechanism or HTTP logic. Therefore, it should be considered as a lower bound for latency.  
TinyCoAP has a latency trend very close to that of HTTP/UDP, so the substantial growth of complexity of TinyCoAP is not reflected in a particular worsening of latency. TinyCoAP outperforms CoapBlip in terms of latency. The main reason for this is found in the enhanced management of RAM memory implemented in TinyCoAP. The memory allocation used in CoapBlip causes a growth of the packet processing time and limits the maximum payload size that CoapBlip is able to send to 650 bytes. Applications that use data aggregation or work with high payload sizes cannot be used either in CoapBlip or with HTTP solutions using TCP. Concerning HTTP, the maximum allowed payload size is limited by the use of the TCP buffers that exhaust the RAM memory.
# A Novel Optimization Layout Method for Clamps in a Pipeline System
## 2 Materials and Methods
### Process of the Optimization
#### 2.1.1 Global Sensitivity Analysis Based on Sobol Method  
Sensitivity analysis is used to study the influence of each layout parameter on the output, which provides a guide for the system design. Currently, the most commonly used method of global sensitivity analysis is the Sobol method [22]. The flow chart of global sensitivity analysis is shown in Figure 2.  
This method is based on the variance-based Monte Carlo method that defines a k-dimensional unit body as the spatial domain of the input parameters, which expressed as:  
\[\Omega^{k}\ =\ \{x|0\leq x_{i}\leq 1;\ i\ =\ 1,2,\cdots,k\} \tag{1}\]  
Figure 1: Flow chart of optimization algorithm.  
Figure 2: Flow chart of global sensitivity analysis.  
The main idea of the Sobol method is to decompose the function into the sum of the subitems.  
\[f(x_{1},x_{2},\cdots,x_{k})\ =\ f_{0}+\sum_{i\ =\ 1}^{k}f_{i}(x_{i})+\sum_{1\leq i<j \leq k}f_{ij}(x_{i},x_{j})+\cdots+f_{1,2,^{-},k}(x_{1},x_{2},\cdots,x_{k}), \tag{2}\]  
The function is decomposed to \(2^{k}\) subitems. Sobol uses a decomposition method based on multiple integrals. The total variance of the output \(f(x)\) is:  
\[D\ =\ \int_{\Omega^{k}}f^{2}(x)dx-f_{0}^{2}. \tag{3}\]  
The variance of each sub-item in Equation (2) is called the partial variance of each order, that is, s-order partial variance.  
\[D_{i_{1},i_{2},\cdots i_{s}}\ =\ \int_{0}^{1}\cdots\int_{0}^{1}f_{i_{1},i_{2}, \cdots i_{s}}^{2}(x_{i_{1}},x_{i_{2},\cdots}x_{i_{s}})dx_{i_{1}}dx_{i_{2}, \cdots}dx_{i_{s}}(1\leq i_{1}<i_{s}\leq k). \tag{4}\]  
Integrate Equation (2) over the entire range, and the relationship between the total variance and the partial variance of each order can be obtained in Equation (5):  
\[D\ =\ \sum_{I\ =\ 1}^{K}D_{i}+\sum_{1\leq i<j\leq k}D_{ij}+\cdots+D_{1,2, ^{-}k}. \tag{5}\]  
Define the sensitivity of each order as the ratio of the each-order partial variance to the total variance. The sensitivity of the S order, \(S_{i_{1},i_{2},\cdots i_{s}}\), could be defined as:  
\[S_{i_{1},i_{2},\cdots i_{s}}\ =\ \frac{D_{i_{1},i_{2},\cdots i_{s}}}{D}\ (1\leq i_{1}<\cdots<i_{s}\leq k). \tag{6}\]  
When calculating the sensitivity of the input parameters, the normalization algorithm is used to normalize the position of the clamps. Sobol sampling is used to obtain the sampling points that need to be calculated, and the corresponding output is obtained by finite element analysis. The computational analysis could obtain global sensitivity coefficients for different parameters.
# A Method of Supplementing Reviews to Less-Known Tourist Spots Using Geotagged Tweets
## 3 Proposed Method
### Feature Selection  
Many tourism-related tweets on Twitter do not contain on-spot information. One of the solutions to extract on-spot tweets is by classifying them as such by using a machine learning-based classifier. In collecting tourists' tweets, it is necessary to determine the conditions of considering which tweets are tourists' tweets. Therefore, we introduce a set of tweets classification features to be used for the automatic classification as follows:  
Tweet location: We observed that tweets tweeted within the radius of the target spot's boundaries (latitude and longitude) introduced in the previous section which was acquired using Google's Geocoding API ([[https://developers.google.com/maps/documentation/geocoding/overview](https://developers.google.com/maps/documentation/geocoding/overview)]([https://developers.google.com/maps/documentation/geocoding/overview](https://developers.google.com/maps/documentation/geocoding/overview)), accessed on 15 January 2022) often had a high chance of becoming a valuable on-spot review.  
Presence of \"NOW\": The word \"now\" is a characteristic keyword on Twitter. Although the presence of the word does not always indicate on-spot information, it is considered to suggest a high probability of the tweet containing on-spot information. We, therefore, retain tweets with this word.  
Presence of a mention \"@ Target spot\": In many cases, tourists' tweets about places they are sightseeing are accompanied with images the users attach to tweets by using mobile camera functions. At that time, expressions such as \"@ Serengeti national park\" frequently indicate places visited after \"@\".  
Bag of Words (BOW): All words from the whole corpus with the term frequency for the BOW language model, which contains 1273 sentences.
## 4 Range-Based Algorithms
### Least Squares (LS)  
Least squares (LS) is a well-known method used to estimate an unknown. It can be formulated by finding a solution that minimises the square of the error, such that:  
\[\operatorname*{arg\,min}_{X}\sum_{i=1}^{N}\Bigl{(}RSS_{i}-\Bigl{(}P _{0i}^{i}-10\alpha\log_{10}(||X-S_{i}||)\Bigr{)}\Bigr{)}^{2}\,, \tag{5}\] \[\operatorname*{arg\,min}_{X}\sum_{i=1}^{N}(TOA_{i}c-||X-S_{i}||) ^{2}\,,\] (6) \[\operatorname*{arg\,min}_{X}\sum_{i=2}^{N}(TDOA_{i}c-||X-S_{i}|| +||X-S_{1}||)^{2}\,,\] (7) \[\operatorname*{arg\,min}_{X}\sum_{i=1}^{N}\Bigl{(}OA_{i}-\arctan \bigl{(}\frac{X_{y}-S_{yi}}{X_{x}-S_{xi}}\bigr{)}^{2}\,, \tag{8}\]  
where \(c\) is the speed of the light, \(RSS_{i}\), \(TOA_{i}\), \(TDOA_{i}\), and \(AOA_{i}\) are the measurements with respect to the ith BS. For the cooperative case where there are \(M\) targets to be localised, it can be written as:  
\[\operatorname*{arg\,min}_{X}\sum_{j=1}^{N}\sum_{i=1}^{M}\bigl{(}RSS _{ij}-\bigl{(}P_{0i}-10\alpha\log_{10}(||X_{j}-S_{i}||)\bigr{)}\bigr{)}^{2}\,, \tag{9}\] \[\operatorname*{arg\,min}_{X}\sum_{j=1}^{N}\sum_{i=1}^{M}\bigl{(} TOA_{ij}c-||X_{j}-S_{i}||\bigr{)}^{2}\,,\] (10) \[\operatorname*{arg\,min}_{X}\sum_{j=1}^{N}\sum_{i=1}^{M}\bigl{(} TDOA_{ij}c-||X_{j}-S_{i}||+||X_{j}-S_{1}||\bigr{)}^{2}\,,\] (11) \[\operatorname*{arg\,min}_{X}\sum_{j=1}^{N}\sum_{i=1}^{M}\biggl{(} AOA_{ij}-\arctan\bigl{(}\frac{X_{yi}-S_{yi}}{X_{xi}-S_{xi}}\bigr{)}^{2}\,. \tag{12}\]  
A straightforward solution to cope with the highly non-linearity is linearisation, such as Taylor expansion [PERSON] (1954). However, the recursive LS method offers more fidelity and accuracy in the cost of complexity and computation burden [PERSON] (1954). The Weighted Least Squares (WLS) method is more efficient, which puts different weights on the measurement. In one intuitive method, weights are selected based on the distance. The optimal method is to exploit the covariance of the measurement noise for WLS. When such information is not at hand for noise, there are some efforts to estimate it [PERSON] (1954).  
In addition to position, other unknown parameters can also be jointly estimated, such as unknown PLE [PERSON] (1954) and transmission power (for RSS-based localisation) [PERSON] (1954). Unfortunately, [PERSON] (1954) also considers the unknown weight matrix and Non-line-of-sight (NLOS) impact by adding a random variable to the propagation model, which adds to the complexity of the problem. To deal with this issue, the semi-definite relaxation (SDR) technique is used as an approximation, transforming the problem into convex semi-definite programming.  
In a more efficient framework, the relative error is employed instead of the estimation error, which is called Least Squares Relative Error (LSRE) [PERSON] (1954); [PERSON] (1954). The relative error is the ratio of the absolute error to the measured value. In LS, all observations are treated equally, implying the same precision for all data, which is critical in reality.
## 2 Related Work
### Dimension Reduction in General Settings  
Dimension reduction on an original rating matrix is usually used to solve the sparsity problem. The underlying reason for dimension reduction is that the factors that govern user choices are less significant than the dimension of the rating data.  
The dimension reduction methods used in model-based recommendations mainly consist of three types [2]: the probabilistic methods [3], the matrix factorization methods [4; 5], and the clustering method [6; 7].  
The first type of method assumes the presence of some latent variables, to which both the users and the items belong. The objective of this type of method is to learn the probability-based similarity between users and items that belong to the same, or a similar, latent variable. A typical representative of this type of method is the PLSA model [8]. An example work is found in [9], which utilizes probabilistic methods for collaborative filtering recommendations. The benefit of adopting a probabilistic framework is that all items and users can be explicitly and simultaneously represented within the framework.  
The second type of method, i.e., the matrix factorization method, mainly derives a matrix with restricted ranks from the original matrix, is often considered as a dimension reduction technique and is widely applied in collaborative filtering problems [10]. One typical example of this type of method is the Non-negative Matrix Factorization (NMF) [11].  
Another example model in this type of method is the Singular Value Decomposition (SVD). In [12], SVD was used to reduce dimensions to improve performance for recommender systems. In [13], SVD was used to reduce dimensions and find the most similar users and items in each cluster. The work in [14] is another example, which applies Latent Semantic Indexing (LSI) to construct a user profile based on both collaborative and content features.  
The third type of method, i.e., the clustering-based method, adopts clustering methods in collaborative filtering. An example is the work in [15], where the authors adopt a clustering procedure on the user profile in the collaborative filtering implementation.  
Another example is the work in [16], which uses clustering techniques for dimension reduction, where the k-means algorithm and the SVD method are combined to cluster similar users.
# Design Procedure of a Frequency Reconfigurable Metasurface Antenna at mmWave Band
## 5 Results and Analysis
### Gain and Directivity  
According to [51], directivity is a property of RF systems which determines how concentrated the radiation emitted is in one direction. It is a factor affecting the gain in these systems. The gain describes how well the proposed structure converts input power into radio waves, which travel in a particular direction [51]. Despite the fact that the gain of the metasurface antenna is closely related to its directivity, it is also a measure of its efficiency and directional capabilities [51]. The simulated results in Figure 9 depict the directivity and gain results before and after the lumped elements were installed on the 16 element metasurface antenna. Table 4 shows a numerical summary of the figure.  
Figure 9: Simulation results of a 16 element metasurface array with and without PIN diodes indicating (**a**) Directivity (**b**) Gain.  
From Table 4, the gain and directivity of the reconfigurable 16 elements metasurface antenna increased when diodes were added. This is due to the decreased angular beamwidth, which led to a narrower beam, resulting in higher directivity and gain [64]. However, since the proposed SRR unit cells are made of copper, when the current is applied, it moves towards the surface of the conducting material, decreasing its effective cross section. This tendency is known as the skin effect and it is the reason attenuation increases with frequency because of high loss resistance. This can result in decreased gain and should be taken into consideration during the design and implementation processes. Furthermore, the directivity and gain were used to compute the radiation efficiency using Equation (15) [51, 65]  
\[\text{Radiation efficiency}=\frac{\text{Gain (dBi)}}{\text{Directivity (dBi)}}\times 100\%. \tag{15}\]  
Since PIN diodes are connected to metallic strips of the metasurface antenna array, loss can occur due to the dissipation of power, which affects the radiation efficiency. It was observed from Table 4 that the radiation efficiency of the metasurface antenna array decreased when diodes were added.  
According to [66], the resistance of the diode does not affect the operating frequency. As a result, capacitance and inductance of the switches have no effect on the gain nor directivity. From Table 4, the directivity, gain, and radiation efficiency with varying capacitances was relatively the same.
## 4 Simulation and Analysis of Magnetic Field Distribution
### Magnetic Field Distribution for Offset Coils  
#### 4.2.1 Current of Offset Coils  
According to the analysis of _LCC_ compensation topology, the primary current is only related to the input voltage. When the two resonant coils are not aligned, magnitude of the primary current does not change. However, the ability of secondary coil to receive a magnetic field changes due to the offset, so the current of secondary coil also changes. Current of the secondary coil can be expressed by the following formula:  
\[\dot{I}_{2}=\frac{U_{ab}}{2\pi fL_{72e}}\angle 0^{\circ}, \tag{19}\]  
\[\dot{I}_{L/2}=\frac{k\sqrt{L_{1}L_{2}}U_{AB}}{j\omega_{0}L_{71e}L_{72e}} \angle-90^{\circ}. \tag{20}\]  
Impedance of the rectifier and battery in the secondary circuit is equivalent to \(R_{eq}\), and the output voltage \(U_{ab}\) can be expressed by the formula:  
\[\dot{U}_{ab}=\dot{I}_{L/2}\cdot R_{eq}=\frac{k\sqrt{L_{1}L_{2}}}{j\omega_{0} L_{71e}L_{72e}}\cdot R_{eq}\angle-90^{\circ}. \tag{21}\]  
Thus, the current of secondary coil can be expressed:  
\[\dot{I}_{2}=\frac{k\sqrt{L_{1}L_{2}}}{\omega_{0}{}^{2}L_{71e}L_{72e}}\cdot R_ {eq}\angle-90^{\circ}. \tag{22}\]  
It can be known that \(R_{eq}\), \(L_{71e}\), \(L_{72e}\), and \(\omega_{0}\) are constant, so the current \(I_{2}\) is positively correlated with the coupling coefficient \(k\), the primary inductance \(L_{1}\), and the secondary inductance \(L_{2}\).  
Currents of primary and secondary coils can be obtained by Simplorer, shown in Figure 13, where TI is the current of primary coil and RI is the secondary current. It can be seen that the primary current lags the secondary current by 90 degrees. When the coils are offset, the primary coil current is almost constant, and the secondary coil current is gradually reduced.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Point** & **Coordinates (mm)** & **Magnetic Field Strength (A/m)** \\ \hline A1 & (0,0,75) & 1223 \\ A2 & (0,0,210) & 13.07 \\ A3 & (0,740,75) & 14.15 \\ A4 & (0,840,700) & 3.22 \\ B1 & (550,0,1200) & 0.21 \\ B2 & (550,0,950) & 0.15 \\ B3 & (360,0,670) & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Position and magnetic field strength of test points.
## 4 Coded Cooperation with Soft Information Relaying and Relay Selection
To justify the adequacy of using the RGL model in a coded cooperative scheme with SIR strategy, Section 5 provides a comparison between the outage performance of the CC-SIR-RS scheme deploying the RGL model and that deploying the following models:  
* The Gaussian LLR (GL) model [9]: The output LLRs at the output of the relay soft encoder are modeled by the following expression: \[L(c_{R,t}|L(\hat{\mathbf{u}}))=\frac{2}{\sigma_{n}^{2}}(x_{t}+n_{t}^{GL}),\quad 1 \leq t\leq N_{R},\] (23) where \(n^{GL}\) is assumed to be a Gaussian distributed noise with zero mean and variance \[\sigma_{n^{GL}}^{2}=\frac{2}{E_{2}}(1+\sqrt{1+E_{2}}),\] (24)  
Figure 2: (a) Histogram of a relay soft encoder output LLRs for \(E_{b}/N_{0}=10\) dB, (b) normal Q-Q plot of the distribution (green line) and \(y=x\) line (dashed line). where, similar to the RGL model, \(E_{2}\) is the second order moment of the LLR sequence at the output of the relay soft encoder that is scaled with a block normalization factor \(\beta^{GL}=1/\sqrt{E\{L(c_{R})^{2}\}}\) before being forwarded to the destination node. * The soft noise (SN) model [8]: The LLRs at the output of the relay soft encoder are entered into a \(\text{tangh}(LLR/2)\) operator and the so-called resulting soft bits \(\tilde{x}\) are modeled by \[\tilde{x}_{t}=x_{t}(1-n_{t}^{SN}),\quad 1\leq t\leq N_{R},\] (25) where \(n^{SN}\) is a Gaussian distributed noise with mean \(\mu_{n^{SN}}=E\{1-x\tilde{x}\}\) and variance \(\sigma_{n^{SN}}^{2}=E\{1-x\tilde{x}-\mu_{n^{SN}}\}\); \(\mu_{n^{SN}}\) and \(\sigma_{n^{SN}}^{2}\) are calculated offline for each relevant SNR [8]. The soft bits are scaled with a block normalization factor \(\beta^{SN}=1/\sqrt{(1-\mu_{n^{SN}})^{2}+\sigma_{n^{SN}}^{2}}\) before being forwarded to the destination node. * The soft scalar (SS) model [10]: The model is also based on soft bits as in the SN model. The soft bits are modeled by \[\tilde{x}_{t}=h^{SS}x_{t}+n_{t}^{SS},\quad 1\leq t\leq N_{R},\] (26) where \(h^{SS}\) is the soft scalar, which is viewed as an equivalent fading coefficient, and \(n^{SS}\) is the soft error. The soft scalar is computed by \(h^{SS}=(1/N_{R})\sum_{t=1}^{N_{R}}[x_{t}\tilde{x}_{t}]\), and the variance of the soft error is computed by \(\sigma_{n^{SS}}^{2}=\sigma_{\tilde{x}}^{2}-h^{SS^{2}}\), where \(\sigma_{\tilde{x}}^{2}\) is the variance of \(\tilde{x}\). The soft scalar and the variance of the soft error are computed offline for each relevant SNR [10]. The soft bits are scaled with a block normalization factor \(\beta^{SS}=1/\sqrt{h^{SS^{2}}+\sigma_{n^{SS}}^{2}}\) before being forwarded to the destination node. In the remainder of this paper, the SIR strategy deployed in the coded cooperative scheme refers to the RGL model given in (19) with resolved parameter given in (20) in and block normalization factor given in (21).