esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Long-Range Propagation Analysis of Air-to-Air Airborne Radars
### Target Detection Performance in Abnormal Atmosphere
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline \(P_{t}\) & \(10^{6}\) W & \(T_{0}\) & 290 K \\ \(\sigma\) & \(15\) m\({}^{2}\) & D & 0.04–17.9 \\ \(\tau\) & \(2\times 10^{-6}\) s & \(P_{d}\) & 0.01–0.99 \\ \(P_{th}\) & \(10^{-8}\) & \(f\) & \(10\times 10^{9}\) Hz \\ \(\tilde{G}\) & \(10^{3}\) & \(N_{f}\) & \(10^{0.5}\) \\ \(k\) & \(1.38\times 10^{-23}\) & \(L_{s}\) & \(10^{0.3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed airborne radar parameters and values. associated with the normal atmosphere. \(F_{atm}\) and \(F_{nor}\) indicate the contour lines for the detectable areas under the abnormal and normal atmosphere conditions, respectively. To examine the effects of the abnormal atmosphere, the elevated duct height and thickness are varied to provide a DPMA map for the air-to-air radar propagation when the airborne radar is located at heights of 3 km and 5 km, as shown in Figure 9. Due to the elevated duct, low DPMA levels are obtained, particularly near the airborne radar (3 km and 5 km). According to the DPMA map, the minimum values are 0.65 at an \(h_{1}\) of 3 km and 0.69 at an \(h_{1}\) of 5 km, respectively. To verify the feasibility of using the DPMA map, two representative elevated duct conditions, namely Case 1 (Jeju; 5 October 2017) and Case 2 (Pohang; 5 October 2017), are analyzed in more detail. Figure 10a,c presents the refractivity of the duct conditions for the two cases using actual refractivity data with the tri-and quad-linear models. Red solid, blue dashed, green dotted, and black dash-dotted lines specify the actual refractivity, quad-linear model, tri-linear model, and the normal atmosphere, respectively. The duct height and thickness are 4.79 km and 495 m for Case 1 as well as 7.37 km and 715 m for Case 2. Figure 10b,d shows the DPMA results according to the airborne radar height for both cases. In the simulation results, the lowest DPMA values of 0.87 and 0.93 are obtained for Case 1 and Case 2, when the radar height \(h_{s}\) is close to the elevated duct height \(h_{1}\). In addition, the DPMA of the quad-linear model well follows that of the actual data, while the DPMA upper \(h_{1}\) of the tri-linear model has a great difference with that of the actual data. Figure 8: Detectable area according to the target detection probability: (**a**) normal atmosphere; (**b**) abnormal atmosphere. Figure 9: Detection performance metric in terms of the atmosphere (DPMA) map in accordance with the duct height and thickness: (**a**) \(h_{s}=3\) km; (**b**) \(h_{s}=5\) km.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 3 Determination of Embedded HVDC Operation Point
### System Total-Loss Minimization (STLM) Control
#### 3.1.1 System Total-Loss Minimization  
The most important factor that affects the determination of the operating point is the minimization of the total loss of the system. The most important advantage of VSC HVDC is independent control of active power and reactive power, which can be used to change the direction of the system's power flow, leading to a change in system losses. Therefore, in this study, we decided on the operating point that minimizes the system's total loss within the capacity of HVDC. The system's total loss means the sum of the existing AC loss and HVDC loss.  
\[\begin{array}{ll}P_{loss,total}&=\sum\limits_{n_{l}}P_{loss,AC}+\sum \limits_{n_{l,DC}}(P_{loss,DC}+P_{loss,comp})\\ &=\sum\limits_{n_{l}}\frac{P_{r,AC}^{2}+Q_{r}^{2}}{V_{r,AC}^{2}},R_{AC}+\sum \limits_{n_{l,DC}}\left(\frac{P_{r,DC}^{2}}{V_{r,DC}^{2}},R_{DC}+(P_{r,DC}+P_{ s,DC}),\lambda\right)\end{array} \tag{2}\]  
in which  
\(P_{loss,total}\) is the system total loss;  
\(P_{loss,AC}\) is the \(AC\) system loss;  
\(P_{loss,DC}\) is the HVDC line loss;  
\(P_{loss,comp}\) is the HVDC conversion loss;  
\(P_{r,AC}\), \(P_{r,DC}\) are the \(AC\)/\(DC\) line flow active powers at the receiving end;  
\(P_{s,DC}\) is the \(DC\) line flow active powers at the sending end;  
\(Q_{r}\) is the \(AC\) line flow reactive powers at the receiving end;  
\(V_{r,AC}\), \(V_{r,DC}\) are the \(AC\)/\(DC\) voltage at the receiving end;  
\(R_{AC}\), \(R_{DC}\) are the resistance values of the \(AC\)/\(DC\) line; and  
\(\lambda\) is the loss rate of the converter.  
The VSC \(DC\) losses have been decreasing over the years because of the application of Modular Multi-Level Converter (MMC) topology and optimization of switching logic. For example, the HVDC VSC loss of the fourth-generation technology is now comparable with the HVDC LCC (\"Classic\") technology [25; 26]. In this paper, conversion loss is considered to be 0.9% of the active power setpoint per converter, but the value of the technology can be lowered according to development.  
In addition, since BTB HVDC has no line loss, the DC related losses did not include the DC line loss, but only the conversion loss of the converter, which has a big influence on the loss reduction of the entire system. Therefore, Equation (2) is simplified as in Equation (3), and from the viewpoint of loss reduction, the conversion loss of BTB HVDC can greatly influence the numerical value of the entire system. At this time, since the converter and the inverter are at the same position, it is assumed that \(P_{r,DC}=P_{s,DC}\).  
\[\text{Minimize }P_{loss,total}=\sum\limits_{n_{l}}\frac{P_{r,AC}^{2}+Q_{r}^{2}}{V_{r,AC}^{2}},R_{AC}+\sum\limits_{n_{l,DC}}2,P_{r,DC},\lambda. \tag{3}\]
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 6 Experimental Results of Decoding by Proposed Multi-class NN-Based Decoder
### Experimental Results
#### 6.2.2 Results for BCH (15, 5)  
The code length of BCH (15, 5) code is \(n=15\), the message length is \(k=5\), the error correcting capability is \(t=3\), and its code rate is \(5/15=0.33\). Figure 12a,b shows that the proposed multi-class NN-based approach has almost the same BER and CER performance as the OSD method. For evaluating the maximum likelihood metric, the proposed method, Chase-II and OSD algorithms require 4, 21 and 121 candidate codewords, respectively. The decoding complexity is thus reduced over the conventional methods. It is also observed that  
Figure 11: (**a**) BER for BCH (15, 7), (**b**) CER for BCH (15, 7) Code.  
Figure 10: Simulation Procedure.  
the presented NN-based decoder is superior to DNN-based decoder [13] and denoiser [14] in terms of CER and BER.
# Development Characteristics and Mechanism of Crack in Expansive Soil under Wet-Dry Cycling
###### Abstract  
Investigating crack evolution characteristics in expansive soil under dry-wet cycle conditions is essential for analyzing the shallow instability of embankments and roadbeds filled with this type of soil. Indoor tests on remodled expansive soil specimens were performed under dry-wet cycle conditions, and digital image processing technology (PCAS) was used to quantitatively analyze the process of crack evolution. The study examined how initial moisture content, compaction degree, thickness, and expansibility affect crack development in expansive soil. Additionally, the study discussed the mechanism of crack evolution, considering the characteristics of crack structures in expansive soil. The findings suggest that crack development in expansive soil specimens is influenced by various factors including initial moisture content, compaction degree, thickness, and expansibility. The crack ratio increases with the initial moisture content, thickness, and expansibility, but decreases with the compaction degree. Furthermore, a significant linear relationship exists between the attenuation rate of soil strength and the crack ratio. The development of cracks is governed by the variance in soil shrinkage, which leads to the formation of distinct shrinkage centers at various locations and is markedly influenced by the soil's thickness. This research explores the laws governing the evolution of cracks in expansive soils, elucidating the mechanisms by which these cracks evolve under the influence of various factors. It addresses a significant gap in the theoretical understanding of crack evolution in expansive soils under conditions of multiple influences, thereby offering crucial insights into the characteristics of soil evolution.  
wef-dry cycling; crack evolution; soil strength attenuation; laboratory experimentation; expansive soil +
Footnote †: journal: Applied sciences  
0000-0001-5649-9999-4000][PERSON]  
3 Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 20240, China  
4 Guangai Transportation Science and Technology Group Co., Ltd., Nanning 530029, China
## 5 Experimental Results
### The Compared Experimental Results
#### 5.4.1 Comparisons on Solution Accuracy
CS, WWO, and SMS do not obtain any best result except for the function F8. With regard to the function F8, there is no big difference about all eight algorithms. The PTBO algorithm performs well for the functions F11, F13, F14, F17, F18, and F20, and according to the data of the last row in Table 9, it can be concluded that the PTBO algorithm has good performance in solution accuracy for multimodal benchmark functions. **(3) The accuracy results of composition functions**  
It can be seen from Table 10 that DE acquires the best results on F25, and WWO obtains the best results on F26. However, PTBO obtains the best results on F21, F22, F23, F24, F27, and F28. In general, PTBO shows the best overall performance from the statistical results according to the data of the last row in Table 10.  
**(4) The total results of solution accuracy of the 28 functions**  
A summary of the total number of ',',', and'about the solution accuracy of 28 functions is given in Table 11. From Table 11, it can be observed that PTBO has the best performance of the nine algorithms.
## 4 Case Study: Embraer E2 Model Aircraft Flag Track  
To demonstrate the technical feasibility and potential of the presented approach, the authors applied it to a real-world case involving structural testing of an Embraer E2 aircraft component. The test monitored various parameters under changing mechanical loads. This proof-of-concept integrated sensing, communication, processing, visualization, and alerting across edge, fog, and cloud layers. Results from the IoT-based system were compared to traditional wired sensors, showing comparable accuracy, speed and reliability, validating the proposed approach. An overview of the AIoT architecture implementation is shown in Figure 3.
## 2 Parallelisation of DEM
### DEM Algorithm
#### 2.1.2 Update Kinematic State of Particles  
The kinematic state of each particle, i.e., translational and rotational motions, is determined by the resultant force and moment from the surrounding particles. The translational motion of a particle is described by the position \(X_{i}\), velocity \(V_{i}\), and acceleration \(a_{i}\). The rotational motion is expressed as angular velocity \(\omega_{i}\), and angular acceleration \(\dot{\omega}_{i}\). The translational and angular accelerations can be calculated by:  
\[a_{i}=\sum F_{ij}/m_{i}+g-\alpha\left|\sum F_{ij}+g\right|\text{sign}(V_{i})/ m_{i} \tag{13}\]  
\[\dot{\omega}_{i}=\sum M_{ij}/I_{i}-\alpha\left|\sum M_{ij}\right|\text{sign}( \omega_{i})/I_{i} \tag{14}\]  
where \(\Sigma\) represents a summation over all neighbour particles that needs a sophisticated contact detection algorithm and will be detailed in the sequel; the second term is to consider the local damping effect with coefficient \(\alpha\)[1]; \(g\) is the gravitational acceleration. Then the velocities can be updated as:  
\[V^{\text{new}}_{i}=V_{i}+a_{i}\Delta t \tag{15}\]  
\[\omega^{\text{new}}_{i}=\omega_{i}+\dot{\omega}_{i}\Delta t \tag{16}\]  
The timestep \(\Delta t\) for a linear contact model can be determined by:  
\[\Delta t=\beta\sqrt{m_{\text{min}}/K^{\text{n}}} \tag{17}\]  
where \(\beta\) is a coefficient, \(m_{\text{min}}\) is the minimum mass of the particles, and the position of the particle is updated as:  
\[X^{\text{new}}_{i}=X_{i}+V_{i}\Delta t \tag{18}\]
## 2 Methods  
### Mathematical Model of the Minimum Zone Sphericity Error  
According to the definitions of the minimum zone spherical error in ASME and ISO, for a given set of sampled contour points, if all data points fall within two concentric spheres, the difference in the radii of these two spheres is referred to as the minimum zone spherical error. Therefore, when evaluating spherical error based on the minimum zone criterion, the central task is to find an ideal sphere center \(\mathbf{p}_{0}^{*}\) that minimizes the difference in radii between the two concentric spheres (where the radius difference represents the sphericity error, denoted as \(\mathbf{e}\)) [27; 28; 31; 32; 33]. This task is expressed by Equation (1).  
\[\mathbf{e}=\underset{\mathbf{P}_{0}\in Q}{\mathrm{min}}\bigg{\{}\underset{i\in[1,n]}{\mathrm{max}}\|\mathbf{p}_{i}-\mathbf{p}_{0}\|_{2}-\underset{i\in[1,n]}{ \mathrm{min}}\|\mathbf{p}_{i}-\mathbf{p}_{0}\|_{2}\bigg{\}} \tag{1}\]In the equation, \(\mathbf{e}\) represents the spherical error, where \(\mathbf{p}_{i}\in\mathbb{R}^{d}\), \(i=1,\,2,\,\ldots\); \(n\) denotes the measurement points in a \(d\)-dimensional space; and \(\mathbf{p}_{0}\) is the center of the concentric sphere. \(Q\) is a cubic region containing the globally optimal solution, which we call the global solution space.  
Figure 1 illustrates the spatial geometric structure of spherical error evaluation based on the minimum zone criterion and the various parameters involved. The construction of the global solution space \(Q\) requires the computation of the least squares sphere center and the least squares spherical error based on the least square criterion. Subsequently, a cube with a side length equal to the least squares spherical error is constructed around the least squares sphere center, allowing us to infer that the ideal sphere center must lie within this search region.
# Analysing Social Media Discourse on Electric Vehicles with Machine Learning
## 3 Materials and Methods
### Text Mining  
Text mining is an interdisciplinary research field that aims to extract meaningful information from large text data [35]. Text mining, which includes natural language processing (NLP), machine learning, and statistical methods, is a process of extracting insights from unstructured texts [36]. Today, it has a wide range of applications, from social media posts to academic articles, from emails to customer reviews. Especially in the era of big data, text mining has become a critical tool to support informed decision-making [37; 38].  
Text preprocessing, the first stage of text mining, aims to make the dataset suitable for analysis [39]. This stage consists of several steps to make the text processable and meaningful. First, the text is divided into words or sentences using tokenisation [40]. Then, stopwords such as \"and\", \"with\", and \"but\", which are frequently used depending on the structure of the language but contain low semantic information, are extracted. Another important step is lemmatisation and stemming techniques, whereby words are reduced to their roots to ensure unity of meaning between their affixed forms. Furthermore, the removal of non-essential elements such as punctuation marks, special characters, and numbers contributes to a more accurate processing of the data [41].  
Feature extraction aims to convert text data into a numerical format to make them suitable for analysis. In this context, there are several commonly used methods [42]. One of them is the Term Frequency-Inverse Document Frequency (TF-IDF) method, which is used to determine the importance of a word in a given text by evaluating the word inversely proportional to its prevalence in the whole document set [43]. Another method, the Bag of Words (BoW) model, is a representation method based on the frequencies of the words in the text and analyses them without considering the order of the words [44]. Word embeddings, which are among the more advanced meaning capture techniques, enable more in-depth analysis by better representing the semantic relationships of words with techniques such as Word2 Vec, GloVe, or FastText [45].
# On the Potential of the RST-FLARE Algorithm for Gas Flaring Characterization from Space
## 2 Data and Methods
### Rst-Flare
#### 2.3.2 Source Emissive Power Computation  
Unlike our previous study [20], in this case flaring sites are high temperature sources making the Fire Radiative Power (FRP) suffering for high uncertainties [26]. Thus, a new metric, named Brightness Temperature Excess (BTE), has been introduced to compute the emissive power of each flaring site. BTE is defined as the difference between the BT measured in the MODIS band 20 for the flaring site (BT20) on the image at hand and that of its surrounding historical background (BT20\({}_{bg}\)). The latter refers to the averaged value of the multi-temporal RST-based temporal mean (i.e., \(\mu_{20}(x_{y}y)\)) computed for a \(5\times 5\) pixel window centered on the potential flaring site.  
The BTE is computed for each flaring site only when a thermal anomaly has been identified by Equation (1). In order to better assess the impact of gas flaring in the area at national level, the BTE is assessed, at yearly temporal scale, for all pixels identified as flaring sites by the multitemporal scheme (regardless of their recurrence percentage) (Equation (2)). Moreover, to account for sampling and cloud coverage across years, the BTE value is corrected by a scaling factor \(\alpha\), depending on the number of useful images. The sum of such values for each investigated year provides a measure of the emissive  
Figure 4: Temporal trends of the recurrence percentages computed for two pixels, P1 and P2, selected within the ROI (see Figure 1).  
power of these sources, which is expected to be related to the amount of gas burned off by the plant in a specific year.  
In detail, BTE is computed using the following formula:  
\[BTE_{y}=\sum_{j=1}^{M}\Bigl{(}BT20_{j}-<BT20_{j,bg}>\Bigr{)}\cdot\alpha_{j,y}[K], \tag{2}\]  
where:  
* \(y\) refers to each of the investigated years in the 2000-2016 period;
* \(j\) refers to each of the M identified flaring site (only in presence of detected thermal anomaly);
* \(BT20_{j}\) is the brightness temperature (K) measured in the MODIS band 20 for the target \(j\) (i.e., the detected flaring site);
* \(<BT20_{j,bg}>\), referred to the background, is equal to the spatial mean of the temporal mean \(\mu_{20}(x,y)\) in a 5 \(\times\) 5 box around the site;
* \(\alpha\) is a coefficient which weights the variability, between years, in sampling and cloud cover. For each of the detected flaring sites within the single year, \(\alpha_{j,y}\) is computed as the ratio between the useful images (i.e., clear and with SatZA \(<\) 40\({}^{\circ}\) and SolZA \(<\) 101\({}^{\circ}\)) and the total ones. To take into account of \(\alpha_{j,y}\) variability in the considered period, it has been normalized with respect to its maximum value computed within the 17 years (\(\alpha_{y,max}=0.30\)).
## 3 Integrating Plausible Rules with Ontologies
### Translating from DL to Plausible Logic Programs  
This section exploits the work in [24] in order to translate description logic based on ontologies into plausible logic axioms. Facing the challenge to reason on huge amount of noise and heterogeneous data, the DL fragment corresponding to Horn clauses, known as Description Logic Programs [25], can be a suitable choice.  
Conjunctions and universal restrictions in the right hand side of inclusion axioms are converted into rule heads (\(T_{h}\) classes), whilst conjunction, disjunction and existential restriction appearing in the left-hand side are translated into rule bodies (\(T_{b}\) classes). Figure 1 presents the mapping function \(\mathcal{T}\) from DL to strict rules in a plausible knowledge base, where \(A\), \(C\) and \(D\) are concepts such that \(A,C\in T_{b}\), \(D\in T_{h}\), \(A\) is an atomic concept, \(X\), \(Y\) and \(Z\) are variables, and \(P\) and \(Q\) are roles.  
Figure 1: Mapping from DL ontologies into strict rules.  
If \(C\) is subsumed by \(D\), then each individual \(X\) in \(C\) is also an instance of the class \(D\). The axiom \(\top\sqsubseteq\forall P.D\) says that, with certainty, all the roles \(P\) point towards individuals of type \(D\). It is translated in second line of Figure 1 as: if there is a relation \(P\) between \(X\) and \(Y\), the element \(Y\) should be of type \(D\). Similar semantics is applied for the inverse role \(P^{-}\), where the domain and range are interchanged. If \(a\) is interpreted as a \(D\) concept, then \(a\) is of type \(D\). The role assertion \((a,b):P\) is interpreted as the two individuals \(a\) and \(b\) being in relation \(P\). The mapping of role subsumption \(P\sqsubseteq Q\) outputs that given two elements \(X\) and \(Y\) in relation \(P\), they are linked by the more general relation \(Q\). The transitivity property of roles \(P^{+}\sqsubseteq P\) says that if the instance \(X\) is related to \(Y\) by the role \(P\) and if \(Y\) is related to \(Z\) by the same role, then it is necessary that \(X\) is related to \(Z\) by the same role \(P\).
## 2 Materials and Methods
### Model Selection  
For stationary time series, autocorrelation means that it is possible to predict the future with historical data. To know if the time series is stationary, it is necessary to enter the autocorrelation function (ACF) and partial autocorrelation function (PACF) figures. ACF indicates the degree of correlation between the current series value and the current series past. PACF describes the correlation between the residuals (after removing the effects already explained by the lag) and the next lag value. Both of their figures have a certain error band, and the data in this area is considered autocorrelated, so it can be used as a basis for judging when the time series is stable. By observing ACF and PACF figures generated by quarterly and monthly sample data, it was found that the original time series was not stable. So, the autoregressive integrated moving average model (ARIMA) was selected because of its ability in solving the problems of data characteristics changing over time and time series instability in stochastic processes [19]. However, considering its disadvantages, such as the requirement that time series are stable or stable by differentiation and only linear relationships can be captured, it should be combined with other models. The idea of the grey model is to fit the data by accumulating the original series, which is suitable for data prediction with a small sample size and a single variable [20]. In the monthly forecast, it was necessary to consider the holiday effect of the Spring Festival and process the data in January and February separately. Since these data are in a relatively fixed margin of error and the series is short, a fractional-order grey accumulation model was introduced.