esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Overview
### Probabilistic Fingerprint
#### 3.3.1 Multivariate Gaussian Mixture Model  
The probabilistic fingerprint is the conditional probability distribution of signal strengths given the cell position \(P(S_{r}|r),r\in R\). The assumption of Gaussian distribution of the RSS is not accurate enough, as proven by [PERSON] and [PERSON] [57]. A Gaussian Mixture Model allows approximation of a probability density function by a weighted sum of Gaussian densities each with different parameters.  
Most research work uses the GMM to approximate the RSS distribution for a single AP and ignores the interference between signals from different APs [58; 59; 60]. The MVGMM is implemented to approximate the probability density distribution of the training data for each cell, which takes advantages of correlation between the RSS from various APs within a certain area.  
Given the training data \(S\) at cell \(r\) contains \(M\) RSS measurements from \(N\) APs, (for convenience, we remove the notation \(r\) in \(S_{r}\) in the subsequent sections), and considering one measurement contains signals coming from \(N\) APs, the training data \(S\) are a matrix consisting of multivariate random variables. The density function modelled by MVGMM can be mathematically defined as:  
\[P(S|\mu,\Sigma,\pi,r)=\sum\limits_{k=1}^{K}\pi_{k}\mathcal{N}(S|\mu_{k},\Sigma_ {k}) \tag{6}\]  
where \(K\) is the number of component of the model, \(\sum\limits_{k=1}^{K}\pi_{k}=1\). \(\pi_{k},\mu_{k},\Sigma_{k}\) are the mixture weight, mean and covariance matrices for the \(k_{th}\) mixture component. \(\mathcal{N}(S|\mu_{k},\Sigma_{k})\) is the \(k_{th}\) mixture component from \(N\)-dimensional multivariate Gaussian distribution:  
\[\mathcal{N}(S|\mu_{k},\Sigma_{k})=\frac{1}{2\pi^{N/2}|\Sigma_{k}|^{1/2}}\exp^{ -\frac{1}{2}(S-\mu_{k})^{T}\Sigma_{k}^{-1}(S-\mu_{k})} \tag{7}\]  
During fingerprinting, the signature of cell \(r\) is generated by the MVGMM parameterised by \(\Phi=\{\mu_{k},\Sigma_{k},\pi_{k}\},k=1,\ldots,K\). The EM algorithm is applied to estimate the parameters of the model.  
1. E step. Calculate the responsibilities using the current parameters, which can be viewed as the posterior probability that the \(m_{th}\) measurement \(S_{m}\) is from the \(k_{th}\) component. \[\gamma_{k}(S_{m})=\frac{\pi_{k}\mathcal{N}(S_{m}|\mu_{k},\Sigma_{k})}{\sum \limits_  
\[\Sigma_{k}^{*}=\frac{1}{M_{k}}\sum\limits_{m=1}^{M}\gamma_{k}(S_{m})(S_{m}-\mu_{k} ^{*})(S_{m}-\mu_{k}^{*})^{T} \tag{10}\]  
\[\tau_{k}^{*}=\frac{M_{k}}{M} \tag{11}\]  
where  
\[M_{k}=\sum\limits_{m=1}^{M}\gamma_{k}(S_{m}) \tag{12}\]  
Evaluation. Evaluate the log-likelihood  
\[lnP(S|\mu,\Sigma,\pi)=\sum\limits_{m=1}^{M}ln\{\sum\limits_{k=1}^{K}\pi_{k} \mathcal{N}(S_{m}|\mu_{k},\Sigma_{k})\} \tag{13}\]  
These three steps are iteratively repeated until the log likelihood convergences.
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 3 Preliminaries
### Cluster Head Selection
#### 4.2.2 Adaptive Node Degree  
Due to the limited network bandwidth resources, the number of neighbor nodes needs to be controlled within a certain range. The clustering scale is constrained by a certain condition in the CH selection. If the CH coverage range has a small number of nodes, the bandwidth cannot be fully utilized. If the CH coverage range contains a significant number of nodes, network congestion and transmission delays grow. The adaptive node degree is used in this work to avoid network congestion, which is positively correlated to the probability of being selected as CH. In [29], it states that if the distance between two nodes is less than the maximum transmission range \(R\), they can be regarded as two \(R\)-neighbors. Furthermore, the distance can be calculated by using the position enclosed in the broadcasted HELLO message. Hence, the total number of \(R\)-neighbors of \(i\) is defined as its node degree (\(Nd_{i}\)), demonstrated in the following equation:  
\[Nd_{i}=|\varphi_{i}|=\sum_{j\in S_{i}\
eq i}\{d_{ij}<R\} \tag{16}\]  
Figure 3: Radio energy dissipation model.  
where \(\varphi_{i}\) is the set of neighbor nodes of \(i\), and \(d_{ij}\) is the average distance between node \(i\) and \(j\). The adaptive node degree difference is defined as  
\[n_{i}=\left|Nd_{i}-\frac{N}{k^{*}}\right| \tag{17}\]  
where \(k^{*}\) is the optimal number of clusters determined by Equation (5). The adaptive node degree is normalized [20] by  
\[N_{i}=e^{-n_{i}} \tag{18}\]
## 3 Results
### Pairwise Band Comparison  
The bands of the Landsat scenes covering the two study areas were found to be significantly correlated. For the Landsat OLI, the pairwise comparison showed that the bands with the highest correlation coefficients were bands 1 and 2 (0.99), 1 and 4, 2 and 4, and 3 and 4, all of which are visible bands (Table 3). Correlation values \(>0.7\) are considered significant--pairs of Landsat bands with such values are known to be correlated. Therefore, they provide redundant information, i.e., the recorded reflectance in one channel could be used to predict the value in the other. Therefore, with the exception of bands 5 and 1, and 5 and 2 which reported the least correlation coefficient values of 0.68 and 0.66, respectively, the Landsat bands of the image are significantly correlated.
# Binary and Multi-Class Malware Threads Classification
## 3 Multiple Features
### Gabor Features  
In image processing, Gabor filters [27] have indeed been widely employed for feature extraction. A coefficient matrix provided by Gabor filters allows for multi-resolution analysis. Thus, a 2D Gabor filter has been applied in order to extract features. A 2D Gabor can be generated in the time and frequency domain [28]. In the time domain, Gaussian function and a sinusoidal wave are produced. In the frequency domain, it is a convolution of the transformations of the Gaussian and sinusoid. In order to extract the 2D Gabor features, the following mathematical expression (Equation (2)) is employed.  
\[\begin{array}{c}G_{\theta,f,\sigma 1,\sigma 2}(x,y)=exp\left[\frac{-1}{2} \left(\frac{x^{2}}{\sigma_{1}^{2}}+\frac{y^{2}}{\sigma_{2}^{2}}\right)\right] \ \cos(2\pi fx^{\prime}+\varphi)\\ x^{\prime}=x\ \sin\theta+y\ \cos\theta\\ y^{\prime}=x\ \cos\theta+y\ \sin\theta\end{array} \tag{2}\]  
where:  
\(f\) = the spatial frequency of the wave at an angle \(\theta\) with the \(x\) axis,  
\(\sigma 1\) and \(\sigma 2\) = the standard deviations of the 2D Gaussian envelope,  
\(\varphi\) = the phase.  
In a number of image analysis and classification applications, Gabor filters are often used. Two frequent Gabor features [29], namely Mean Squared Energy and Mean Amplitude, are recovered across a range of orientations and sizes.  
At different scales and orientations, Gabor features can be retrieved. Figure 3 shows the 2D Gabor filters in a variety of eight orientations and five scales [30].  
Figure 2: SFTA Extraction process.  
Finally, feature vector [32] is obtained by extracting Mean Squared Energy and Mean Amplitude as feature vectors from the Response Matrices. Mean Squared Energy is calculated by adding the squared values of each matrix value in a response matrix. The mean amplitude of a response matrix is calculated as the sum of the absolute values of each matrix value. If you want to understand further about Gabor Features, I suggest reading [32].
## 4 Results and Analysis
### Node-Based Analysis
#### 4.1.2 The Accuracy of the Sound Level  
The test for checking accuracy and robustness is necessary since the measuring algorithm of sound level was implemented in each sensor device. Generally, the devices have minor differences on performance even if they are the same type. In addition, different environments have different effects on the evaluation of the algorithm. Therefore, we should use a professional sound level meter and multiple devices for contrastive test in complex environments with different SNRs. To sum up, a test in real-life scenarios was carried out with the professional sound level meter AWA5688 that can record the current sound level in every second [29]. We programed our devices to record the sound level every second as the sound level meter. We deployed the sound level meter and four nodes at the school road and school gate with the same position and same height to implement a 1-min test. As can be seen in Figure 5(a), the variation of noise within 1 min is slight in the quiet environment of the school street. On the contrary, the variation is random and varied in the noisy environment of the school gate. In summary, the maximum error between the sound level meter and the four nodes is 0.47 dB in a quiet environment and is 0.93 dB in a noisy environment. We considered that the noisy environment with a low SNR leads to the error increase but was less than 1 dB. As such, this test indicates that the proposed system has a high accuracy of the sound level.
###### Abstract  
In a turbine network, an accurate wind speed prediction plays a key role in improving the efficiency of turbine operations. At present, Large Language Models (LLMs) have shown their strong capabilities in time sequence forecasting. Unfortunately, the existing LLM-based models face the following two difficulties in precise wind speed prediction: (1) These models fail to capture spatio-temporal correlations. (2) These models rely exclusively on supervised learning to train. Supervised learning updates parameters by optimizing a loss function, which focuses solely on improving accuracy on a specific regression or classification task rather than understanding the true data distribution. To tackle these difficulties, we devise a novel wind speed prediction architecture entitled Spatio-Temporal GPT-2 United Generative Adversarial Network (STGPT2 UGAN). In particular, in order to model spatio-temporal correlations, we first design a Spatio-Temporal GPT-2 (STGPT-2), which comprises a Spatio-Temporal Block and the variant of GPT-2. Second, to improve the training of STGPT-2, a Generative Adversarial Network (GAN) is introduced to develop an adversarial training strategy. The strategy combines unsupervised training and supervised training in a complementary fashion. The unsupervised training promotes STGPT-2 to learn the distribution of true data through minimizing the adversarial loss. The supervised training intends to align the true values and the predicted values. We conduct the experiments on one real-world wind speed dataset. The experimental results verify that STGPT2 UGAN outperforms the state-of-the-art benchmarks in terms of prediction precision.  
wind speed prediction; GAN; LLM; spatio-temporal +
Footnote †: journal: Applied sciences
# Feature Selection Algorithms as One of the Python Data Analytical Tools +
## 7 Conclusions  
In the last few years, Python programming language has become extremely popular in most machine learning applications. Unfortunately, most of the existing Python machine learning tools are neural networks oriented, which results in an increasing gap between existing and implemented methods for most classical machine learning fields.  
* This paper contains an overview of existing Python feature selection libraries and libraries on other languages that are easily compatible with Python.
* This paper pProvides a description of newly created feature selection library ITMO FS. We provided a complete description of the library including program architecture design.
* This paper shows that ITMO FS library provides better view to the traditional feature selection algorithms, supports hybrids and ensembles, and in most cases is more user-friendly than other libraries. Moreover, some of the provided test showed that ITMO FS library works more quickly than the biggest Python feature selection ASU library on some algorithms.
* This paper provides code samples for better understanding of library usage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Library** & **F-score index** & **Gini index** \\ \hline \multirow{2}{*}{Basehock} & ITMO & 1.084 & 0.376 \\ \cline{2-4}  & ASU & 0.116 & 1.507 \\ \hline \multirow{2}{*}{COIL} & ITMO & 1.081 & 0.048 \\ \cline{2-4}  & ASU & 0.048 & 253.257 \\ \hline \multirow{2}{*}{ORL} & ITMO & 5.793 & 0.058 \\ \cline{2-4}  & ASU & 0.028 & 97.622 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of ASU feature selection library and ITMO FS library computational time in seconds.  
In the future, we plan to implement all algorithms mentioned in Table 1, more modern algorithms of feature selection as well as more classical ones, and then move to implementation of different ensembling and hybrid algorithms. In this perspective, we will also add meta-learning approaches for easier selection of feature selection algorithms and their tuning.  
Conceptualization, I.S.; Data curation, N.P.; Formal analysis, I.S.; Funding acquisition, I.S.; Investigation, N.P.; Methodology, I.S.; Resources, I.S.; Software, N.P.; Supervision, I.S.; Visualization, N.P.; Writing--original draft, N.P.; and Writing--review and editing, I.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Government of the Russian Federation, Grant 08-08 and Research and Development grant No. 619421 \"Development of the Python open-source feature selection library\".  
The authors declare no conflict of interest.
## 5 Field Applications  
To validate the superiority of the support optimization scheme, a field application was conducted at the Kekegai coal mine subsidiary inclined shaft in Yulin, China. The testing location was 2600 m from the entrance, with a depth of 258 m and a testing length of 20 m. The displacement within two months after support was monitored with a roof separation gauge, as shown in Figure 19.  
It can be observed that the optimization measures have effectively controlled the fragmentation and collapse of the surrounding rock in the stope. Extending the length of the anchor cable has anchored it to stable bedrock and also helped in controlling the development of internal fractures. The reinforcement measures for the fractured tunnel walls have rapidly and effectively stabilized the surrounding rock. The maximum deformation has been reduced from 50.3 cm to 28.5 cm, achieving a 43.3% improvement. The water inflow has decreased from 308 m\({}^{3}\)/d to 98 m\({}^{3}\)/d, with a reduction of 68.2%. The excavation speed has increased from 10 m/d to 17 m/d, representing a 70% improvement. These improvements provide sufficient support for the TBM's cutting shoes and resistance for excavation, facilitating rapid progress.
## 3 Results
### Ecosystem Monitoring through Metabarcoding
In the case of the MAP and VP CSS, no statistical differences in terms of the OTU abundance were noticed between days 0 and 14 (Figures 2 and 3). Only a slight increase in abundance of 2 OTU belonging to _Leuconostoc_ genera was observed for the MAP CSS. Moreover, in contrast to gravlax, the storage time showed no statistical effect on the ecosystem richness of the two smoked products (ANOVA, \(p\)-values of 0.854 and 0.196). For the three products, the most important shift in the ecosystem took place between 14 and 28 days of storage (Figure 3). After 28 days, the SG ecosystem composition was more diverse than the two smoked products (Figure 2). The _Photobacterium_ abundance dropped from 97.5 to 16.5-32.6% (variation between the triplicates) and Enterobacteriaceaeae increased to reach 35.1-46.9% for _Serratia/Yersinia_ and 1.7-6.0% for _Enterobacter/Klebsiella_. Some LAB genera, such as _Lactobacillus_, _Carnobacterium_ and _Lactococcus_, which were weakly present at 14 days (<1.5%), also increased after 28 days to reach 4.1-14.3, 1.9-6.6 and 10.8-16.4%, respectively. For the MAP CSS, _Photobacterium_ and _Lactococcus_ disappeared (<0.5% of abundance) in favor of _Lactobacillus_, which became the only dominant genus with more than 90% abundance and, to a lesser extent, _Leuconostoc_ (1 to 5% abundance). Conversely, for VP CSS, despite the variability between replicates, the _Photobacterium_ abundance increased to reach 78.4 and 96.8% in the second and third replicates, respectively, although _Lactococcus_ and _Lactobacillus_ remained present in some replicates.
###### Abstract  
Karstification in carbonate platforms of the Miocene age in Central Luconia province, offshore Sarawak, Malaysia, has been discussed since the onset of exploration and initial discoveries in the region, with over 200 mapped platforms to date. An extensive drilling program over the last decade confirmed the existence of karst during the drilling process where issues such as total loss circulation and bit drops were common. Karsti in Central Luconia has been proposed by several authors; however, detailed quantitative description of the observed features have not yet been conducted. This study involves systematic mapping of loss circulation depths, chalkified/rubble/vuggy zones described from cores, and vugs of >2 mm in size and moldic porosity observed on thin sections of the Jintan platform. These data supplement the interpretation of karst from multiple 3D seismic attributes. Seismic interpretation of the Jintan and M1 platforms revealed an extensive dendritic pattern which is on average 70-100 m deep and 3-5 km long, and circular geobodies of 1 km in width that exist on the upper part of the platform. Spectral decomposition, also known as time-frequency analysis, was used to enhance the interpretation of karst features on seismics within a specific wavelength. In this study, a comparison of three spectral decomposition methods applied on the 3D seismic cube of the Jintan and M1 platforms was undertaken to determine the method which allowed for better delineation of the karst features. The results show that the short-time Fourier transform (STFT) method using frequencies of 46, 54, and 60 Hz delineated most of the karst features compared to the continuous wavelet transform (CWT) Morlet and CWT Ricker wavelet methods. This paper aims to discuss the dimensions, evolution and geometry of the karst features quantitatively on three selected karst horizons named \"K1\", \"K2\", \"K3\". Interpretation revealed that the dendritic karst features were found to be most prominent on the K2 horizon which lies below a conspicuous change of the external geomorphology of the platform. Backstepping of the platform margin by 12 km is observed in both platforms. Quantitative seismic interpretation shows that the karst observed in M1 platform is approximately 70-100 m deep, and the dendritic features are around 1-2 km in length and approximately 500 m wide; whereas, in the Jintan platform the dendritic features observed are up to 5 km in length with several 1 km wide circular/sinkhole features. More than 20 dendritic features orientated SE and NS were mapped mainly in the transitional area as well as the center of both platforms. The nature of the karst morphology in Central Luconia remains controversial; however, it is proposed to be of mixing zone karst origin.  
karst; geobodies; carbonate platform; attributes; spectral decomposition; volume calculation; Central Luconia; Malaysia +
Footnote †: journal: Applied Sciences
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
###### Abstract  
The Global Navigation Satellite System Reflectometry (GNSS-R) technique exploits the characteristics of reflected GNSS signals to estimate the geophysical parameters of the earth's surface. This paper focuses on investigating the wind speed retrieval method using ocean scattered signals from a Beidou Geostationary Earth Orbit (GEO) satellite. Two new observables are proposed by computing the ratio of the low energy zone and the high energy zone of the delay waveform. Coastal experimental raw data from a Beidou GEO satellite are processed to establish the relationship between the energy-related observables and the sea surface wind. When the delay waveform normalized amplitude (this will be referred to as \"threshold\" in what follows) is 0.3, fitting results show that the coefficient of determination is more than 0.76 in the gentle wind scenario (<10 m/s), with a root mean square error (RMSE) of less than 1.0 m/s. In the Typhoon UTOR scenario (12.7 m/s-37.3 m/s), the correlation level exceeds 0.82 when the threshold is 0.25, with a RMSE of less than 3.10 m/s. Finally, the impact of the threshold and coherent integration time on wind speed retrieval is discussed to obtain an optimal result. When the coherent integration time is 50 milliseconds and the threshold is 0.15, the best wind speed retrieval error of 2.63 m/s and a correlation level of 0.871 are obtained in the UTOR scenario.  
GNSS-R; wind speed; energy zone; Beidou +
Footnote †: journal: Sensors
## 3 Simulation Demonstration and Performance Evaluation
In this paper, we used the 7% overhead forward-error-correction (FEC) hard judgment threshold (3.8 \(\times\) 10\({}^{-3}\)) as the system bit-error-rate (BER) performance judgment value. Figure 6 shows that for the same laser linewidth, when the BER reached 3.8 \(\times\) 10\({}^{-3}\), the effect of suppressing Gaussian noise became obvious with the increase in phase-offset-estimation symbol-block length N2. The required transmitting power gradually decreased, and the receiver sensitivity gradually increased. When block length N2 increased to a certain level, the performance of the receiver system deteriorated sharply. This is because the long average length of N2 meant that a large number of symbol samples shared the same estimated phase, so the phase estimation accuracy of each sample decreased. Considering various factors such as noise tolerance, carrier phase tracking, and algorithm complexity [13; 26], we consider N2 = 32 the optimal average length for this simulation system. Figure 7 shows the normalized MSEs of the proposed JCSCR and the 4 th-PD-VV method when the system was set to different frequency offsets under different atmospheric turbulence states. The symbol-block length of frequency-offset estimation N1 was set to 512 for both algorithms. The transmitting power values of the strong-turbulence, medium-turbulence, and weak-turbulence states were \(-\)16 dBm, \(-\)18 dBm, and \(-\)20.5 dBm, respectively. As shown in Figure 7, the FOE value of the proposed scheme was closer to the actual frequency offset value, with a lower normalized MSE. As the turbulence state enhanced, the proposed scheme was more advantageous. Therefore, the frequency offset estimation accuracy of the proposed JCSCR was significantly better than that of the 4 th-PD-VV algorithm. Figure 5: Normalized MSE as a function of the transmitting power for different N1 values. Figure 8 shows the effect of transmitting power versus BER for the proposed JCSCR and the traditional 4 th-PD-VV scheme for different laser linewidths in different atmospheric turbulence channels. The simulation system set the FO to 300 MHz. The lengths of the estimated symbol blocks, N1 and N2, were set to 512 and 32, respectively. As shown in Figure 8, the BERs of both algorithms gradually increased with increases in laser linewidth. However, the JCSCR with a larger linewidth still outperformed the 4 th-PD-VV scheme with a smaller laser linewidth under medium turbulence and strong turbulence. With a BER of \(3.8\times 10^{-3}\) and a laser linewidth of 50 kHz, the JCSCR required less transmitting power, and the receiver sensitivity was improved by 3.6 dB, 2.2 dB, and 1.2 dB for the strong turbulence, medium turbulence, and weak turbulence, respectively. Figure 6: Transmitting power as a function of different N2 values. Figure 7: Normalized MSEs as functions of different frequency offsets: (**a**) \(6.5\times 10^{-14}\) m\({}^{-2/3}\), (**b**) \(5\times 10^{-15}\) m\({}^{-2/3}\), and (**c**) \(6\times 10^{-16}\) m\({}^{-2/3}\).