esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
Therefore, considering the simplicity and cost-effectiveness of these systems, an LED-based system would be suitable for short-range applications in saline water. Nevertheless, both LEDs and LDs may be used as light source transmitters in a hybrid UWOC system, as proposed by [PERSON] et al. [25]. To make optical alignment less challenging, LDs are used for high-precision positioning, while LEDs are used for coarse alignment. 3. Effect of turbidity: The third scenario studied the impact of turbidity. When an optical wave passes through turbid water, it is attenuated significantly. Scattering is a type of accumulation that implies that the greater the turbidity, the greater the impact on signal quality. With a maximum water salinity of 40 g/L, the water's turbidity is increased as dirt is gradually added. Turbidity level was measured using the EXO 2 turbidity sensor. After that, power readings were collected at various turbidity levels, including 6.8, 15.84, 24.75, and 39 NTU. As shown in Figure 6, turbidity significantly affects the LED-based UWOC link. Even a small amount can block most of the channels. It is observed that 6.8 NTU of turbidity (maximum salinity) can block more than 80% of the channel at transmission distance of more than 60 cm. The results of the turbidity analysis are consistent with those reported in Refs. [22; 23], which showed that the received power gradually decreased as the turbidity of the water increased. However, turbidity is a relative value because it varies with the type of additive contained in the water. Considering the maximum Red Sea salinity of 40 g/L and reported turbidity of 2.4 FTU in [21], the \(\sim\)1 m white-LED-based UWOC is feasible in the Red Sea environment. An example of an application for short-range LED-based communication is a real-time swimmer's feedback. Short-range communication for this system requires approximately 1 m between sensors mounted on the swimmer's wrist and a receiver on his/her goggles [26]. This feedback can provide crucial information such as lab rate and stroke rate. Another important example is communication between divers [27]. Normally, communication between divers is via hand signals or by using underwater writing slates. However, using hand signals enables basic communication yet needs extensive memorization. Slates, on the other hand, do not permit real-time communication as it takes time to write and draw the partner's attention underwater. Therefore, it can be concluded that white-LED-based UWOC is a promising technology for short-range application in the Red Sea environment.
## 3 Results
### In Vitro Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**SNP Allele Frequencies (_n = 304_)**} \\ \hline  & \multicolumn{2}{c}{**All subjects**} & \multicolumn{2}{c}{**Ca**} & \multicolumn{2}{c}{**Co**} \\ \hline
**Allele** & **Count** & **Proportion** & **Count** & **Proportion** & **Count** & **Proportion** \\ \hline T & 414 & 0.68 & 241 & 0.69 & 173 & 0.67 \\ C & 194 & 0.32 & 109 & 0.31 & 85 & 0.33 \\ \hline \multicolumn{6}{c}{**SNP Genotype Frequencies (_n = 304_)**} \\ \hline  & \multicolumn{2}{c}{**All subjects**} & \multicolumn{2}{c}{**Ca**} & \multicolumn{2}{c}{**Co**} \\ \hline
**Genotype** & **Count** & **Proportion** & **Count** & **Proportion** & **Count** & **Proportion** \\ \hline C/C & 24 & 0.08 & 14 & 0.08 & 10 & 0.08 \\ T/C & 146 & 0.48 & 81 & 0.46 & 65 & 0.5 \\ T/T & 134 & 0.44 & 80 & 0.46 & 54 & 0.42 \\ \hline \multicolumn{6}{c}{**SNP Exact Test for Hardy–Weinberg Equilibrium (_n = 304_)**} \\ \hline  & **TT** & **TC** & **CC** & **T** & **C** & _p-value_ \\ \hline
**All subjects** & 134 & 146 & 24 & 414 & 194 & 0.086 \\
**Ca** & 80 & 81 & 14 & 241 & 109 & 0.38 \\
**Co** & 54 & 65 & 10 & 173 & 85 & 0.16 \\ \hline \multicolumn{6}{c}{**SNP Association with Response STATUS (_n = 304, Crude Analysis_)**} \\ \hline
**Model** & **Genotype** & **Ca** & **Co** & **OR (95\% CI)** & _p-value_ \\ \hline  & T/T & 80 (45.7\%) & 54 (41.9\%) & 1.00 & \\ Codominant & C/T & 81 (46.3\%) & 65 (50.4\%) & 1.19 (0.74-1.91) & 0.77 \\  & C/C & 14 (8\%) & 10 (7.8\%) & 1.06 (0.44-2.56) & \\  & T/T & 80 (45.7\%) & 54 (41.9\%) & 1.00 & \\ Dominant & C/T+ C/C & 95 (54.3\%) & 75 (58.1\%) & 1.17 (0.74–1.85) & 0.5 \\  & T/T+ C/T & 161 (92\%) & 119 (92.2\%) & 1.00 & \\ Recessive & C/C & 14 (8\%) & 10 (7.8\%) & 0.97 (0.41–2.25) & 0.94 \\  & T/T+ C/C & 94 (53.7\%) & 64 (49.6\%) & 1.00 & \\ Over-dominant & C/T & 81 (46.3\%) & 65 (50.4\%) & 1.18 (0.75–1.86) & 0.48 \\ Log-additive & n/a & n/a & n/a & 1.10 (0.76–1.58) & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The allele, genotype frequencies, the exact test for Hardy–Weinberg equilibrium analysis, and the single nucleotide polymorphism (SNP) association with breast cancer status of rs12976445 miR-125a gene polymorphism. Ca—cases, Co—controls, OR—odds ratio, CI—confidence interval. 2 with amplification and status 3 were ranked as the positive group (P). Patients were also divided into two groups according to ER1\(\alpha\) or PR status, 0-10% as the negative group and 10-75% as the positive group. We analyzed data using \(\chi\) square test. The lowest \(p\)-value, 0.0606, was obtained for TT, CT, CC genotypes analysis for HER2 receptor. Our analysis revealed a tendency that T allele predominated in HER2 positive samples (Table 2). These results are concomitant with genotype frequency, which TT-variant slightly predominates in cases over controls. To determine how C- and T(U)-variants could regulate miR-125a expression, we decided to analyze the 2D structure of pre-miRNA using simicmethods.
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 1 Introduction
In addition, the method of series inversion is used to derive the two-dimensional spectrum of the target in bistatic forward-looking SAR [14]. In this paper, regarding AA-BiSAR with one-moving transmitter, a range frequency-domain algorithm was adjusted to the imaging process. First, the residual phase caused by the moving transmitter is corrected in range frequency-domain. Based on this, keystone transform (KT) [15; 16; 17; 18] is used to eliminate the coupling between the range and azimuth, since the imaging capability is greatly affected by the coupling between the range and azimuth. Finally, the focused image is obtained through matched filtering. The rest of the paper is organized as follows. The working mode, including the imaging geometry and the basic principle of AA-BiSAR with one-moving transmitter are introduced in Section 2. The signal model is analyzed based on the imaging geometry, and the imaging algorithm based on KT is presented in Section 3. In Section 4, the spatial resolution of AA-BiSAR is derived and evaluated in detail. In Section 5, the simulation experiments are carried out to verify the effectiveness of the proposed algorithm. Finally, a conclusion is given in Section 6.
# Multithreading-Based Algorithm for High-Performance Tchebichef Polynomials with Higher Orders
## 2 Background and Literature Review
### Background of the Tchebichef Polynomials  
In this section, the fundamentals background of the TPs and their moments are presented. We first provide the definition of the TPs. Then, the moments' computation for multidimensional signals is provided.  
The Tchebichef polynomials, which refer to \(\mathcal{T}_{n}^{N}(x)\) of the \(n\)th order and size \(N\), are given by [20]:  
\[\mathcal{T}_{n}^{N}(x)=\frac{\mathcal{A}}{\sqrt{B}}\,{}^{3}F_{2} \bigg{(}\begin{array}{c|c}-n,-x,1+n\\ 1,1-N\end{array}\bigg{|}\,1\bigg{)} \tag{1}\] \[\mathcal{A}=(1-N)_{n}\] \[\mathcal{B}=(2n)!\binom{N+n}{2n+1}\] \[n,x=0,1,\dots,N-1\]  
where \(n\) represents the moment order, \(x\) is the signal index, \(N\) represents the polynomial size, and \((a)_{b}\) denotes the Pochhammer symbol as defined in [41; 42]:  
\[(a)_{b}=a\,(a+1)\,(a+2)\,\dots\,(a+b-1)=\frac{\Gamma(a+b)}{\Gamma(a)}, \tag{2}\]  
\(({}_{d}^{c})\) represents the binomial coefficients and \({}_{3}F_{2}(\cdot)\) denotes a special function that is represented by the hypergeometric series as follows:  
\[{}_{3}F_{2}\bigg{(}\begin{array}{c|c}-n,-x,1+n\\ 1,1-N\end{array}\bigg{|}\,1\bigg{)}=\sum_{k=0}^{\infty}\frac{\left(-n\right)_{ k}\left(-x\right)_{k}\left(1+n\right)_{k}}{\left(1-N\right)_{k}\left(1\right)_{k}k!} \tag{3}\]  
The TPs satisfy the orthogonality conditions [20]:  
\[\sum_{n=0}^{N-1}\mathcal{T}_{n}^{N}(x)\mathcal{T}_{m}^{N}(x)=\delta_{mn} \tag{4}\]where \(\delta_{mn}\) denotes the Kronecker delta and is given by  
\[\delta_{mn}=\begin{cases}1,&n=m\\ 0,&n\
eq m\end{cases} \tag{5}\]  
To obtain the Tchebichef moments of a multidimensional signal and support a signal with \(l\) dimensions, the following formula can be used:  
\[\mathbb{T}(n_{1},n_{2},\ldots,n_{l})=\sum_{x_{1}=0}^{N_{1}-1}\sum_{x_{2}=0}^{ N_{2}-1}\cdots\sum_{x_{l}=0}^{N_{l}-1}f(x_{1},x_{2},\ldots,x_{l})\mathcal{T}_{n_{ 1}}^{N_{1}}(x_{1})\mathcal{T}_{n_{2}}^{N_{2}}(x_{2})\cdots\mathcal{T}_{n_{l}}^ {N_{l}}(x_{l}) \tag{6}\]  
where \(\mathbb{T}\) represents the multidimensional Tchebichef transform. Reconstructing the signal from the Tchebichef domain to the signal domain can be performed by employing the following formula:  
\[\hat{f}(x_{1},x_{2},\ldots,x_{l})=\sum_{n_{1}=0}^{Ord_{1}-1}\sum_{n_{2}=0}^{ Ord_{2}-1}\cdots\sum_{n_{l}=0}^{Ord_{l}-1}\mathbb{T}(n_{1},n_{2},\ldots,n_{l}) \mathcal{T}_{n_{1}}^{N_{1}}(x_{1})\mathcal{T}_{n_{2}}^{N_{2}}(x_{2})\cdots \mathcal{T}_{n_{l}}^{N_{1}}(x_{l}) \tag{7}\]  
where \(\hat{f}\) denotes the reconstructed signal and \(Ord_{i}\) signifies the \(i\)th order used for signal reconstruction.
## 2 Related Works
### Security Using Blockchain  
IIoT-sensed data are trained using a machine learning algorithm called Support Vector Machine (SVM) [28]. Though SVM is typically applied in real-world applications such as disease diagnosis, it does not directly address the security concerns. To preserve the privacy of IIoT data, a secure SVM approach has been employed by utilizing blockchain-based encrypted IIoT data. Encrypted sensed data are securely transmitted to other nodes without the involvement of a trusted authority. However, it should be noted that homomorphic encryption, which can support complex sensed data, requires significant energy resources, and it may not be suitable for this scenario.
# A Comparative Analysis of High Availability for Linux Container Infrastructures
## 6 Discussion
One limitation of the study may be that the evaluation focused on a limited set of container platforms, namely Docker, Kubernetes, and Proxmox. However, these platforms represent widely used tools in the industry and form the basis of many container infrastructures. Comparisons between them provide important information for practitioners and organizations to decide on the appropriate container platform for their needs. Focusing directly on these three popular container platforms allowed for relevant and comparable results. However, the container environment is constantly evolving, and new platforms and orchestration tools may emerge over time that will find widespread adoption among users. Future investigations could extend the research to encompass a wider range of container technologies and take into account the latest advances in the industry. In addition, it is important to note that the performance of container platforms can be affected by multiple factors including hardware configurations, network environments, and workload characteristics. In the experiments carried out, an attempt was made to provide the same conditions for all container platforms, focusing on minimizing differences between the tested tools to better compare their capabilities. However, in practice, it can be difficult to achieve a complete environment identity for each container platform, as there are multiple variables that can affect performance. As a result, performing tests in different scenarios and settings can provide a more comprehensive understanding of the capabilities and limitations of the platforms. The variety of conditions under which testing platforms can be performed can provide an understanding of how different platforms behave in different situations and which factors may have the greatest impact on their performance. In this way, research can get even closer to real-world conditions and better understand their behavior in practical scenarios. Moreover, realistic operating environments can have unpredictable impacts on the performance and availability of container platforms. Another limitation is that direct node failure was used in the testing. In addition to direct node failure, it would be useful to address realistic failure scenarios, such as simulating hardware errors or network problems. These scenarios would help to evaluate how quickly and efficiently container platforms can respond to unforeseen events and restore applications to full functionality. In addition, considering the limited number of nodes used in the testing phase, it is important to recognize the possible impact on generalizability of the results to larger and more complex deployments. Further investigations with a broader range of nodes would provide a more comprehensive understanding of the capabilities and performance solutions in various scenarios.
## 2 Materials and Methods
### Noise Detection  
After a series of signal processing methods, we obtain refined CSI data. However, the data still may contain noise caused by external movements. Therefore, we introduce a technique to detect the part containing such noise to enhance the result. We deliberately added a noise during the last part of the experiment. In Figure 10 shows intended noise after frame 385 after applying filtering methods. The extensive difference can be observed after applying Butterworth Filtering. Due to this distinctive nature, we added threshold to determine if that window contain a noise.  
The RR data after all filtering is seen as a sinusoidal time-series data. Therefore, within a considering window 30 s, we calculated \(Z\)-Score for Anomaly Detection [35] for detecting. If there is anomaly within the window, we discard it. \(Z\) score at a certain frame is given by Equation (14).  
\[Z_{i}=\frac{X_{i}-\mu_{w}}{\sigma_{w}} \tag{14}\]  
where \(X_{i}\), \(\mu_{w}\) and \(\sigma_{w}\) are the data point, the mean of this window and the standard deviation of this window, respectively.  
Figure 11 (top) shows poor estimation after frame 355 due to intentional noise. We investigated by plotting \(Z\) score as shown in Figure 11 (bottom). Maximum \(Z\) shifts explicitly at the frame where prediction went far from minimum \(Z\). Then we set some threshold for the distance between minimum \(Z\) score and maximum \(Z\) score, denoted as \(\tau\) in the study. If any window size has the distance between minimum \(Z\) score and maximum \(Z\) score higher than \(\tau\), it is considered as noise caused by external movement, and discarded.  
Figure 9: Example of applying Butterworth low pass filtering when \(\delta=2\) and \(\epsilon=30\) to refine the output CSI data.  
Figure 11: Noise in predicted RR after frame 355 due to deliberate noise (**top**), and removal of frame rates containing deliberate noise using Z-Score (**bottom**).  
Figure 10: Example of noise detection when noise is intendedly made after frame 385 in Hampel, Filtering (**top**), Guassian Filtering (**middle**) and Butterworth Filtering (**bottom**).
# Satellite Requirements for Observation of Close Proximity Celestial Bodies +
## 4 Conclusions  
Satellites offer a great option to explore other places of the solar system and to collect information gathered in the surface by rovers and sensors. In this paper we have discussed some of the main concerns for implementing of a satellite constellation in other celestial bodies of the solar system. Among those, are the power levels that could be received from satellites in low orbit altitudes and the constellation designs that could be used.  
Conceptualization, J.M.G.-T. and C.V.-R.; methodology and software, C.E.D.-B.; validation, C.E.D.-B. and J.F.C.-V.; formal analysis, C.E.D.-B. and J.F.C.-V.; investigation, C.E.D.-B. and J.F.C.-V.; writing--original draft preparation, J.M.G.-T.; writing--review and editing, C.E.D.-B., J.F.C.-V., C.V.-R. and [PERSON]-H.; visualization, J.M.G.-T.; supervision, [PERSON]-T., [PERSON]-R. and [PERSON]-H.; project administration, [PERSON]-R. and [PERSON]-H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We would like to acknowledge the support from the Telecommunications Research Group, the School of Engineering and Sciences for providing the means to develop this collaborative work.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial intelligence \\ LRO & Lunar Reconnaissance Orbiter \\ LOS & Line of sight \\ RAAN & Right ascension of the ascending node \\ \end{tabular}
## 4 Case Study
### Data Preparation  
Block model (csv) and stope solids (.obj) files are used to assign the coordinates of each block, block size, grade values, and density, as well as the face and vertices information of each stope. Additional attributes, such as resource classification, can also be incorporated if needed. In this study, a block model containing chrome grade values and the corresponding optimized stope solids for the relevant mine were used as input data for the software.  
User-defined inputs are required for accurate calculations for the stope margin due to every mine having a different processing recovery, portability, and mining operational cost. The editable parameter modular design of D-Loss allows delivering flexible and adaptable results. User-defined inputs for the financial analysis are provided in Table 3 for each individual stope.  
A total 20 number of stopes were uploaded using the files, but Stopes 4, 8, and 10 were selected for the analysis in the case study. Three different unplanned dilution scenarios were applied as 8%, 10%, and 15%, where impacts on the stope tonnage and grades were analyzed to understand how unplanned dilution affects the overall stope grade, financial performance, and associated CO\({}_{2}\) emissions.
## 5 Case Studies
### Effect of Multiple Frequency-Modulated Detection  
To verify the accuracy of the multiple frequency detection, the modulated waveform is set to include three modulation frequency components, as shown in (13). The frequencies are modulated at 60, 150, and 300 Hz, respectively, and the associated modulated magnitudes are 1, 0.15, and 0.33 p.u., respectively. Figure 6a shows the waveform of harmonics-polluted signal. The performances in TFA results of STFT, CWT, FSST, and SSWT are shown in Figure 6b-e, respectively. The efficiency of SSWT in this case is superior to the other methods since it has a better TF resolution. A better TF resolution allows a more precise TF representation with more concentrated TFR.  
\[s(t)=\sin(2\pi\cdot 60t)+0.15\sin(2\pi\cdot 150t)+0.33\sin(2\pi\cdot 300t) \tag{13}\]  
Figure 5: The flowchart of proposed solution procedure for harmonic and interharmonic detection.
###### Abstract  
As objects of load prediction are becoming increasingly diversified and complicated, it is extremely important to improve the accuracy of load forecasting under complex systems. When using the group method of data handling (GMDH), it is easy for the load forecasting to suffer from overfitting and be unable to deal with multicollinearity under complex systems. To solve this problem, this paper proposes a GMDH algorithm based on elastic net regression, that is, group method of data handling based on elastic net (EN-GMDH), as a short-term load forecasting model. The algorithm uses an elastic net to compress and punish the coefficients of the Kolmogorov-Gabor (K-G) polynomial and select variables. Meanwhile, based on the difference degree of historical data, this paper carries out variable weight processing on the input data of load forecasting, so as to solve the impact brought by the abrupt change of load law. Ten characteristic variables, including meteorological factors, meteorological accumulation factors, and holiday factors, are taken as input variables. Then, EN-GMDH is used to establish the relationship between the characteristic variables and the load, and a short-term load forecasting model is established. The results demonstrate that, compared with other algorithms, the evaluation index of EN-GMDH is significantly better than that of the rest algorithm models in short-term load forecasting, and the accuracy of prediction is obviously improved.  
short-term load forecasting; GMDH; neural network; lasso; elastic net; difference degree
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 4 Implemented Statistical and AI Methods
### Least Squares Regression  
The polyfit function in NumPy is used for polynomial fitting, which involves finding the coefficients of a polynomial that best fits a set of data points. The theoretical foundation of polynomial fitting lies in the concept of least squares regression.  
The goal to minimize the sum of squared differences  
\[\text{minimize}\ \sum_{i=1}^{N}(y_{i}-P(x_{i}))^{2} \tag{1}\]  
In the Matrix form, it can be represented in this way:  
\[X=\begin{bmatrix}1&x_{1}&x_{1}^{2}&\ldots&x_{1}^{n}\\ 1&x_{2}&x_{2}^{2}&\ldots&x_{2}^{n}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 1&x_{N}&x_{N}^{2}&\ldots&x_{N}^{n}\end{bmatrix}\quad C=\begin{bmatrix}c_{0} \\ c_{1}\\ \vdots\\ c_{n}\end{bmatrix}\quad Y=\begin{bmatrix}y_{1}\\ y_{2}\\ \vdots\\ y_{N}\end{bmatrix}\]  
Finally, a solution is gained using least squares  
\[C=(X^{T}X)^{-1}X^{T}\gamma \tag{2}\]
## 2 Materials and Methods  
### Materials  
Geopolymers were produced based on waste materials: fly ash and waste white glass. The fly ash was taken from electrofilters in the Dolna Odra power plant (Gryfino, Poland). The fly ash chemical composition is presented in Table 1, and its grain size distribution is presented in Figure 1. The broken white laboratory glass, stored at the University (WPUT, Szczecin, Poland), was used as WG. The waste glass was ground in the jaw crusher (EKOLAB, Cracow, Poland, LAB-08-240) and sieved on a sieve with a mesh of 1 mm. The WG grain size distribution is presented in Figure 1.  
The following substances were also used for geopolymer manufacturing:  
* Sodium hydroxide (water solution of sodium hydroxide, NaOH) with molar modulus 10 (Warchem S.A., Warsaw, Poland).
* Sodium metasilicate pentahydrate (Na\({}_{2}\)SiO\({}_{3}\cdot\)5H\({}_{2}\)O) with a molar mass of 212.4 g/mol (Warchem S.A., Warsaw, Poland).  
A standard cement mortar has been prepared to compare the geopolymer's thermal and mechanical properties according to the European Standard EN-196-1 [29]. Three specimens (40 \(\times\) 40 \(\times\) 160 mm) of the reference cement mortar (RCM) were prepared using  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline LOI & SiO\({}_{2}\) & Al\({}_{2}\)O\({}_{3}\) & Fe\({}_{2}\)O\({}_{3}\) & CaO & MgO & Na\({}_{2}\)O & K\({}_{2}\)O & SO\({}_{3}\) & NH\({}_{3}\) \\ \hline
3.1 & 52.5 & 26.71 & 6.95 & 3.1 & 2.2 & 0.98 & 2.98 & 0.43 & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of fly ash (FA).