esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Statistical Methodologies and Analysis Results
### Analysis of Reliability  
The analysis of reliability measures the consistency of the questionnaire constructs. That is, if a group of respondents is given the same questionnaire, this analyzes whether the results of multiple measurements are consistent. Taking the 5-point Likert approach as an example, when Cronbach's \(\alpha\) value \(<\)0.35, this indicates low reliability; a value between 0.35 and 0.70 means moderate reliability; and a value \(>\)0.7 indicates higher reliability. The analysis standard is reached if all constructs have a value of 0.7 or higher [48]. This research used SPSS to undertake the reliability analysis for each research component, and the results were: 'Perceived Playfulness' 0.907, 'Perceived Ease of Use' 0.875, 'Perceived Usefulness' 0.869, 'Satisfaction' 0.856, and 'Intention' 0.841. All the measured Cronbach \(\alpha\) values were over 0.7, which means every research component of the questionnaire has an acceptable level of reliability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Construction** & **Explanation of the Construction** & **Related Literature** & **Method of Measurement** & **Question Number** \\ \hline Perceived & The user is entertained during the & & [35; 36; 37; 38; 39; 40] & 5-point Likert scale & 4 \\ Playfulness & interactive process. & & [30; 31; 32; 36; 44; 45] & 5-point Likert scale & 4 \\ \hline Perceived & How difficult or easy it is to use the & & & \\ Ease of Use & interactive system. & & [30; 31; 32; 36; 44; 45] & 5-point Likert scale & 4 \\ \hline Perceived & The user obtained sufficient information & & & \\  & through their interaction with the & & [30; 31; 32; 36; 37; 43] & 5-point Likert scale & 4 \\  & interactive device. & & & \\ \hline Satisfaction & The user is satisfied while using the & & & \\  & interactive artwork. & & [17; 31; 32; 44; 47] & 5-point Likert scale & 4 \\ \hline Intention & The degree of the user’s willingness of & & & \\  & return to reuse the interactive art system. & [31; 32; 43] & 5-point Likert scale & 4 \\ \hline Personal & The user’s personal information & Research design & Ordinal Scale & 7 \\ Information & & & & \\ \hline Total & & & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The construction of the questionnaire and reference resource.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constructions** & **Individual** & **Mean** & **Standard Deviation** & **Variance** \\ \hline Perceived Playfulness & 42 & 4.5595 & 0.49328 & 0.243 \\ Perceived Ease of Use & 42 & 4.3571 & 0.49121 & 0.241 \\ Perceived Usefulness & 42 & 4.5060 & 0.44683 & 0.200 \\ Satisfaction & 42 & 4.3512 & 0.45216 & 0.204 \\ Intention & 42 & 4.2083 & 0.35748 & 0.128 \\ \hline Effective Sample Size N (Excluded) & & & 42 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics for the constructs.
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 2 Materials and Methods
### Costs of a Photovoltaic Installation
#### 2.1.3 Inverters  
Solar inverters convert the DC energy generated from solar panels into AC electricity that is compatible with the AC voltage used in a home or business. A string inverter is the most common type of inverter in the market, and it is likely to be quoted by most small-to-medium-scale solar installation businesses. A microinverter is another type of inverter that is generally more expensive and not as common in the marketplace at the moment. The inverter selection is one of the most important aspects in any solar installation, and the cost of this component can be reduced if adequate analysis is made in selecting its correct size. For a 1 MW inverter, the market offers solutions of around 0.1 \(\upmu\)/W. A quick market survey revealed a single inverter of 5 kW from a European manufacturer can be obtained at \(\sim\)1200\(\upmu\), hence at a cost of 0.24 \(\upmu\)/Wn.  
The cost of the inverter was modelled using a constant PDF, and it was built into the installation costs. It changes across the three different cases (500 kW, 50 kW and 5 kW).
## 5 Properties Analysis of the Functions \(\varphi_{m}^{s}\)
### Single-Resource-Block Power Allocation  
First, we optimize the power allocation on a single resource block. When the maritime user allocation \(A_{s}*\) on the resource block \(s\) is fixed, the optimizing problem is organized as follows:  
\[W^{s}_{RBPA}(A_{s}*,p^{s})=max\sum_{m=1}^{T}\varphi^{s}_{m}(\widetilde{p}^{s}_ {m})+C^{s}. \tag{27}\]  
\[w1^{*},w4^{*}:0\leq\widetilde{p}^{s}_{T}\leq\widetilde{p}^{s}_{T-1}\leq\ldots \leq\widetilde{p}^{s}_{1}\leq P^{s}. \tag{28}\]  
The maritime user \(t\
otin A_{s}*\) cannot obtain the allocated power without reusing the resource block \(s\). Therefore, we exclude these invalid offshore users while calculating problem \(W^{s}_{RBPA}\). The valid offshore users can be expressed as \(m_{s}\in\{1_{s},2_{s},\cdots|A_{s}*|_{s}\}\), where \(m\) represents the SIC decoding order. For example, \(A_{s}*=\{2,5,7,9\}\) indicates that there are four valid offshore users occupying resource block \(s\): \(1_{s}=2\), \(2_{s}=5\), \(3_{s}=7\), and \(4_{s}=9\). To facilitate the statistical analysis, we add an index \(0_{s}=0\). According to the definition, for \(m\geq 1\), we can conclude that \(\widetilde{p}_{1}=\widetilde{p}_{2}>\widetilde{p}_{3}=\widetilde{p}_{4}= \widetilde{p}_{5}>\widetilde{p}_{6}=\widetilde{p}_{7}=\widetilde{p}_{8}= \widetilde{p}_{9}\). This equation also explains \(\widetilde{p}_{(m-1)_{s}+1}=\cdots=\widetilde{p}_{m_{s}}\). Therefore, we only need to consider effective users when calculating \(W^{s}_{RBPA}\). We can further simplify the optimization objective to \(W^{s}_{RBPA}*\):  
\[W^{s}_{RBPA}*=max\sum_{m=1}^{|A_{s}*|}\varphi^{s}_{(m-1)_{s}+1,m_{s}}\big{(} \widetilde{p}^{s}_{m_{s}}\big{)}+D^{s},D^{s}=\varphi^{s}_{|A_{s}*|_{s}+1,T}(0) +C^{s}. \tag{29}\]  
For \(1\leq n\leq m\leq T\), we can obtain \(\varphi^{s}_{n,m}\triangleq\varphi^{s}_{(n-1)_{s}+1,m_{s}}(A_{s}*)\). Then, we derive \(argmax_{n,m}^{s}\triangleq argmax\varphi^{s}_{(n-1)_{s}+1,m_{s}}(A_{s}*)\). Equation (29) can be transformed into the following formula:\[W^{s}_{RBPA}*=max\sum_{m=1}^{|A_{s}*|}\rho^{s}_{m,m}\big{(}A_{s}*,\widetilde{p}^{s }_{m_{s}}\big{)}+D^{s}. \tag{30}\]  
\[w1^{*},w4^{*},w5^{*}:0\leq\widetilde{p}^{s}_{T}\leq\widetilde{p}^{s}_{T-1}\leq \ldots\leq\widetilde{p}^{s}_{1}\leq P^{s}. \tag{31}\]  
Algorithm 2 describes the power allocation algorithm on a single resource block (RBPA). In the subsequent multi-resource-block resource optimization, it is necessary to calculate the optimal power allocation on each resource block with various power budget values \(P^{s}\). As the RBPA program is repeatedly executed, the computational complexity will gradually increase. In lines 3-16 of Algorithm 2, we introduce Function 1 to calculate \(W^{s}_{RBPA}*(A_{s}*,p^{s})\). The results \(\widetilde{p}^{s}_{1,},\widetilde{p}^{s}_{2,},\ldots,\widetilde{p}^{s}_{|A_{ s}*|_{s}}\) are stored in a lookup table. As shown in line 19, the final return values are limited by the power budget, since the optimized results have to meet the requirements of the constraint \(P^{s}\leq P_{max}\).
## 2 Experiment
### Color Image Complexity Measures
#### 2.4.2 Color Fractal Dimension  
The most representative quantitative measure for expressing fractal geometry of color texture images is fractal dimension [3,49]. It expresses the variations and irregularities of a texture [50,51], as a relation to the self-similar regions observed across different size scales. The fractal dimension (Hausdorff dimension [52,53]) is estimated based on the probabilistic box-counting approach of [54], extended for the assessment of complexity of color fractal images with independent color components, as described in [7]. The spatial arrangement of the image (where the image is defined as a set of points \(S\), (x,y,r,g,b)) is characterized by the probability matrix \(P(m,L)\), the probability of having points included into a hyper-cube  
Figure 2: Stimuli images examples: Top natural textures (Nat) and synthetic fractal textures (Frac) bottom.  
of size \(L\) (also called a box), centered in an arbitrary point of \(S\). The scaling factor D (fractal dimension) is related to total number of boxes needed to cover the image:  
\[N(L)=\sum_{m=1}^{N}(1/m)P(m,L)\propto L^{-D} \tag{2}\]  
where \(N\) is the number of pixels included in a box of size \(L\), and \(m\) the amount of points contained in the box \(L\). The extension of the [PERSON] approach to color images [7] counts the number of pixels that fall inside a 3-D RGB cube of size \(L\), using the Minkowski infinity norm distance, centered in the current pixel. The estimation of the regression line slope for the evolution of \(N(L)\) (log-log curve) is modified with a weighting function \(\omega(L)=1/\epsilon^{2}(L)\). The color fractal dimension D is then estimated using the robust fit approach with its 9 methods: 'ols' (least squares method), 'andrews', 'bisquare', 'cauchy', 'fair', 'huber', 'logistic', 'talwar', 'welsch', and the average over all estimations is considered as the CFD value. For more details, see [7, 31].  
From the point of view of entropy, the synthetic color fractal images exhibit a larger complexity as compared to color natural images, however, considering color fractal dimension, their complexity is similar, between 1.89-3.98 for natural images and between 2.03-4.12 for fractal images. As observed in Figure 2(b), their complexity distributions intersect for the majority of the images (91.84%).
## 3 Simulation Setup  
In addition to the experimental comparison between the 3 rd-order IVSTF-NLE and the DBP-SSF method, we extend this comparison study, in terms of reach extension, after propagation in an SSMF and in a LEAF. Due to lack of a LEAF link in the lab, we decided to model a transmission system similar to the experimental case depicted in Figure 3c, in which the nonlinearities are exacerbated. In this way, we complete the experimental comparison with Monte Carlo simulations and measure what the reach extension is when employing the different nonlinear compensation methods. The replacement of the SSMF with a LEAF is only an extension of our study in order to gain a better insight on the robustness of the NLEs in extreme nonlinear effects and it is not suggested for practical use.  
The simulated transmission model is shown in Figure 4 and consists of three coherent, 4-band DP, OFDM super-channels. Quantizations are carried out in the simulation in order to take into account the ENoB, which is set equal to 5, in the DAC and ADC. The laser phase noise is neglected. The SSMF is modeled with attenuation coefficient equal to \(\alpha=0.2\) dB/km, CD of \(D=17\) ps/nm/km and Kerr coefficient \(\gamma=1.3\) W\({}^{-1}\)km\({}^{-1}\)[27]. Considering the parameters of LEAF, we have \(\alpha=0.19\) dB/km, \(D=4\) ps/nm/km, and \(\gamma=1.5\) W\({}^{-1}\)km\({}^{-1}\)[27]. The BER is calculated by error counting. A total number of 436,224 bits per band are used.
# Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping
## 2 Materials and Methods
### Objective Function  
The performance of lightning mapping estimation was measured by tuning the window size (\(W_{S}\)) of the analyzed ITF lightning signal. As shown in Figure 1 (Phase 1), each pair of the incident angles azimuth and elevation (\(Az\)-\(El\)) were estimated relative to the ground-truth simulated lightning mapping (as in Figure 1--phase 3). To effectively reduce the estimation error of ITF lightning signal and ground-truth simulated lightning mapping, a suitable objective function of the proposed PSO-based optimization algorithm \(f(x)\) was formulated by minimizing the Euclidean distance function \(J\). This can be achieved by tuning \(x\) decision variable (windows size (\(W_{S}\)))  
\[\min\ J=f(x),\]  
where \(f(x)\) is represented by the Euclidean distance  
\[f(x)=Eulidean\ distance=\frac{1}{W_{n}}\sum_{i=1}^{W_{n}}\sqrt{\big{(}Az_{i}- \hat{Az_{i}}\big{)}^{2}+\big{(}El_{i}-\hat{EL_{i}}\big{)}^{2}}, \tag{3}\]  
where \(W_{n}\) is the total number of sliding windows referred to the (\(W_{S}\)) in the processed lightning data. \(Az_{i}\) and \(El_{i}\) are the handcrafted azimuth and elevation incident angles, and \(\hat{Az}_{i}\) and \(\hat{EL_{i}}\) are the optimized incident angles by the PSO algorithm.
# Lempel-Ziv Parsing for Sequences of Blocks
## 5 Conclusions  
Given the results obtained in this paper, the main open problem is to verify whether the upper bound from Theorem 3 is tight. To this end, one either has to improve the upper bound \(z_{b}=O(bz\log\frac{n}{z})\) or has to provide a more elaborate series of examples improving the lower bound \(z_{b}=\Omega(bz)\) from Section 3 (obviously, the examples must deal with non-phrase-aligned parsings). We point out, however, that the tightness of the bound from Theorem 3 would necessarily imply the tightness of the currently best upper bound \(g=O(z\log\frac{n}{z})\)[4, 18] from Lemma 3 that relates the size \(g\) of the minimal grammar generating the string and the size \(z\) of the LZ77 parsing for the string (the best lower bound up-to-date is \(g=\Omega(z\frac{\log n}{\log\log n})\)[8, 10]). Indeed, for a constant \(b>1\), if there exists a string whose LZ77 parsing has size \(z\) and whose \(b\)-block contraction can have only LZ77 parsings of size at least \(\Omega(z\log\frac{n}{z})\), then the minimal grammar of such string must have a size of at least \(g=\Omega(z\log\frac{n}{z})\) since, by Lemma 3, the string has a phrase-aligned LZ77 parsing of size \(g\), and thus, by Theorem 2, the \(b\)-block contraction has an LZ77 parsing of size \(O(bg)\), which is \(O(g)\) as \(b\) is constant.  
The present work stems from the paper [11] where the following LZ77 parsing was considered: we first parse the input string \(s\) into phrases \(p_{1}p_{2}\ldots p_{k}\) such that each phrase \(p_{i}\) is either one letter or has an earlier occurrence in the prefix \(s[1..\ell]\), for a fixed \(\ell>0\), and then we treat every phrase \(p_{i}\) as a separate letter and construct the greedy LZ77 parsing for the obtained \"contracted\" string of length \(k\). The resulting parsing naturally induces an LZ77 parsing for \(s\). The motivation for this variant is in the considerable reduction in space during the construction of the parsing that allowed us to compress very large chunks of data using this two-pass scheme. The problem that was investigated in the present paper can be interpreted as a special case when all phrases \(p_{i}\) have the same length \(b\) (at least those phrases that are located outside of the prefix \(s[1..\ell]\)). The following question can be posed: can we adapt the techniques developed here to a more general case in order to find an upper bound for the LZ77 parsing from [11] in terms of the optimal greedy LZ77 parsing for \(s\)?  
All authors have contributed equally. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Higher Education of the Russian Federation (Ural Mathematical Center project No. 075-02-2021-1387).  
Not applicable.  
We would like to thank the anonymous referees for their comments that helped to improve the paper.  
The authors declare no conflict of interest.
## 3 Results
### Effects of fSFP on Liver Damage
#### 3.5.2 Changes in AST, ALT, ALP, LDH and GGT Levels in Blood  
In comparison to the NFD control group, there was a significant elevation (\(p<0.01\)) in the levels of Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT), Alkaline Phosphatase (ALP), Lactate Dehydrogenase (LDH), and Gamma-Glutamyl Transferase (GGT) in the blood of the HFD control group. However, in the groups receiving all three doses of fSFP, including the metformin treatment group, a significant reduction (\(p<0.01\)) in serum levels of AST, ALT, ALP, LDH, and GGT was observed when compared to the HFD control group (Table 2).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & **Items** & **AST** & **ALT** & **ALP** & **LDH** & **GGT** & **BUN** & **Creatinine** \\  & **(U/L)** & **(U/L)** & **(IU/L)** & **(\(\times\)10 IU/L)** & **(IU/L)** & **(mg/dL)** & **(mg/dL)** \\ \hline Controls & & & & & & & \\ NFD & 78.20 \(\pm\) 15.65 & 41.80 \(\pm\) 11.38 & 75.10 \(\pm\) 15.24 & 71.26 \(\pm\) 15.63 & 5.40 \(\pm\) 1.51 & 29.60 \(\pm\) 10.49 & 0.64 \(\pm\) 0.17 \\ HFD & 195.60 \(\pm\) 26.68 \({}^{\rm a}\) & 152.20 \(\pm\) 16.82 \({}^{\rm a}\) & 219.60 \(\pm\) 21.80 \({}^{\rm a}\) & 407.86 \(\pm\) 57.83 \({}^{\rm c}\) & 20.40 \(\pm\) 1.71 \({}^{\rm a}\) & 128.80 \(\pm\) 20.46 \({}^{\rm a}\) & 2.24 \(\pm\) 0.41 \({}^{\rm c}\) \\ Reference & & & & & & & \\ Metformin & 129.00 \(\pm\) 15.01 \({}^{\rm a}\) & 97.70 \(\pm\) 13.84 \({}^{\rm ab}\) & 143.40 \(\pm\) 21.05 \({}^{\rm a}\) & 242.18 \(\pm\) 72.72 \({}^{\rm c}\) & 11.90 \(\pm\) 1.29 \({}^{\rm a}\) & 76.10 \(\pm\) 18.62 \({}^{\rm a}\) & 1.36 \(\pm\) 0.16 \({}^{\rm a}\) \\ Test materials–fSFP & & & & & & & \\
400 mg/kg & 108.20 \(\pm\) 17.87 \({}^{\rm a}\) & 83.80 \(\pm\) 16.61 \({}^{\rm a}\) & 121.30 \(\pm\) 27.04 \({}^{\rm a}\) & 167.05 \(\pm\) 27.53 \({}^{\rm a}\) & 9.30 \(\pm\) 1.42 \({}^{\rm a}\) & 62.20 \(\pm\) 15.35 \({}^{\rm a}\) & 1.15 \(\pm\) 0.16 \({}^{\rm a}\) \\
200 mg/kg & 127.10 \(\pm\) 16.33 \({}^{\rm a}\) & 95.40 \(\pm\) 20.28 \({}^{\rm a}\) & 141.80 \(\pm\) 21.28 \({}^{\rm a}\) & 221.47 \(\pm\) 58.13 \({}^{\rm a}\) & 11.80 \(\pm\) 1.99 \({}^{\rm a}\) & 76.70 \(\pm\) 16.11 \({}^{\rm a}\) & 1.33 \(\pm\) 0.25 \({}^{\rm a}\) \\
100 mg/kg & 144.70 \(\pm\) 12.24 \({}^{\rm a}\) & 111.60 \(\pm\) 15.25 \({}^{\rm a}\) & 157.60 \(\pm\) 17.52 \({}^{\rm a}\) & 287.73 \(\pm\) 55.39 \({}^{\rm a}\) & 15.20 \(\pm\) 1.23 \({}^{\rm a}\) & 96.90 \(\pm\) 8.70 \({}^{\rm a}\) & 1.62 \(\pm\) 0.99 \({}^{\rm a}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Changes on serum AST, ALT, ALP, LDH, GGT, BUN and creatine levels in NFD- or HFD-supplied mice.  
Figure 5: Effects on total cholesterol and triglycerides in the feces of mice due to fSFP administration. Analysis underwent DT3 testing, revealing noteworthy distinctions (a) with \(p<0.01\) compared to NFD control and (b) with \(p<0.01\) compared to HFD control. The values are depicted as the mean \(\pm\) SD for a sample size of 10 mice.
## 4 Discussion
### Evolution Characteristics of Surrounding Rock Pressure
It can also be seen from Figure 12 that in the early growth period, the overall growth rate of the surrounding rock pressure of the No. 4 and No. 5 sensors at the arch top was faster than that of the No. 6 and No. 7 sensors at the left arch shoulder and the left wall, while the growth rate of the surrounding rock pressure of the No. 1 and No. 3 sensors at the right wall and the No. 8 sensors at the left arch foot was slower. The spatial evolution trend of the surrounding rock pressure in the monitoring section is that the growth rate of the arch is greater than that of the two sides of the wall, and the growth rate of the left side is greater than that of the right side. The measured distribution of the deformation pressure of the surrounding rock of the monitoring section with the parallel pilot tunnel on 23 June 2024 is shown in Figure 13. During the initial support process of concrete spraying, the data line of sensor No. 2 on the right-side wall of the monitoring section was cut off in the concrete, and the surrounding rock pressure value was 0 MPa. When calculating the pressure value of sensor No. 2 in Figure 13, this paper adopts the interpolation of the monitoring results of sensors No. 1 and  
Figure 12: Pressure changes curve of surrounding rock on monitoring section with the parallel pilot tunnel. No. 3. It can be seen from the monitoring results of other normal working pressure sensors that the distribution of the surrounding rock pressure is asymmetrical. The surrounding rock pressure of the left side wall of the cross-section is greater than that of the right-side wall, and the surrounding rock pressure of the arch is greater than that of both sides of the wall. It is worth noting that the surrounding rock pressure at measuring point No. 4 of the arch reaches the maximum value of 0.809 MPa. To sum up, the spatial distribution characteristics of the surrounding rock pressure in the parallel pilot tunnel section are the highest on the left arch shoulder, second highest on the arch top, and smallest on the left wall.
## 4 Conclusions  
* Hydrogen sulfide is a prospective fuel, being a substrate of a liquid-phase fuel cell operating in sulfide form in aqueous media. It is suitable to use in water basins rich in hydrogen sulfide, as it can be relied on to simultaneously produce electric energy and cleanse water.
* Operational parameters of the tested fuel cell show reasonable current and power yields especially for sulfide concentrations in the medium range of 200-300 mg dm\({}^{-3}\). The concentration of sulfite and sulfate ions provides for the main products of the electrochemical processes. Above this range parasitic reactions take place, i.e., polysulfide and thio-compounds with low power yield are obtained. Conversion rate is relatively low, (around 30%). Therefore, efforts should be made to increase sulfide to sulfate conversion rate. Further minimization of parasitic reactions in the bulk is required.
* Three different ion-exchange membranes were tested. In all cases the total cell resistance was found to be lower than some reference values. Fumapem and Neosepta membranes showed high sensitivity to hydroxyl ions whereas Celgard 3501 showed better durability.
* However, the achieved current and power densities are still low, and cannot compete with the traditional fuel cells. Efforts should be directed to further reduction of cell resistance by different methods such as membrane selection, new catalyst application to speed-up oxidation and reduction processes to improve the aeration system, etc.  
Conceptualization: V.B.; Methodology: V.B., E.R.-V., M.M.; Validation: E.R.-V., M.M., S.S.; Investigation: E.R.-V., M.M., S.S.; Writing--Original Draft Preparation: V.B.; Project Administration & Funding Acquisition: V.B.  
The research and work presentation were supported by the Scientific Research Fund of The Republic of Bulgaria, Grant DN 07/7.  
The authors are grateful to [PERSON] (Institute of Electrochemistry and Energy Systems, BAS) for the SEM imaging and to [PERSON], PhD (Institute of Mineralogy and Crystallography, BAS) for the accomplished EDS analysis.  
The authors declare no conflict of interest.