esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Section 5 provides the conclusion of this paper. We use the following notation throughout this paper: **A** is a matrix; **a** is a vector; \(a\) is a scalar; \(\mathcal{A}\) is a set. \((\cdot)^{T}\) and \((\cdot)^{H}\) denote transpose and conjugate transpose, respectively. \(|\mathcal{A}|\) denotes the cardinality of set \(\mathcal{A}\) and \(\|\mathbf{a}\|_{2}\) denotes the Euclidean norm of vector \(\mathbf{a}\). \(\mathcal{A}\backslash\{i\}\) represents the set \(\mathcal{A}\) with element \(i\) removed. \(\mathbf{H}(i,:)\) represents the \(i\)th row of matrix \(\mathbf{H}\). \(\mathbf{H}_{ij}\) denotes the element in the \(i\)th row and \(j\)th column of the channel matrix \(\mathbf{H}\).
## 3 Results
### Research Stream 1: Associations between Personality and TAM Scales and Moderation Effects of Gender  
Regarding Research Stream 1 (personality & TA), we present our results in Table 2. As can be seen in Table 2, honesty-humility is negatively related to all TAM scales. Moreover, extraversion is positively related to all TAM scales. The other HEXACO-PI-R scales reveal only some significant associations with different TAM scales.  
With regard to the main research question, namely whether gender would be a moderator for the relation between personality and TAM constructs, moderation analyses revealed nearly no significant interaction effects of personality with gender on the TAM constructs. However, the interaction between gender and altruism on ITU (controlled for age; all variables z-standardized) (\(B\) = 0.23, \(SE\) = 0.08; \(F\)(1,681) = 7.63, \(p\) = 0.006) was significant and revealed a negative association between altruism and ITU in men but a positive association in women. Additionally, the interaction between altruism and gender on the PUs scale (controlled for age; all variables z-standardized) was significant (\(B\) = 0.16, \(SE\) = 0.08, \(F\)(1,681) = 3.92, \(p\) = 0.048). Again, a negative association was found between PUs and altruism in men but a positive in women. All other interaction effects were non-significant (all \(p\)'s \(>\) 0.158). Additionally, when separately calculating the Pearson partial correlations (corrected for age) between ITU and altruism and PUs and altruism in men and women, only the association between ITU and altruism in men (\(r\) = -0.14, \(p\) = 0.048) turned out to be significant.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(M\) (\(SD\)) & \(M\) (\(SD\)) & & & & \\  & **Men** & **Women** & **\(t\)** & **\(df\)** & **\(p\)** & **Hedge’s \(g\)** \\  & **(\(n\) = 209)** & **(\(n\) = 477)** & & & & \\ \hline \hline
**Smartphone TA** & & & & & & \\ PU & 4.06 (0.98) & 3.94 (0.84) & 1.48 & 347.43 & 0.141 & 0.130 \\ PeU & 4.39 (0.86) & 4.28 (0.78) & 1.65 & 684 & 0.099 & 0.137 \\ ITU & 4.86 (1.10) & 4.92 (1.05) & \(-\)0.64 & 684 & 0.522 & \(-\)0.053 \\ PUs & 3.50 (1.35) & 3.27 (1.11) & 2.18 & 334.76 & 0.030 & 0.196 \\
**HEXACO-PI-R** & & & & & & \\ Honesty–Humility & 3.15 (0.63) & 3.44 (0.53) & \(-\)5.84 & 344.03 & \(<\)0.001 & \(-\)0.517 \\ Emotionality & 2.81 (0.56) & 3.50 (0.47) & \(-\)15.63 & 340.60 & \(<\)0.001 & \(-\)1.391 \\ Extraversion & 3.47 (0.56) & 3.43 (0.55) & 0.92 & 684 & 0.359 & 0.076 \\ Agreeableness & 3.18 (0.54) & 3.05 (0.49) & 3.01 & 684 & 0.003 & 0.250 \\ Conscientiousness & 3.43 (0.53) & 3.56 (0.48) & \(-\)3.23 & 684 & 0.001 & \(-\)0.268 \\ Openness & 3.25 (0.56) & 3.28 (0.53) & \(-\)0.79 & 684 & 0.432 & \(-\)0.065 \\ Altruism & 3.34 (0.69) & 3.85 (0.54) & \(-\)9.55 & 322.16 & \(<\)0.001 & \(-\)0.873 \\ \hline \hline \end{tabular} Note: PU = perceived usefulness, PeU = perceived ease of use, ITU = intention to use, PUs = predicted usage.  
\end{table}
Table 1: Mean values and standard deviations (\(SD\)s) of all scales of interest separately for men and women as well as results of \(t\)-tests on differences between genders.
## 2 Facility Layout Problem with Relative Positioning Constraints
### The FLPRC Formulation in a Matrix Form
#### 2.3.4 Distance Constraints  
We define the _distance constraints matrix_\(A_{d}\in\mathbb{R}^{N(N-1)\times 4N}\) as  
\[A_{d}=\begin{bmatrix}A_{d}^{x}&O&O&O\\ O&A_{d}^{y}&O&O\end{bmatrix},\]  
where \(A_{d}^{x}=A_{d}^{y}\) are defined as  
\[A_{d}^{x}=A_{d}^{y}=\begin{bmatrix}A_{d1}\\ A_{d2}\\ \vdots\\ A_{dN}\end{bmatrix},\]  
and \(A_{di}\) is defined as  
\[A_{di}=\begin{bmatrix}O_{(N-i+1)\times(i-1)}&\mathbf{1}_{(N-i+1)}&-I_{(N-i+1) \times(N-i+1)}\end{bmatrix}.\]  
Using these matrices, we can write the distance constraints (3) as in (18).  
\[-d_{ij}^{x}\leq x_{i}-x_{j}\leq d_{ij}^{x} \forall i<j\] \[-d_{ij}^{y}\leq y_{i}-y_{j}\leq d_{ij}^{y} \forall i<j\] \[\Leftrightarrow\left\{\begin{array}{l}x_{i}-x_{j}-d_{ij}^{x} \leq 0\\ y_{i}-y_{j}-d_{ij}^{x}\leq 0\\ -(x_{i}-x_{j})-d_{ij}^{x}\leq 0\\ -(y_{i}-y_{j})-d_{ij}^{y}\leq 0\end{array}\right., \forall i<j.\] \[\Leftrightarrow\left\{\begin{array}{l}A_{d}^{x}\mathbf{x}- \mathbf{d}_{\mathbf{x}}\leq \mathbf{0}\\ A_{d}^{y}\mathbf{y}-\mathbf{d}_{\mathbf{y}}\leq\mathbf{0}\\ -A_{d}^{x}\mathbf{x}-\mathbf{d}_{\mathbf{x}}\leq\mathbf{0}\\ -A_{d}^{x}\mathbf{y}-\mathbf{d}_{\mathbf{y}}\leq\mathbf{0}\end{array}\right.\] \[\Leftrightarrow\left\{\begin{array}{l}A_{d}\mathbf{r}-\mathbf{d }\leq\mathbf{0}\\ -A_{d}\mathbf{r}-\mathbf{d}\leq\mathbf{0}\end{array}\right.\] \[\Leftrightarrow\left[\begin{array}{l}A_{d}&-I\\ -A_{d}&-I\end{array}\right]\begin{bmatrix}\mathbf{r}\\ \mathbf{d}\end{array}\right]\leq\mathbf{0} \tag{18}\]  
If there are redundant constraints that can be eliminated, the corresponding rows are eliminated. Instead, the corresponding term is represented by the cost vector (17).
# Seven-Phase PMSM Drives Operation Post Two Types of Faults
## 3 Operation of the Seven-Phase Drive after Losing Phase 'A'
### Simulation Results of the Speed Control of the Seven-Phase Drive after Losing Phase 'A'  
The new SVPWM algorithm that is proposed in the above section has been implemented using SABER simulator as shown in Figure 12. When the motor is running under healthy operating conditions, the multi-dimension space vector modulation will be used. If phase 'A' is disconnected due to the open-circuit fault, then the new space vector modulation is implemented as a part of the fault-tolerant technique. It should be mentioned here that the switching frequency for both the multi-dimension space vector modulation and the new space vector modulation was 5 kHz. The result of implementing the control structure depicted in Figure 12 is shown in Figure 13. The speed of the seven-phase motor was controlled to 120 rpm while the load was half without faults. And the NSV-SVPWM was used to generate the gate signals to the seven-phase inverter switches. Then at \(t=1.5\) s, an open-circuit fault was introduced to phase 'A' without changing the SVPWM technique. Although the motor continued operation, the stator currents in this period were unbalanced and some of them increased by 50% compared to the currents under healthy operating condition. Moreover, the ripples in speed and torque were increased too. At \(t=2.5\) s, the new SVPWM technique proposed in this paper was used. It can be seen that the remaining  
Figure 11: Synthesizing V_ref in the first sector using V3 and V1 only. Figure 10: Illustration of how the adjacent active vectors are used to synthesis V_ref in the first sector using the new SVPWM after losing phase ‘A’. (**a**) active switching sequence. (**b**) PWM waveform. currents became equal in magnitudes and the new amplitude of the remaining currents has increased by 23% compared to the currents under healthy operating conditions. Also, the phase shifts between the currents were changed too and became similar to those obtained in Figure 7 (i.e., under minimum inverter current limitation optimization technique). Finally, the ripple in speed and torque was improved using the new SVPWM technique.
# Numerical and Statistical Analyses of Tsunami Heights with the L-Moments Method
###### Abstract  
As devastating and unpredictable tsunamis generated by underwater earthquakes are occurring more frequently, the need for tsunami disaster prevention measures is rapidly increasing. In this study, tsunami heights were estimated, and the best-fit distribution was examined through a combination of numerical analyses and statistical methods. A numerical model was employed to estimate the tsunami heights, and the parameters were estimated using the method of L-moments applied to the estimated tsunami heights. The best-fit distribution was determined by applying the estimated parameters to the L-moment ratio diagram. The study areas were the Imwon Port and the Sadong Port located in the eastern part of the Korean Peninsula. The tsunami height distribution was represented by a log-normal distribution for the Imwon Port, whereas the distribution was represented by a generalized Pareto distribution for the Sadong Port. The study indicates that the distribution most commonly suggested by previous studies, i.e., the log-normal distribution, is not always accurate. Therefore, when performing statistical analysis on tsunami heights, the assumption of a log-normal distribution should be considered carefully.  
Keywords:tsunami; wave height; L-moments method; L-moment ratio diagram; best-fit distribution +
Footnote †: journal: Applied Sciences
###### Abstract  
The performance of Precise Point Positioning (PPP) using different Global Navigation Satellite System (GNSS) product sets, including broadcast ephemerides, International GNSS Service Real-Time Service (IGS-RTS) corrections, Galileo High Accuracy Service (HAS) corrections, and precise products from the Center for Orbit Determination in Europe (CODE) Multi-GNSS Experiment (MGEX), has been evaluated. The availability of solutions, convergence time, position accuracy and Zenith Tropospheric Delay (ZTD) estimation across these products were analyzed using simulated real-time and postprocessing static modes, using data from globally distributed stations with a 1 s observation interval. The results indicate that precise products from the MGEX provide the highest accuracy, achieving centimeter-level precision in post-processed mode. Real-time simulated solutions, such as HAS and IGS-RTS, deliver promising results, with Galileo HAS meeting its target accuracy of 20 cm horizontally and 40 cm vertically and a convergence time under 5 min. However, Global Positioning System (GPS) performance within HAS is limited by a significantly lower correction availability--around 67% on average compared to over 95% for Galileo--which negatively impacts PPP performance. ZTD estimation results show that real-time services (HAS, IGS-RTS) achieved errors within 1-3 cm, sufficient for meteorological applications. This study highlights the growing importance of HAS in real-time positioning applications and suggests further improvements in GPS for enhanced performance.  
Faculty of Civil Engineering and Geodesy, Military University of Technology, [PERSON] 2, 00-908 Warsaw, Poland; [EMAIL_ADDRESS]  
HAS; RTS; PPP; GPS; Galileo; ZTD; convergence time
## 3 Analysis of Numerical Results
### Comparison of Vertical Stress with Different Methods  
The vertical stress caused by a train load decreases gradually with the increase of soil depth in the process of the transfer from the track surface to the ground. At a certain soil depth, the additional stress caused by a train load can be ignored compared with the self-weight of soil. Figure 18 demonstrates the vertical stress on the ground with different train speeds and embankment stiffnesses obtained from SSM and TDM. As shown in Figure 18a, the vertical stress on the ground soil shows an increasing trend with the increase of train speed, especially within a 10 m depth. Moreover, attenuation trends of vertical stress are calculated differently by SSM and TDM. Specifically, the vertical stress in the ground calculated by TDM is less than that calculated by SSM under a low speed condition. When the train speed is relatively high (i.e., \(v=112\) m/s), the vertical stress on the soil calculated by TDM within 5 m below the ground surface is greater than that calculated by SSM, although the former is smaller than the latter when the ground depth exceeds 5 m. This seems to indicate that under high-speed conditions, this method may underestimate the vertical stress of soil at a certain ground depth caused by a train moving load. However, due to the influence of the short time of the moving load on the vibration energy transfer to an increased depth, the soil vertical stress calculated by the proposed model decays more rapidly with depth. Moreover, in Figure 18b, when the embankment stiffness increases, the vertical stress in the railway ground soil obtained by the two methods decreases slightly. More importantly, the vertical stress obtained by SSM is larger than that obtained with TDM, regardless of the embankment stiffness. This shows that when the vehicle speed is not high (i.e., \(v=83\) m/s), the proposed method underestimates the stress level under different embankment stiffnesses. The above findings imply that, while the vertical stress in the railway ground soil has a noticeable growth trend with an increase of train speed, it is hardly affected by changing the embankment stiffness. It should be noted that for a lower velocity and greater soil depth, the vertical stress obtained by SSM is significantly greater than that obtained by TDM, which means that the proposed model may be limited in applications under these conditions.  
Figure 17: Variations of \(a_{x}\), \(d_{z}\), and \(\sigma_{x}\) with depth under different embankment stiffnesses in Path 4: (a) \(a_{z}\); (b) \(d_{z}\); (c) \(\sigma_{x}\).
# Analyzing the Impact of Variability and Uncertainty on Power System Flexibility
## 2 Flexibility Index: Ramping Capability Shortage Probability
### System Ramping Capability (SRC), Ramping Capability Requirement (RCR)  
First, the ramping capability (RC) of a generating unit is defined as the generation power that the unit can increase/decrease in a given period of time. The RC can be thought of as the system's ability to response to increased variability and uncertainty. The flexibility depends on the amount of RC secured. The total RC of all generating units in the system is denoted as the system RC (SRC), given by  
\[SRC_{t}=\sum\limits_{i\in I}A_{i,t-\Delta t}O_{i,t-\Delta t}\min(P_{\max,j}-P_ {i,t-\Delta t},\ \ r_{i}\Delta t). \tag{1}\]  
The \(SRC_{t}\) indicates the extent to which the system can supply the RC during the interval \(\Delta t\). The larger the SRC secured, the greater the flexibility; however, uncertainty in the system may lower RC availability. In Equation (1), \(A_{i,t-\Delta t}\) is a random variable denoting whether the unit fails from \(t-\Delta t\) to \(t\); its value may vary with the failure rate of the generating unit. Further information on the \(A_{i,t-\Delta t}\) calculation can be found in [30]. Parameters such as \(O_{i,t-\Delta t}\) and \(P_{i,t-\Delta t}\) are determined by the generation schedule.  
The RC requirement (RCR) can be represented as  
\[RCR_{t}=NLFE_{t}+FNL_{t}-\sum\limits_{i\in I}A_{i,t-\Delta t}O_{i,t-\Delta t}P _{i,t-\Delta t}, \tag{2}\]  
where  
\[NLFE_{t}=LFE_{t}-VGFE_{t} \tag{3}\]  
\[FNL_{t}=FL_{t}-FVG_{t}. \tag{4}\]  
\(RCR_{t}\) is the total RC required to respond to unexpected load variations and failures of loaded generating units from \(t-\Delta t\) to \(t\). The forecast errors \(NLFE_{t}\), \(LFE_{t}\), and \(VGFE_{t}\) are random variables.
# Semi-Supervised Classification Based on Low Rank Representation
###### Abstract
Low rank representation was recently introduced to GSSC [PERSON] et al. (2016). [PERSON] et al. [PERSON] et al. (2016) constructed a graph by using the calculated low rank representation coefficients of both labelled and unlabeled samples as the graph weights, and incorporated that graph into GFHF for transductive classification. Low rank representation forces the coefficient matrix to be low rank and optimizes the matrix as a whole, whereas sparse representation often optimizes the coefficients per sample. It is recognized that low-rank is an appropriate approach for capturing the global structure of the data and the global mixture of subspace structure [PERSON] et al. (2016); [PERSON] et al. (2016). [PERSON] et al. [PERSON] et al. (2016) proposed structure preserving low-rank representation technique by enforcing the local affinity property to be preserved without distorting the distant repulsion property and by utilizing the label information. [PERSON] et al. [PERSON] et al. (2016) integrated the kernel trick with low rank representation, and introduced a kernel low-rank representation graph for GSSC. However, these approaches focus on transductive classification. Similar to LGC and GFHF, they cannot directly apply to samples outside of the graph. In this paper, we introduce an inductive semi-supervised classifier based on low-rank representation (SSC-LRR). SSC-LRR first constructs a graph based on the low-rank representation coefficients. Next, it incorporates this graph into a graph-based semi-supervised linear classifier to classify unlabeled samples that are outside of the graph. Experimental results on four high-dimensional facial image datasets demonstrate that SSC-LRR performs better than other related GSSC methods. In addition, SSC-LRR is robust to noisy features and input parameters. The remainder of this paper is organized as follows. In Section 2, we give the details of how to construct a graph by low rank constraints and introduce inductive semi-supervised classification based on a low rank. Section 3 provides the experimental results and analysis, followed with conclusions in Section 4.
# Evaluation of the Prospects for the Use of Microalgae in Functional Break Production
###### Abstract  
Microalgae are widely used to produce sorbitol, ethyl and methyl alcohols, acetone, organic acids, esters, pigments, chlorophyls, carotenoids, and other compounds. The purpose of this work was to study the potential of the Baltic Sea microalgae _Arthospira platensis_ and _Chlorella vulgaris_, as new ingredients for functional bread. Bread was based with a dry mixture of _Arthospira platensis_ and _Chlorella vulgaris_ microalgae (1:1). Gas chromatography, sequencing, chromatography, pH-metry, rheological methods, methods for determining the antioxidant ability of bread samples with microalgae for ferric reduction and removal of active free radicals were used for the research. When baking bread, the organoleptic, physicochemical properties, density, coefficients of elasticity, chewiness, stickiness, resistance to crumbling, the content of polyphenols and volatile compounds in the samples of bread with microalgae were controlled. It was found that with a higher content of microalgae in bread (3% of the flour weight on dry basis), the color of the crust and crumb decreased due to the degradation of the pigment during baking. The specific volume of a load with a large amount of microalgae (5%) was lower than the volume of loaves with the addition of 1% and 3% microalgae. It is shown that the moisture content increased with an increase in the amount of microalgae in bread. Replacing wheat flour with the amounts of microalgae of 1% and 3% did not affect the pH and water activity in bread. With the addition of 5% microalgae, bread became more sour, sticky, with a slight \"fishy\" smell. The addition of more microalgae led to an increase in polyphenols in bread samples, and, accordingly, to an increased antioxidant capacity. In total, 42 volatile compounds were found in bread with microalgae, which makes it possible to obtain functional bread. It is assumed that in the future, bread with microalgae will be competitive with ordinary wheat bread due to improved nutritional and biological value.  
Arthospira platensis; Chlorella vulgaris; microalgae; bread; functional properties; antioxidant capacity +
Footnote †: journal: Applied Sciences
## 4 Establishment of Forecasting Modules for Frequency Nadir and Frequency Support Capability
### Frequency Support Capability of BESS, DLC and EDRP  
The relationship between frequency nadir and frequency support capability, including BESS, DLC or EDRP, are constructed via the BP-ANN and the linear regression. For the BESS method, the value of \(k_{BESS}\) in equation (1) is nearly a constant if the system continuously operates at the same condition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**EDRP Load Reduction** & **Frequency Nadir** & **Frequency Support** \\
**(MW)** & **(Hz)** & **(Hz/0.3 MW EDRP)** \\ \hline
0 & 57.1103 & - \\
0.3 & 57.1369 & 0.0266 \\
0.6 & 57.1631 & 0.0262 \\
0.9 & 57.1896 & 0.0265 \\
1.2 & 57.2160 & 0.0264 \\
1.5 & 57.2421 & 0.0261 \\
1.8 & 57.2685 & 0.0264 \\
2.1 & 57.2947 & 0.0262 \\
2.4 & 57.3205 & 0.0258 \\
2.7 & 57.3467 & 0.0262 \\
3.0 & 57.3724 & 0.0257 \\
3.3 & 57.3984 & 0.0260 \\
3.6 & 57.4243 & 0.0259 \\
3.9 & 57.4499 & 0.0256 \\
4.2 & 57.4758 & 0.0259 \\
4.5 & 57.5015 & 0.0257 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The frequency support capability of EDRP–case study.  
Figure 9: The frequency nadir and frequency support capability forecasting module using back-propagation artificial neural network (BP-ANN).  
For example, \(\mathbf{k_{BESS}}\) in Table 1 is approximately to 0.0367 Hz/MW and it varies under different operating conditions. Therefore, this work first utilizes the linear regression technique to approximate the values of \(\mathbf{k_{BESS}}\) under various operating conditions. Then, the BESS frequency support capability forecasting module is developed to predict the optimal \(\mathbf{k_{BESS}}\) under various system operating conditions. It is trained by the BP-ANN and illustrated in Figure 9. The input data include all the system operating data from the database established in Section 3 and the output data are the optimal \(\mathbf{k_{BESS}}\) calculated by linear regression. Additionally, this work can establish the formulation relationship between frequency nadir and BESS frequency support capability, presented as (1). For the DLC and EDRP method, the optimal \(\mathbf{k_{DLC}}\) and \(\mathbf{k_{EDRP}}\) values are obtained by the similar process and the formulation relationship are established and presented as (2) and (3).  
If only the BESS method is considered, the optimized output capacity of BESS \(\mathbf{P_{BESS,opt}}\) is determined by (4). For the DLC method only and EDRP method only, the optimized load-shedding of DLC \(\mathbf{P_{DLC,opt}}\) and optimized peak-load reduction of EDRP \(\mathbf{P_{EDRP,opt}}\) are determined similarly, presented as (5) and (6), respectively.  
\[\mathbf{P_{BESS,opt}} =(f_{\min,threshold}-f_{\min})/k_{BESS} \tag{4}\] \[\mathbf{P_{DLC,opt}} =(f_{\min,threshold}-f_{\min})/k_{DLC}\] (5) \[\mathbf{P_{EDRP,opt}} =(f_{\min,threshold}-f_{\min})/k_{EDRP} \tag{6}\]