esa-sceva/satcom-chunk-collection huggingface dataset:
# Directional Support Vector Machines
## 8 The Multi-Dimensional Setting  
The extension of the ideas presented before to the multi-dimensional setting is easy. For this purpose, assume our data consist of both directional and non-directional components. This allows each data example to be represented as a vector \(\mathbf{x}=\left(\mathbf{x}^{(d)\intercal}\quad\mathbf{x}^{(l)\intercal}\right)^{\intercal}\), where \(\mathbf{x}^{(d)}\in\mathbb{R}^{D}\) represents the directional components and \(\mathbf{x}^{(l)}\in\mathbb{R}^{L}\) represents the non-directional ones. Suppose we wish to represent the \(i\)th directional component \(\mathbf{x}^{(d)}_{i}\) in a feature space \(\mathbb{X}^{(d)}_{i}\), through a mapping \(\mathbf{\phi}^{(d)}_{i}:\mathbb{R}\mapsto\mathbb{X}^{(d)}_{i}\), and the non-directional ones in a feature space \(\mathbb{X}^{(l)}\), through a mapping \(\mathbf{\phi}^{(l)}:\mathbb{R}^{L}\mapsto\mathbb{X}^{(l)}\). Then, our model \(f(\mathbf{x}|\mathbf{w})\) becomes:  
\[\begin{split} f(\mathbf{x}|\mathbf{w})&=w_{0}+\sum_{i=1}^{D }\mathbf{w}^{(d)\intercal}_{i}\mathbf{\phi}^{(d)}_{i}\left(\mathbf{x}^{(d)}_{i}\right)+\mathbf{ w}^{(l)\intercal}\mathbf{\phi}^{(l)}\left(\mathbf{x}^{(l)}\right)\\ &=w_{0}+\bar{\mathbf{w}}^{\intercal}\begin{pmatrix}\mathbf{\phi}^{(d)}_ {1}\left(\mathbf{x}^{(d)}_{1}\right)\\ \vdots\\ \mathbf{\phi}^{(d)}_{D}\left(\mathbf{x}^{(d)}_{D}\right)\\ \mathbf{\phi}^{(l)}\left(\mathbf{x}^{(l)}\right)\end{pmatrix},\end{split} \tag{16}\]  
where \(\bar{\mathbf{w}}=\left(\mathbf{w}^{(d)\intercal}_{1},\ldots,\mathbf{w}^{(d)\intercal}_{D},\mathbf{w}^{(l)\intercal}\right)^{\intercal}\). Therefore, in the standard setting where the feature spaces are fixed and possibly infinite dimensional, but the respective inner products have a closed form, we may use the kernel trick to solve the optimization problem. Such kernel is an inner product in the joint feature space \(\mathbb{X}^{(d)}_{1}\times\cdots\times\mathbb{X}^{(d)}_{D}\times\mathbb{X}^{( l)}\) and equals the sum of the individual kernels:  
\[k(\mathbf{x},\mathbf{z})=\sum_{i=1}^{D}k^{(d)}_{i}\left(\mathbf{x}^{(d)}_{i},\mathbf{z}^{(d)} _{i}\right)+k^{(l)}\left(\mathbf{x}^{(l)},\mathbf{z}^{(l)}\right), \tag{17}\]  
where \(k^{(d)}_{i}(\mathbf{\cdot},\mathbf{\cdot})=\langle\mathbf{\phi}^{(d)}_{i}(\mathbf{\cdot}),\mathbf{ \phi}^{(d)}_{i}(\mathbf{\cdot})\rangle\) and \(k^{(l)}(\mathbf{\cdot},\mathbf{\cdot})=\langle\mathbf{\phi}^{(l)}(\mathbf{\cdot}),\mathbf{\phi}^{( l)}(\mathbf{\cdot})\rangle\).  
If the feature mappings \(\mathbf{\phi}^{(d)}_{i}\) are finite dimensional functions that depend also on parameters to be optimized, like, for instance, in the case of \(\mathbf{\phi}^{(d)}_{i}\left(\mathbf{x}^{(d)}_{i}\right)=g_{t}\left(\mathbf{x}^{(d)}_{i}+ \theta_{i}\right)\), the kernel itself becomes dependent on such parameters. In this setting, we opted to plug Equation (16) directly into Equation (7), solving the problem directly in its primal form using gradient-based optimization.  
For simplicity, we set \(\mathbf{\phi}^{(l)}\) to the identity in our experiments, inducing the usage of the linear kernel for all the non-directional components.
## 5 Short-Baseline Instantaneous RTK Performance with Smartphones in Lying Down and Upright Positions
### Double-Differenced Carrier-Phase Residuals for Short-Baselines Using External Antennas  
Figure 8 shows the DD phase residuals for L1 GPS, E1 Galileo and L1 QZSS for S20 in the top panels and GP4 in the bottom panels for the first hour of data (out of 8 h, cf. Table 2), while using external antennas in the short-baseline setup configuration (Figure 3). In this analysis, we exclude the BDS DD phase residuals since there is thirty days between the datasets in the left and right column, respectively. This means that only the GPS, QZSS and Galileo constellations are expected to repeat (cf. DOY in Table 2). We do not depict the L5 DD GPS, Galileo and QZSS phase residuals in Figure 8 since they exhibit a similar pattern to L1 and are thus not shown for brevity. Finally, we remark that there was only one QZSS satellite tracked for GP4, when in the lying down position, possibly due to an internal GNSS data logger rejection criterion used, so the corresponding DD phase residuals are not depicted.  
In Figure 8a, we can see a poorer RMS in the DD carrier-phase residuals for all three GNSSs when the S20 smartphones are in a lying down position (left column) and when  
Figure 8: Short baseline with S20 in (a) and GP4 in (b) using external antennas (cf. Table 2) in lying down (left column) and upright position (right column): The DD phase residuals for 1 h of data of L1 GPS are depicted as blue, E1 Galileo as green and L1 QZSS as cyan dots. Note that there was only one QZSS satellite in the GP4 dataset in (b) and the left column, and thus the corresponding DD phase residuals were not depicted. (a) S20 smartphones used in the lying down position (left) and upright position (right column) using external antennas. (b) GP4 smartphones used in the lying down position (left) and upright position (right column) using external antennas.  
compared to an upright position (right column). We can also see linear trends and some mean of the DD carrier phase residuals that are different to zero meters for S20 in the left column of Figure 8a, similar to the corresponding results in Figure 7a for the zero-baseline setup. Note that the linear trends are absent when the S20 smartphones are upright in the right column of Figure 8a and also have zero mean errors, similar to the results in Figure 7a. For instance, the RMS of the L1 GPS DD phase residuals goes from 0.021 m to 0.006 m when the S20 smartphones are in an upright position. At the same time, we can see that the RMS values for GP4 remain consistent between setups, with the L1 GPS RMS being 0.005 m for both setups, however, with a slight increase from 0.004 m to 0.005 m for E1 Galileo, when having the smartphones upright. This consistency in RMSs implies again that the GP4 smartphone antennas are not as sensitive to interferences when lying down as the S20 smartphones.
###### Abstract  
This work evaluates in vitro the influence of a new biocompatible porous Si-Ca-P monophasic (7 CaO-P\({}_{2}\)O\({}_{5}\):2 SiO\({}_{2}\)) ceramic on the cellular metabolic activity, morphology and osteogenic differentiation of adult human mesenchymal stem cells (_al_MSCs) cultured in basal growth medium and under osteogenic inductive medium. Alamar Blue Assay and FESEM were carried out in order to monitor the cell proliferation and the shape of the cells growing on the Si-Ca-P monophasic ceramic during the study period. The osteogenic differentiation of _al_MSCs was investigated by means of immunofluorescence staining (osteocalcin, osteopontin, heparan sulphate and collagen type I expression), quantitative reverse transcription polymerase chain reaction (qRT-PCR) (integrin-binding sialoprotein, osteocalcin, alkaline phosphatase, osteopontin, osteonectin, runt-related transcription factor 2 and collagen type I) and expression of surface markers (CD73, CD90 and CD105). We could check osteogenic differentiation in _al_MSCs growing under the influence of Si-Ca-P monophasic ceramics itself, but especially when growth medium was replaced by osteogenic medium in the culture conditions. These results allowed us to conclude that the new Si-Ca-P monophasic scaffold greatly enhanced _al_MSCs proliferation and osteogenic differentiation; therefore, it may be considered to be employed as a new bone graft substitute or scaffold for bone tissue engineering.  
bioceramics; Si-Ca-P based materials; mesenchymal stem cells; osteogenic differentiation; tissue engineering +
Footnote †: journal: Applied Sciences
# Delamination Localization in Multilayered CFRP Panel Based on Reconstruction of Guided Wave Modes
## 2 Materials and Methods
### Guided Wave Dispersion Curves  
Obtaining dispersion curves is an important task for the selection of appropriate GW modes and the excitation frequency for the inspection. The SAFE method is a fundamental technique used to compute the dispersion curves of guided waves. In this method, a 2D arbitrary plate is divided into elements in the thickness direction, and the \(x\)-axis corresponds to the wave propagation direction, as shown in Figure 2.  
The governing equation of the plain strain model for wave propagation in an elastic medium using the SAFE method can be represented by the following equation [56]:  
\[(\lambda+\mu)\
abla\big{(}\
abla\cdot\mathbf{u}^{\prime}\big{)}+\mu\
abla^{2} \mathbf{u}^{\prime}=\rho\frac{\partial^{2}\mathbf{u}^{\prime}}{\partial t^{2} }\, \tag{1}\]  
where \(\lambda\) is the Lame constant, \(\mu\) is the shear modulus, \(\rho\) is the density, and \(t\) is the time. The \(\mathbf{u}^{\prime}_{x},\ \mathbf{u}^{\prime}_{y},\ \text{and}\ \mathbf{u}^{ \prime}_{z}\) are symmetric, shear horizontal, and asymmetric displacement vectors.  
In this study, it was assumed that the plate's width is infinitely long along the \(y\)-axis. A total of 36 elements are assigned in the \(z\)-axis, the thickness of the plate is discretized into elements, and each element is 0.097 mm. In this multilayered CFRP plate, each ply is assigned two elements. Each ply had a thickness of 0.194 mm, and the thickness of 18 plies of CFRP plate was 3.5 mm.  
The purpose of the SAFE method was to determine the phase velocity, \(c_{ph}(f)\), and group velocity, \(c_{g}(f)\), at various frequencies. The dispersion curves obtained for the CFRP  
Figure 1: Guided wave inspection setup of CFRP panel: defect-free (**a**), receiver position before the delamination (**b**), and receiver position after the delamination (**c**).  
Figure 2: Schematic diagram of the 2D model used in the SAFE method.  
plate under investigation are presented in Figure 3a,b. Based on the dispersion curves, it can be observed that at a frequency of 150 kHz, the A\({}_{0}\) mode phase and group velocities are 1450 m/s and 1623 m/s, respectively. The S\({}_{0}\) mode phase and group velocities are 7664 m/s and 7616 m/s, respectively.  
The maximum amplitude of each mode varies depending on the excitation frequency. At higher frequencies, the guided wave modes exhibit higher-order modes with smaller wavelengths. However, this results in complex signal interpretation because of dispersion, as multiple modes are superimposed on each other. By selecting the frequency range of 150 kHz, only fundamental modes were exhibited, and higher-order modes should not be present.
# Influence of Region on Sensory and Chemical Profiles of Pennsylvania Gruner Veltliner Wines
## 3 Results and Discussion
### Relating Sensory and Instrumental Data
Gallic acid can provide both bitter taste and asringent mouthfeel in wines [51], and it is possible higher concentrations of gallic acid in samples reduced perception of sour taste due to mixture suppression [37]. Correlations for Year 2 sensory and instrumental data are shown in Figure 3B. Thiol aroma was positively correlated with 1-propanol and 3-hexen-1-ol, which provide an alcohol and grassy aroma, respectively. It is unlikely that these compounds are contributing to the thiol aroma perceived in wines, as panelists were trained using a 4-MMP solution to define thiol aroma. Gallic acid and isoamyl alcohol, which provides a fruity aroma, were also positively correlated. Floral aroma was positively correlated with all three phenolic compounds, VA, and total SO\({}_{2}\). Gallic acid, \((-)\)-epicatechin, and \((+)\)-catechin are not believed to impart aroma or flavor characteristics to wines, so the correlation with floral aroma is likely coincidental. As previously mentioned, additions of sulfur dioxide can decrease floral aromas by binding carbonyl compounds [49], so it is unlikely that SO\({}_{2}\) bound to floral compounds in the wine samples since these two variables were positively correlated. Sweet taste was positively correlated with RS (\(r(13)=0.76\), \(p=1.72\times 10^{-3}\)), indicating panelist perceived wines with higher sugar content as sweeter than other samples. Green apple flavor was positively correlated with TA (\(r(13)=0.59\), \(p=2.72\times 10^{-2}\)). Higher TA values indicate a sample is more acidic, and it is possible panelists associated more sour samples with green apple flavor. This flavor attribute was also correlated with methyl octanoate, which can give a waxy and green aroma. Haziness was negatively correlated with a number of volatile compounds in Year 2, including ethanol, isobutanol, isoamyl alcohol, and 1-hexanol. Haziness was also negatively correlated with \(L^{\star}\) (\(r(13)=-0.86\), \(p=8.89\times 10^{-5}\)), indicating that the trained panel rated darker samples as hazier. Floral aroma was negatively correlated with ethyl hexanoate (\(r(13)=-0.62\), \(p=1.82\times 10^{-2}\)), which can give a green apple aroma. Ethyl hexanoate was also negatively correlated with the three phenolic compounds quantified. The most prominent difference in correlation results across years is that sulfur aroma was negatively correlated with many ethyl and acetate esters in Year 1 while esters were negatively correlated with haziness in Year 2. It is possible that higher SO\({}_{2}\) levels in Year 1 wines provided a sulfur aroma that overpowered fruity notes provided by esters.
# An Algorithm for Rescheduling of Trains under Planned Track Closures
## 4 Joint Problem of Train Rescheduling and Track Closure Planning
### Representation of Train Timetable  
In the paper, the train transport is considered to handle the flow of people traveling between stations. Many time demands and restrictions are imposed on resources (trains) and infrastructure (railway network) involved in such transport. They are reflected in a timetable, which takes into account not only constraints resulting from the joint use of resources and infrastructure, as well as their availability, but also nationally oriented regulations, along with other technical limitations. The railway transport is organized as a set of \(TR\) train runs (or trains) \(\mathbf{TR}=\{1,2,\ldots,TR\}\) which have specified routes with distinguished origins, destinations, and stopping stations provided with departure and arrival times at each station.  
More specifically, each train \(p\in\mathbf{TR}\) consists of a set of stations and a connected available set of tracks. They are organized in routes denoted by \(\boldsymbol{R}_{p}^{\mathrm{T}}=[R_{p,i,j,k}^{\mathrm{T}}]_{i,j\in\mathbf{V},k \in\mathbf{K}}\), where \(R_{p,i,j,k}^{\mathrm{T}}=1(0)\) if train \(p\) goes from station \(i\) to \(j\) by track \(k\) (otherwise). The timetable determines when the train \(p\) arrives and departures at station \(i\), which is a part of a route \(\boldsymbol{R}_{p}^{\mathrm{T}}\). The arrival and departure times of \(p\) at station \(i\) belonging to route \(\boldsymbol{R}_{p}^{\mathrm{T}}\) are denoted by \(t_{i,p}^{\mathrm{AER}}\), \(t_{i,p}^{\mathrm{DEP}}\), respectively. Besides the virtual station \(i=0\), some obvious relations among the times are valid, like every departure time at a station must be not less than the corresponding arrival time, and an arrival time of the consecutively visited station \(i\) specified in \(\boldsymbol{R}_{p}^{\mathrm{T}}\) must be not less than the departure time from the previous one. Moreover, maximal arrival \(T_{i,p}^{\mathrm{AER}}\) or departure \(T_{i,p}^{\mathrm{DEP}}\) lateness are given for some stations. A minimal dwell time \(T_{i,p}^{\mathrm{D}}\) is defined for each station \(i\) and train \(p\). Zero dwell times mean no stops at stations. The maximal dwell time during the train run for each station (except station 0) is common and equal to \(T^{\mathrm{DL}}\).  
The timetable for train run \(p\) is represented by a tuple \(TT_{p}^{\mathrm{G}}=<\boldsymbol{R}_{p}^{\mathrm{T}},(t_{i,p}^{\mathrm{AER}}, t_{i,p}^{\mathrm{DEP}})_{i\in\mathbf{V}}>\), which forms for all trains the original timetable denoted by \(\boldsymbol{TT}^{\mathrm{G}}=[TT_{p}^{\mathrm{G}}]_{p\in\mathbf{TR}}\). The timetable is not valid if, for any train run, any station exceeds its capacity \(CAP_{i}\) (see Formula (7)). For stations that do not belong to the train route, all arrival and departure times are equal 0.  
Figure 1: Illustration of a very simple railway network.
# Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment
## 2 Bacterotherapy in CRC Treatment
### Mechanisms Used in Bacterotherapy in CRC
#### 2.1.5 Bacterial Biofilms  
Biofilm is an ancient type of multicellular life, more precisely it describes a community of microbes that is either attached to a surface or encompassed in an extracellular matrix, giving bacteria resistance to drugs and hosts defense mechanisms [114; 115]. Biofilms that are, for example, found in bacterial pathogens such as _Salmonella typhimurium_, _Pseudomonas aeruginosa_ or _Staphylococcus aureus_ are responsible for causing many chronic diseases and thus play an important role in their pathogenesis [116; 117]. _Salmonella typhimurium_ and other infections have been linked to significant tumor hemorrhage. When hemorrhage is activated, T-cell production is induced as an important feature for the formation of biofilms [116]. Besides the pathogenic role of biofilms in a wide range of diseases [116; 117], their potential as a novel treatment strategy in cancer therapy has just been discovered [118]. Moreover, it has been reported that metastasis can be prevented through bacterial biofilm formation burying cancer cells during the SOS response [118]. These findings indicate bacterial biofilms being able to influence CRC formation and progression by altering cancer metabolome and regulating cellular proliferation, to have great potential as an effective anti-cancer agent in CRC treatment [118]. On top of that, previous research found evidence that the bacterial macromolecules required for biofilm formation (proteins and DNA) are helping to block cancer cells to metastasize by simply coating them [119]. Regarding metastasis, polysaccharides produced by _Streptococcus agalactiae_ have been revealed to prevent cancer cells from adhering to endothelial cells, thus blocking an essential stage of metastasis and disease progression [120]. However, the opportunities to use bacteria biofilm in the treatment of CRC, especially for metastasis distraction, need to be further investigated.
# Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network
## 4 Experimental Results and Analysis
### Network Metrics Experiment
#### 4.1.3 Node Ranking Monotonicity Analysis  
For the importance ranking indicator of nodes, the finer the granularity of the indicator, the easier it is to distinguish the variability among nodes. [PERSON] [33] defined the ranking monotonicity indicator Monotonicity \((i)\) as follows:  
\[\text{Monotonicity}_{p}(i)=\left[1-\frac{\sum_{i\in I}N_{i}(N_{i}-1)}{N_{p} \big{(}N_{p}-1\big{)}}\right]^{2} \tag{16}\]  
In Equation (16), \(N_{i}\) represents the number of nodes with the same indicator evaluation score; \(N_{p}\) represents the number of candidate nodes selected from the network with a ratio of \(p\) for ranking; and Monotonicity \((i)\) fluctuates in the range \([0,1]\), which represents the proportion of nodes with the same index value to the set of candidate points. The higher the monotonicity of the node importance index in the network, the more accurate the identification of the importance of different nodes; conversely, the lower the monotonicity of the node ranking, the poorer the granularity of the metric for identifying different nodes. We chose \(p=1\), i.e., the monotonicity was calculated separately for the whole network of the Chesapeake Bay network and the contiguous USA network. The results of the monotonicity calculation are shown in Figure 5. According to the simulation results, it can be found that the monotonicity of the node importance ranking of MCNDI is closer to 1 in both networks, so in comparison with the single indicator, the integrated indicator MCNDI has better monotonicity, which tends to improve the accuracy of the node importance ranking. It should be noted here that the monotonicity may not necessarily remain 1 for the multi-attribute composite indicator because there may be structurally symmetric nodes in the real network.
# The Effects of Augmented Reality Companion on User Engagement in Energy Management Mobile App
## 4 Results
### Overall Experience after Three Months of Use  
Figure 8 represents the result of the overall experience and impression of the PHOENIX solution on the mobile app with TUI, answered by the participants in Group B. Each question's (Appendix A) mean with bars, and the mean range is depicted with different color intensities. For example, a bar with a mean over four is colored dark purple, whereas a mean between three and four is colored with a normal purple. A mean lower than three is filled with a light purple. We identified that the participants were highly interested in optimizing energy consumption to obtain energy cost reduction or other benefits (Q5-Q7 in Figure 8, M = 4.27). Instead of manual management, they preferred that the system support auto-management of indoor conditions based on personal configuration (Q12 and Q13, M = 3.90). The participants answered with an average score of 3.33 for the questions regarding the overall experience of the mobile app with the TUI (Q14 and Q16), the usefulness of the notification messages (Q4, Q10, and Q11), and the influence of energy management on their quality of comfort (Q1, Q3, and Q9). We found three questions received less than an average score of 3, such as a noticeable energy cost reduction (Q2, M = 1.80), an improvement in indoor air quality (Q8, M = 2.60), and frequent use of the mobile app (Q15, M = 2.00).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{\(M_{d}\)} & \multirow{2}{*}{\(SD_{d}\)} & \multirow{2}{*}{SE} & \multirow{2}{*}{\(p\)} & \multirow{2}{*}{\(t\)} & \multicolumn{2}{c}{**95\% CI**} \\ \cline{5-6}  & & & & & **Lower** & **Upper** \\ \hline SUS & \(-6.04\) & \(12.60\) & \(2.57\) & \(0.01\) & \(-2.35\) & \(-10.45\) & \(-1.64\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The result of the paired sample \(t\)-test (right-tailed) of Group A: mean difference (\(M_{d}\)), standard deviation for the differences (\(SD_{d}\)), standard error mean for the differences (SE), \(p\)-value (\(p\)), test statistic (\(t\)), and 95% of confidence interval of the difference (CI) of SUS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Participant**} & \multicolumn{2}{c}{**SUS Score**} \\ \cline{2-3}  & **TUI** & **ARUI** \\ \hline P25 & 60.00 & 70.00 \\ P26 & 77.50 & 65.00 \\ P27 & 97.50 & 95.00 \\ P28 & 82.50 & 75.00 \\ P29 & 27.50 & 55.00 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The system usability scale (SUS) score of the participants in Group B for the TUI and ARUI.