esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Bard**} & \multicolumn{2}{c}{**ChatGPT**} & \multicolumn{2}{c}{**Llama**} & \multicolumn{2}{c}{**QUAL**} & \multicolumn{2}{c}{**ANOVA**} \\  & \multicolumn{2}{c}{**M (SD)**} & \multicolumn{2}{c}{**M (SD)**} & \multicolumn{2}{c}{**M (SD)**} & \multicolumn{2}{c}{**F**} & \multicolumn{2}{c}{\(\eta_{\text{p}}\)\({}^{2}\)**} \\ \hline
**Dietitian** & & & & & & & & \\ Clarity & 5.86 & (0.82) & 5.81 & (0.89) & 6.20 & (0.76) & 5.84 & (0.91) & & \\ Completeness & 5.39 & (1.15) & 5.30 & (1.17) & 5.75 & (0.91) & 5.36 & (1.10) & & \\ Consistency & 5.68 & (0.96) & 5.57 & (1.10) & 5.89 & (0.82) & 5.57 & (0.91) & & \\ Credibility & 5.14 & (1.05) & 5.12 & (1.20) & 5.48 & (1.08) & 4.90 & (1.25) & & \\ Empathy & 4.60 & (1.28) & 4.67 & (1.28) & 4.97 & (1.19) & 4.51 & (1.33) & & \\ Likability & 5.09 & (1.23) & 5.09 & (1.30) & 5.52 & (1.05) & 4.90 & (1.18) & & \\
**Patient** & & & & & & & & & \\ Clarity & 6.25 & (0.59) & 5.79 & (0.98) & 6.10 & (0.78) & 5.87 & (0.86) & 4283.722 *** & 0.048 \\ Completeness & 5.73 & (0.80) & 5.35 & (1.09) & 5.72 & (0.98) & 5.56 & (0.91) & 2200.721 *** & 0.026 \\ Consistency & 5.80 & (0.92) & 5.66 & (0.95) & 5.75 & (0.104) & 5.51 & (1.23) & 960.804 *** & 0.011 \\ Credibility & 5.68 & (1.04) & 5.29 & (1.03) & 5.71 & (0.97) & 5.31 & (1.21) & 2926.731 *** & 0.034 \\ Empathy & 5.18 & (1.10) & 4.76 & (1.08) & 5.02 & (1.20) & 4.56 & (1.34) & 3491.944 *** & 0.040 \\ Likability & 5.20 & (1.15) & 4.83 & (1.04) & 5.05 & (1.21) & 4.50 & (1.37) & 4142.605 *** & 0.047 \\
**Pediatric** & & & & & & & & & \\ Clarity & 5.94 & (0.97) & 5.85 & (0.94) & 5.99 & (0.76) & 5.96 & (1.02) & & \\ Completeness & 5.58 & (0.94) & 5.58 & (0.99) & 5.46 & (0.98) & 5.80 & (0.80) & & \\ Consistency & 5.72 & (0.94) & 5.68 & (0.94) & 5.72 & (0.82) & 5.87 & (0.84) & & \\ Credibility & 5.27 & (1.04) & 5.33 & (1.06) & 5.43 & (0.93) & 5.66 & (0.84) & & \\ Empathy & 4.93 & (1.25) & 4.68 & (1.29) & 4.86 & (1.25) & 5.03 & (1.16) & & \\ Likability & 5.37 & (1.04) & 5.11 & (1.19) & 5.23 & (1.12) & 5.52 & (1.02) & & \\ \hline \hline \end{tabular} Notes. QUAL = qualitatively developed Persona description. \({}^{a}\) For all ANOVA comparisons within the Patient Persona, F(3, 252248). *** \(p<0.001\). ANOVA results are not reported for Dietitian and Pediatric Personas, as ANOVA results did not reveal significant effects.  
\end{table} 
Table 2: Descriptive statistics and post-hoc differences among the perception of Personas’ dimensions in the clinical context.
## 3 Results
### Simulated Results of Topographic DOI Method  
The optical properties were recovered for every grid point measured using the homogenous model. Following this, the properties were spatially interpolated to generate 2D color maps from which ROIs were manually selected for the inclusion and substrate to quantify the properties for each wavelength. An example of the absorption map at 650 nm and the ROI selection is shown in Figure 7 below. The grid points have been overlaid on the figure. Figure 8a,b shows a bar graph representation comparing the true values, hybrid DOI method recovery, and conventional topographic DOI recovery at a single wavelength (650 nm) for the multiple tumor depths (0-10 mm). The simulations were performed for five repetitions in order to observe the effects of the noise. The error bars represent the standard deviation in the recovered results over five repetitions. We investigated the variation in standard deviation up to 10 repetitions and considered five iterations to be a representative number required to estimate the variation in the results. Figure 8c,d shows the corresponding bar graph plots for \(\mu_{s}^{\prime}\) at 650 nm. The absolute differences and percentage differences of the hybrid DOI and conventional topographic DOI recovered \(\mu_{a}\) and \(\mu_{s}\)' from the true \(\mu_{a}\) and \(\mu_{s}\)' have been summarized in Table 2 for all tumor depths. Figure 6: Ground truth (row 1) versus simulated recovery (row 2) in \(\mu_{a}\) (650 nm) at different tumor depths (columns). Figure 7: Spatially interpolated topographic DOI recovered absorption at 650 nm with region of interest (ROI) selection for tumor and normal regions.
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 5 Performance Evaluation
Figure 5a,b show the effect on the network's stability and latency of various data frame sizes. The figures reveal that the 2 PRACH procedure works well for both; reliability and end-to-end latency relative to existing UD-based RACH when considering the different types of data frame sizes. However, the influence of data frame interarrival rate has fewer effects on the network's stability and latency efficiency, as seen in Figure 6a,b, respectively. The importance of using a Poisson process-based distribution is evident from the figures (that is, Figures 4-6). The main purpose of the proposed 2 PRACH for the RA procedure is to enable the UEs in the network to carry out their initialization of the association more effectively, where reliability is accomplished by reducing network collisions. Figure 4: Performance comparison of process-based random access channel (2 PRACH) with existing random access channel (RACH) and contention-resolution-based RACH (CRB-RACH) procedures with the varying number of contending UEs, where (**a**) network reliability (%), and (**b**) end-to-end latency (ms).
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 1 Introduction
Due to their porosity, nanomaterials, such as nano-sized zeolites, metal, and metal oxide nanoparticles, metal-organic frameworks (MOFs), covalent organic frameworks (COPs) promisingly increase the efficiency of CO\({}_{2}\) absorption [26]. This article is a comprehensive review of the summary of the last advances in nanofluids in increasing CO\({}_{2}\) absorption. In addition to describing nanofluids, properties, and applications, this study also elaborately introduces the factors contributing to increasing CO\({}_{2}\) absorption. In the following, the nanofluid preparation methods consisting of one-step and two-step methods will be explained. Afterward, nanofluid stability, mechanisms of increasing mass transfer in nanofluids (shuttle effect, bubble breakage effect, and micro convection), and the factors affecting CO\({}_{2}\) absorption through the nanofluid will be discussed and summarized. Ultimately, based on the base liquid, nanofluids will be categorized into three categories (water, amine, and methanol), and a conclusion and viewpoint in reaching favorable CO\({}_{2}\) absorption in nanotechnology will be provided. By describing the application of nanofluid in the CO\({}_{2}\) absorption field, this article evaluates and helps understand this novel technology and compares it with conventional methods.
# Trends from 30-Year Observations of Downward Solar Irradiance in Thessaloniki, Greece
## 2 Data and Methodology
### Aggregation of Data and Statistical Approach  
In order to investigate the SDR trends that are the main focus of the study, we implemented an aggregation scheme to the one-minute data to derive series in coarser time scales. To preserve the representativeness of the data, we used the following criteria: (a) we excluded all days with less than 50% of the expected daytime measurements, (b) daily means for the clear-sky and cloudy-sky datasets were calculated only for days with more than 60% of the expected daytime measurements identified as clear or cloudy, respectively, and (c) monthly means were computed from daily means. For the all-skies dataset, monthly means were computed only when at least 20 days were available. Seasonal means were derived by averaging the monthly mean values in each season (winter: December-February, spring: March-May, etc.). The daily and monthly climatological means were derived by averaging the data for each day of the year and calendar month, respectively. The daily and monthly datasets were deseasonalized by subtracting the corresponding climatological annual cycle (daily or monthly) from the actual data. Finally, to estimate the SZA effect on the SDR trends, the one-minute data were aggregated in 1\({}^{\circ}\) SZA bins, separately for the morning and afternoon hours.  
The linear trends were calculated using a first-order autoregressive model with lag of 1 day using the'maximum likelihood' fitting method [26; 27] by implementing the function 'arima' from the library'stats' of the R programming language [25]. The trends were reported together with the 2\(\sigma\) errors.
## 2 Materials and Methods
### VFCs Analysis  
The sample analysis of VFCs was performed by using HS-SPME-GC-MS analysis. The PDMS/DVB/65 \(\upmu\)m fiber was headed into the headspace of a 20 mL glass vial containing 2 g respective of sample sealed with a Teflon cover and kept at 80 \({}^{\circ}\)C for 30 min. It was then desorbed for 5 min at 280 \({}^{\circ}\)C into the GC inlet with the automatic auto-sampler. Volatile components analysis was carried out by using a GC-MS (Agilent Technologies Inc., 7000 D, Santa Clara, CA, USA) equipped with an HP-5 MS Ultra Inert column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA) attached to a mass spectrometer. The GC operation conditions were as follows: an inlet temperature of 280 \({}^{\circ}\)C, split ratio of 5:1, Helium (purity: 99.999%) carrier gas flow of 1 mL/min. The oven temperature setting was as follows: 50 \({}^{\circ}\)C (2 min), 10 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C, 5 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C (2 min), 20 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C (5 min). The ion energy for the electron impact (EI) was kept at 70 eV. The chromatograms were recorded by monitoring the total ion currents in the 40-300 mass range. The VFCs in samples were identified by matching the mass spectra and retention index (RI) with the National Institute of Standards and Technology (NIST) library database (Nist2008/Wiley275) and the retention index with relevant references were compared. The compounds with a matching degree greater than 80% were extracted and analyzed. The peak area normalization method was used to calculate the relative content of each component.  
The Relative odor activity value (ROAV) was used to study the characteristic VFCs and evaluate their contributions to the whole flavor perception [12]. The ROAV was calculated based on odor activity value (OAV) according to the following formula  
\[\text{ROAV}_{\text{i}}=100\;(\text{C}_{\text{i}}/\text{C}_{\text{max}})\;( \text{T}_{\text{max}}/\text{T}_{\text{i}}), \tag{1}\]  
where \(\text{T}_{\text{i}}\) and \(\text{C}_{\text{i}}\) are the threshold and percentage of an arbitrary flavor component; \(\text{T}_{\text{max}}\) and \(\text{C}_{\text{max}}\) are the threshold and percentage of the volatile component with the highest OAV, respectively. The substance with \(\text{ROAV}\geq 1\) is divided into the key flavor compounds of the analyzed samples.
## 2 Materials and Methods
### Methods
#### 2.3.2 Validation of GPP and ANPP  
The EC-LUE model was refined to enhance the accuracy of GPP assessments, consequently improving the precision of ANPP estimations. In the EC-LUE model used for the GLASS GPP product, average temperature data were derived from Modern Era Retrospective Analysis for Research and Applications (MERRA) with a spatial resolution of 0.5\({}^{\circ}\)[40]. However, our study employed average temperature data from ERA-LAND, offering superior spatial resolution compared to MERRA's temperature data.  
However, as the actual field data cannot directly calculate the GPP values, it is not feasible to determine the accuracy of GPP directly from field data. Therefore, we first compared the calculated GPP values with the product GPP values to ensure the rationality of the calculated GPP. Subsequently, we converted the biomass observed in the field plots to obtain the NPP of the sampled area. This ANPP was then compared with both the calculated ANPP and the product ANPP to evaluate the accuracy of ANPP, thereby ascertaining the accuracy of GPP. The field-derived NPP used to verify and enhance the precision of the EC-LUE model was sourced from field survey data within the study area. Twelve sites were surveyed, with four 1 m \(\times\) 1 m plots established at each site. The above-ground parts were harvested and dried in a constant temperature oven until a constant dry weight was obtained, following which the carbon content was measured.  
By comparing the calculated GPP with the product GPP, an R\({}^{2}\) value of 0.854 was achieved (see Figure 2), demonstrating the reasonableness of the calculated GPP for estimating ANPP. Next, the ANPP derived from GPP calculations was compared with the NPP obtained from field sites, yielding an R\({}^{2}\) of 0.791 (Figure 3a). In contrast, comparing the product NPP with field-derived NPP results gave an R\({}^{2}\) of only 0.768 (Figure 3b). Therefore, it can be inferred that the optimized EC-LUE model provides a more accurate simulation of ANPP than the product NPP.  
Figure 2: GPP data simulated using the EC-LUE model and product GPP data.
## 3 New GTR and GBR Using Tissue Engineering
### Clinical Application of 3D scaffolds
#### 3.5.2 Scaffolds for GBR  
For application in socket preservation, and alveolar bone regeneration and augmentation, scaffolds made of bioceramics combined with mechanically strong materials are recommended. In a load-free area, collagen can be the option for mixing [100]. The scaffold should degrade within five to six months for the ideal tissue apposition, and maturation and mechanical strength should be adequate to avoid the collapse of operation sites [71]. PCL is the most common scaffold due to its high mechanical strength and a variety of manufacture techniques. However, its delayed degradation proved to inhibit the osteoconduction, and acidic byproducts can expose the flap earlier and lead to failure of the regeneration process [91; 94]. The use of PCL alone is not recommended, and it should preferably be combined with bioceramics such as HA for controlling the degradation rate and neutralization of the byproduct [97]. The combination of collagen and HA is encouraged for GBR due to the compositional similarities to native bone and reasonable degradation rates [53]. Bioceramics can be applied mainly in sinus elevation and bone augmentation procedures. The scaffold replaces the need for membranes and provides better mechanical support, such as the application of 3D-printed BCP scaffolds (HA 30%, \(\beta\)-TCP 60%, and \(\alpha\)-TCP 10%) grafted in sinus, which showed favorable outcomes [119]. When combined with bioceramics, the increase in degradation rate regarding the percentage of its composition should also be considered.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 2 Related Work
### Multiple Signal Classification (MUSIC)  
Multiple Signal Classification (MUSIC) algorithm was proposed by [PERSON] in 1979. MUSIC algorithm provides asymptotically unbiased estimates of  
1. number of incident wavefronts present;
2. directions of arrival;
3. strengths and cross correlations among the incident waveforms;
4. noise/interference strength.  
Due to the reliable results and anti-multipath characteristic of this approach, MUSIC algorithm is very general and can be applied widely in signal processing. Much advanced research of indoor localization is based on the MUSIC algorithm.
# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 10 Future Directions  
### Information Fusion and Trust  
Conducting a reliable information fusion is an open field of research [36; 92]. A data source might be dishonest, the fusion centre might be untrustworthy affecting the reliability of the fusion process itself, or the fusion center might act dishonestly affecting the fused data. As such, we need to consider trust in the data fusion notion. How to rely on data fusion processes would be a new area of research that unfortunately did not receive much attention from the research community. Furthermore, modeling trust should provide incentives to sensor nodes to participate as either data sources or fusion centers as long as they act in a trustworthy and honest manner.  
Information fusion processes are being integrated into distributed systems as well as system-of-systems. Studies such as [93] apply a system-of-systems engineering process to obtain integrated architectures of information fusion systems. Distributed trust management systems have also been explored by many researchers [11; 88; 94]. A future direction is to apply a system-of-systems engineering process to obtain integrated architectures of trust-based information fusion systems.
# E-Mail Network Patterns and Body Language Predict Risk-Taking Attitude
## 2 Data and Methods
### Study A: E-Mail Analysis
#### 2.2.2 Structure Variables  
We employed the dynamic social network analysis tool Condor [18] to calculate the following three kinds of structure variables: Position, contribution, and dynamic signals.  
Position signals  
A static e-mail social network is reconstructed using the filtered 912 nodes (users) and 8074 unique links (the exchange of at least one e-mail between two nodes). The position of a given node in the network can be characterized by its nodal centrality, which has been found to be useful in predicting productivity [35], job turnover [36], and individual performance [37]. In particular, we consider three well-known nodal centralities: (1) Degree centrality, i.e., the number of links connected to it, (2) betweenness centrality [38], measuring the probability with which a node is on the shortest path between other nodes, and (3) closeness centrality [39], measuring the mean distance from a node to other nodes. Apart from the nodal centrality, we also calculate reach-2 as a proxy for individual social capital [40], which is the number of other nodes a given node can reach with two steps.  
* Contribution signals  
The amount of information contributed by an individual in the e-mail network is calculated as the number of e-mails sent and received by the individual. Hence, we use the contribution index metric [18] defined as the balance of messages sent and received by a user during the study period. It is calculated by subtracting messages received from the messages sent and then dividing the result by the messages sent added to the messages received [18].  
* Dynamic signals  
E-mail communication is generally a dynamic process, implying that an individual's position in the network may change continuously over time. To allow for this, we reconstruct day-by-day networks by merely including the communications (links) happening on that particular day, leading to e-mail social networks per day. Based on these networks, we calculate individual betweenness oscillation [18] by quantifying the number of local maxima and minima in the betweenness centrality curve. We also calculate the contribution index oscillation in a similar way. These metrics' utility have been validated repeatedly [41; 42]. In addition, we investigate individual responsiveness in e-mail communication, i.e., how fast a responder replies to a sender, which plays an important role in dynamic communications analysis [43; 44]. Specifically, for each user we consider two different variables, the Average Response Time (ART) defined as the average time needed for the given user to respond to an e-mail, and the nudges defined as the average number of pings (e-mails sent) needed to get a response [18].
## 3 Open-Area Environmental Interference Suppression Model
### Hybrid Segmentation Algorithm
The most popular connected component analysis algorithms include the two-pass method [14] and seed-filling method [22]. Usually, the object processed by connectivity analysis is a binary image. There are basic algorithms and improved algorithms for connected component analysis. The most common algorithms are as follows: (1) the two-pass method and (2) the seed-filling method. The two-pass method can find and mark all the connected areas in the image by scanning the image twice. The specific steps are as follows: In the first scan, a label is assigned to each pixel position. In the scanning process, the pixel set in the same connected region may be assigned one or more different labels. Therefore, these labels belonging to the same connected region but with different values need to be merged; that is, the equal relationship between them should be recorded. In the second scan, the pixels labeled with equality are grouped into a connected component and assigned the same label (usually the minimum of equal labels). The idea of the seed-filling algorithm is as follows: A foreground pixel is selected as a seed, and the foreground pixels adjacent to the seed are merged into the same pixel set according to the two basic conditions of the connected area (pixels with the same value and adjacent locations). The resulting pixel set is a connected area. Because the two-pass method is more simple, stable, and much faster than that of the seed-filling algorithm, the two-pass method is chosen for signal segmentation.
# An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network
## 5 Conclusions  
In the in-vehicle smartphone navigation scenario, as RTK is prone to drift or jump in complex environments and the DR heading angle is prone to divergence, this paper proposes an RTK/DR positioning method that integrates OSM road network information. RTK/DR uses standard Kalman filter estimation, and there are two main points of optimisation in the positioning process. The first point of optimisation is to use the DR heading angle change in order to assess the turning situation, obtain the OSM road line segment through map matching and, finally, use the direction of the matched road to correct the heading angle. The second point of optimisation is to combine the covariance matrix of RTK positioning, vehicle motion status, and RTK heading angle change information in order to automatically determine the reliability of RTK positioning results and stop measurement updates when the positioning results significantly jump.  
Two smartphones were used, i.e., Huawei P30 and P40, and four positioning tests were carried out in order to verify the proposed algorithm. From the test results, it was possible to deduce that the accuracy of the smartphone DR heading angle is highly improved after the OSM road network correction: in the eight sets of test results, the smartphone heading angle accuracy is better than that of RTK, and the improvement rate of six sets of tests is higher than 15%. Thanks to the heading angle correction and measurement update switch, the RTK/DR described in this paper is better than RTK in both lateral andhorizontal accuracy. The lateral accuracy of P40 is improved by more than 20%, and the horizontal accuracy is improved by more than 10%. As the DR velocity update cannot remove the dependence on RTK positioning, the forward positioning accuracy slightly decreases. Future research will attempt to solve this problem.  
Conceptualization, C.G. and F.W.; methodology, F.W.; software, F.W.; validation, F.W.; formal analysis, F.W.; investigation, F.W.; resources, C.G. and F.W.; data curation, C.G. and F.W.; writing--original draft preparation, F.W.; writing--review and editing, C.G., F.W., R.Z. and R.S.; visualization, F.W., Q.L. and L.G.; supervision, C.G. and F.W.; project administration, C.G., F.W. and J.W.; funding acquisition, C.G., F.W. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education--China Mobile Research Fund (MCM2020001).  
The authors thank the IGS data center of Wuhan University for its support of real-time precise ephemeris.  
The authors declare no conflict of interest.