esa-sceva/satcom-chunk-collection huggingface dataset:
# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
## 4 Experiment
### Participants  
We originally recruited a total of 220 participants through Amazon Mechanical Turk (MTurk). We conducted power analysis on correlation test, and we derived the total number of participants needed as 123 (\(\alpha=0.05\), \(\beta=0.2\), \(r=0.3\)). We also expected some dropouts, outliers, and bad turkers because of the nature of online studies [47]. Thus, we decided to initially recruit 220 participants. We recruited crowdsourced workers who had a total of 100 or more approved HITs (Human Intelligence Tasks, unit used for tasks on MTurk) and a 95% or greater HIT approval rate only from the United States. MTurk has become increasingly popular as an online experiment platform, and it has clear strengths and weaknesses [48; 49]. For example, while MTurk allows researchers to recruit a large number of diverse participants in a relatively in a short amount of time and at low costs, researchers cannot control participants and their work environment as in controlled lab studies. However, MTurk can be reasonably used for experiments using objective instruments [49; 50].  
We intentionally recruited crowdsourced workers who were native English speakers because the measures used in this study were written in English, and using a non-native language might prevent smooth thought processes and lead to poor performance [51; 52]. We also tried to recruit crowdsourced workers who were not color blind because the measures of visualization literacy did not use color-blind-safe colors. So we ruled out one participant who was a self-reported non-native English speaker and one participant who was self-reported color blind. Out of the 218 participants, we also ruled out 32 participants who failed to complete the tasks as instructed. In addition, we considered the following participants as random clickers and discarded their responses: (1) three participants who answered more than eight items in the measure of visualization literacy in under five seconds, and (2) five participants who provided inappropriate answers to the following question in the measure of visualization literacy: \"In the test that you took, can you recall what the data visualizations were about (e.g., hotel, bicycle, or airfare)? Please type the context as your memory serves.\" Note that we included three filtering questions asking to retrieve a value from a simple seven by six table in the experiment; however, all participants went through the questions well.  
As a result, a total of 178 participants remained. The remaining participants were 85 females and 93 males with the self reported ages ranging from 21 to 69 (\(M=35.02\), \(SD=10.39\)). Everybody had an education level of high school or above. Of the participants, 43% of the participants had a bachelor's degree and 8% of the participants had a master's or a doctoral degree.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 4 Map Construction and Test  
### Overview of Mapping Scheme  
Although the acquired pose can provide a priori information for the moving of the roadheader, its navigation also depends on the environment map. Based on SLAM technology, a roadway map is established, which can help the roadheader identify obstacles and judge roadway edge shapes, and provide support for remote man-machine interactions, roadheader travel prediction, section forming control, and so on.  
Common mapping schemes are shown in Table 1, including laser-based and vision-based schemes, and the real triangle symbol indicates that the algorithm supports the relevant interface. It can be seen from Table 1 that the RTAB-MAP algorithm can provide a variety of sensor input interfaces, and can output a dense 3D point cloud map, which is currently an excellent application-based mapping solution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mapping Algorithm**} & \multicolumn{4}{c}{**Input**} & \multicolumn{4}{c}{**Output**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Camera**} & \multicolumn{2}{c}{**Laser Radar**} & \multicolumn{2}{c}{**Odometer**} & \multicolumn{2}{c}{**Grid Map**} & \multicolumn{2}{c}{**Feature/Point Cloud Map**} \\ \cline{2-10}  & **Monocular** & **Binocular** & **RGB-D** & **2D** & **3D** & & **2D** & **3D** & **Sparse** & **Dense** \\ \hline Gmapping [35] & & & & & \(\mathbf{\Delta}\) & & & \(\mathbf{\Delta}\) & & & \\ Cartographic [36] & & & & & \(\mathbf{\Delta}\) & & \(\mathbf{\Delta}\) & & & \\ Kartor [37] & & & & & \(\mathbf{\Delta}\) & & \(\mathbf{\Delta}\) & & & \\ Hector [38] & & & & & \(\mathbf{\Delta}\) & & & & & \\ ORB-SLAM [39] & \(\mathbf{\Delta}\) & & & & & & & & & \\ ORB-SLAM2 [40] & \(\mathbf{\Delta}\) & & & & & & & & & \\ RTAB-MAP [19] & & & \(\mathbf{\Delta}\) & & \(\mathbf{\Delta}\) & & \(\mathbf{\Delta}\) & & \(\mathbf{\Delta}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common mapping schemes of laser/visual SLAM [19].  
Comprehensively considering the underground environment of a coal mine as well as the existing conditions of a laboratory, a long corridor is adopted to simulate an underground roadway, and some mapping algorithms, listed in Table 1, are tested. The long corridor used for test, as shown in Figure 7, is as close as possible to the dimly lit, narrow and closed roadway environment.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 6 Network Solutions at Data-Center Back-Ends to Meet Performance Requirements of 5G
### Offloading Applications to the NIC  
To mitigate the overwhelmed CPU usage of low latency network solutions, some functions had been offloaded to the network hardware. Initially, FPGAs had been utilized for these tasks; however, most of the developers are not familiar with hardware description languages (HDLs). Therefore, in [65], a solution is introduced that implements network functions by using a C-like language that are compiled into low-level HDLs for FPGAs.  
On the other hand, in [66], the accelerated networking of Microsoft Azure is introduced, implemented on top of FPGA-based NICs, reaching a consistent <15 \(\upmu\)s VM-to-VM TCP latency and 32 Gbps throughput. However, by using FPGAs only stateless utilities can be implemented; nevertheless, the need has increased for solutions supporting stateful applications onto the network hardware. For this reason, the use of smart NICs are getting more and more relevant. Smart NICs do not have enough compute resources to run large monolithic applications; however, according to the trend, virtualization technologies running in cloud environments are getting more and more granular. Therefore developers can implement highly distributed services of small components. Exploiting this Smart NICs, can cope with particular building blocks of a higher level service.  
According to [67], by offloading microservices to smart NICs, the energy efficiency of the compute cluster could be improved by 3\(\times\) and the cost efficiency up to 1.9\(\times\), by sacrificing only up to 4% latency for common microservices.  
However, a recent research [68] revisited the field of smart NICs by comparing four commodity smart NICs by examining their offloading performance. The authors also implemented a framework--iPipe--for offloading distributed applications onto the network hardware, of which core implements a scheduler that is able to schedule tasks between host CPU cores and the smart NIC's cores. By using iPipe, one can achieve considerable CPU and latency savings.
## 2 Materials and Methods
### Identification and Normalization of Volatile Components  
The identification of VFCs was achieved by comparing Kovats linear retention indices (RIs) and EI mass spectra to those of authentic reference standards, whenever possible, or by using mass spectra databases (NIST library) and by comparing the experimental RI values to those previously reported in the literature [10; 14; 16]. The RIs were determined by injection of a solution containing the homologous series of normal alkanes (C8-C40). The relative content (%) of the VFCs was investigated with a TIC chromatogram by using the following equation:  
Relative content (%) = single component area/total area \(\times\) 100  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound** & **Molecular Mass** &
\begin{tabular}{c} **Selected Ions** \\ **(Qualifier/Quantifier)** \\ \end{tabular} \\ \hline
2,4-heptadiene1 & 110.1 & 110.1/81.0 \\ \hline \(\beta\)-damascenone & 192.3 & 192.3/69.2 \\ \hline Linalool & 154.2 & 136.2/71.1 \\ \hline Indole & 117.1 & 117.1/90.0 \\ \hline
2-ethyl-6-methyl-pyrazine & 122.2 & 121.2/94.1 \\ \hline
2-ethyl-5-methyl-pyrazine & 122,2 & 121.2/56.1 \\ \hline
2-ethyl-3,6-dimethyl-pirazine & 136.2 & 135.2/108.1 \\ \hline
2-ethyl-3,5-dimethyl-pyrazine & 136.2 & 135.2/108.1 \\ \hline
2-acetyl-pyrazine & 122.1 & 122.1/43.0 \\ \hline linalool-d5 & 159.23 & 141.3/74.2 \\ \hline hexanal-d12 & 112.2 & 92.2/47.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: VFC names, molecular mass and qualifier and quantifier ions selected for quantitative analysis in SIM mode.  
The results obtained in green teas and roasted tea (180 \({}^{\circ}\)C, considered as an intermediate temperature among those used) were used to select most abundant VFCs whose percentage significantly varied after roasting.
# Na-Doping Effects on Thermoelectric Properties of Cu\({}_{2-x}\)Se Nanoplates
## 3 Results and Discussion
Regardless of the exact mechanism, Na appears to play a role in lowering the lattice thermal conductivity. According to the above measured results of electrical conductivity, the Seebeck coefficient and the thermal conductivity, the dimensionless figure of merit \(ZT\) can be calculated using the formula \(ZT\) = \(S^{2}\sigma T/\kappa\), as shown in Figure 6. It can be seen that the \(ZT\) values of all of the samples were  
Figure 5: Temperature dependent thermoelectric properties of nanowire, nanoplate and Na-doped nanoplate Cu\({}_{2-\chi}\)Se samples: (**a**) electrical conductivity; (**b**) Seebeck coefficient; (**c**) Power factor; (**d**) thermal conductivity along with the lattice contribution to the thermal conductivities. Solid dots are experimental data in this work, while open dots are bulk Cu\({}_{1.75}\)Se samples from reference [34]. increased with the increasing temperature. Benefiting from the carrier concentration optimization and low thermal conductivity, the Na-doped Cu\({}_{2-x}\)Se shows the highest \(ZT\) value among all of the samples over the entire temperature range. The maximum \(ZT\) value of 0.24 was obtained at 673 K for a Na-doped Cu\({}_{2-x}\)Se sample, which is greater than those of undoped Cu\({}_{2-x}\)Se nanowire and nanoplate. It indicates that Na doping can indeed increase the figure of merit of the Cu\({}_{2-x}\)Se nanoplates.
# Comparison of Two Detector Magnetic Systems for the Future Circular Hadron-Hadron Collider
## 4 Discussion  
In this section, we summarize the compared features of the baseline design [4] and the minimal steel yoke option of the magnetic system for the FCC-hh detector at the FCC.  
The minimal steel yoke option, simulated with the coil parameters of the baseline magnetic system, offers the following advantages:  
* 6% larger central magnetic flux density.
* 13.2% smaller compression axial force and pressure in the main coil middle plane.
* 16.2% smaller radial pressure in each forward coil.
* 12.1% shorter radial distance to the acceptable safety level of the magnetic stray field.
* 7.2% shorter axial distance to the acceptable safety level of the magnetic stray field.
* 3.2 to 8.1% larger magnetic field double integrals and, thus, better transverse momentum resolution of the charged particle registered in the inner tracker.  
The minimal steel yoke magnetic system, simulated with the coil parameters of the baseline magnetic system, has the following disadvantages:  
* 0.97% larger axial attractive force onto each forward coil.
* 9.3% lower magnetic flux density in the transition region between central and each forward coil.
* 5.8% larger stored energy in the magnetic system.
* 61.3% larger radial pressure in the main coil.
* The compression axial force to the central barrel wheel of 236.9 MN each side is 2.92 times larger than the similar axial force in the CMS magnet yoke at the 3.81 T central field.
* The attractive axial force to the yoke endcap assembly of 164 MN from each side is 2.42 times larger than the similar axial forces in the CMS magnet yoke at the 3.81 T central field.  
However, the main disadvantage of the minimal steel yoke option would be the cost of the steel yoke construction which would negate all advantages of this option. The mass of steel used in the minimal steel yoke model amounts to 22,240 tons without considering the mass of the support structure. The cost of a 10,745 ton CMS yoke was estimated in 1997 [11] at CHF 43.8 million. Based on this estimate, the cost of the minimal steel yoke option could be at least CHF 90.7 million, which is more than twice the cost of manufacturing the CMS yoke.  
Taking this cost into account, the relative gains from the minimal steel yoke design are not significant enough to warrant the additional expense. The iron-free baseline magnetic system [4] designed for the FCC-hh detector has almost the same electromagnetic features and similar charged particle momentum resolution as the simulated minimal steel yoke option.
# One-Step Synthesis of Cross-Linked Esterified Starch and Its Properties
## 3 Results and Discussion
### FT-IR Analysis of CES  
The FT-IR analysis of CES was conducted to estimate the exact changes in chemical properties (Figure 2). The significant changes of CES in FT-IR analysis were located at 1734 cm\({}^{-1}\) and 1247 cm\({}^{-1}\), which were the characteristic absorbance peaks of starch acetate. Therefore, these results demonstrated that acetate was successfully cross-linked with starch, namely, the peak at 1734 cm\(-1\) represented the vibration of C=O, and the peak at 1247 cm\({}^{-1}\) represented the vibration of C-O. As the elevated esterification ratio of starch, the stronger absorbance was detected at 1247 cm\({}^{-1}\), along with enhanced absorbance, also at 1734 cm\({}^{-1}\). Moreover, since a relationship existed between the DS value and the peak, the introduction of the acetyl radical group during the synthesis of CES also contributed to the promotion of the esterification process. However, the characteristic peak of cross-linked starch was not detected. The reason was ascribed to the low degree of cross-linking of CES [17].  
Figure 2: Infrared spectrum of the cross\(-\)linked esterified starch sample.
## 4 Discussion
### Low-Light Enhancement Algorithm  
We evaluated the proposed low-light enhancement algorithm and compared it with others, as shown in Figure 6. Figure 6a is the Queen's Road image, while Figure 6b-f present results from gamma correction [37], GFC [45], SRIE [46], MSretinex [47], and our adaptive dimming method. From Figure 6b,d, it is apparent that, while the gamma correction and SRIE methods successfully improve background brightness, their enhancements are constrained, with certain local details remaining poorly resolved. Figure 6c shows the results of the GFC method, which exhibits marginally better visual quality. The MSretinex method (Figure 6e) enhances the contrast but introduces significant color distortion. By comparison, both Figure 6c,f achieve substantial improvements. However, in Figure 6c, streetlights and billboards within the red-framed region appear blurred, lacking the contrast attained by the proposed adaptive low-light adjustment method in Figure 6f.  
We examined the impact of low-light enhancement on fusion results, as shown in Figure 7. Without enhancement (Figure 7c), the fusion suffers from poor brightness and contrast, whereas our method (Figure 7h) preserves rich details and superior visual quality. Zoomed-in comparisons of the two rectangular regions highlight the improved clarity and brightness in streetlights and billboards. Overall, the adaptive dimming algorithm effectively integrates complementary information and enhances weak textures in dark scenes.
## 3 Analyses
### Photogrammetry  
Following [PERSON] et al. [3], the setup for the photogrammetry consisted of a light tent surrounded by five LED lights on tripods, a Bluetooth controlled turntable with a custom foam stand placed over it, and photogrammetry targets printed from Agisoft Metashape 1.5.3 [19]. Twenty-five images were taken around the object per rotation, with nine rotations done at different angles for a total of 225 photos of the helmet plus one image of the background to use as a mask during model construction. A Canon EOS 7D with a Canon EF 50m f/2.5 Macro lens was used for the photographs. Colour correction was done with an X-Rite Color Checker Photo Passport 2 [20,21]. The colour checker was photographed at the start of each set-up. Raw photographs were processed as digital negatives (DNG) in ColorChecker Passport and Adobe Lightroom Classic CC with the ColorChecker Passport plugin and JPG files for photogrammetry. Agisoft Metashape 1.5.3 [19] was used for the model creation. Blank images of the workspace were used to a create masks of the helmet. The constructed sparse point cloud was edited with the following parameters: reprojection error: 0.2 px; reconstruction uncertainty: 15 px; image count: 2; projection accuracy: 2.5 px. Editing of the dense cloud was done manually with some automation based on colour where possible. A mesh was generated over the object, and a texture was created. Where required, a reflexive method of deleting stray points on the dense cloud and re-generating the mesh and the texture was conducted. The final photogrammetry model is available as part of the dataset of [PERSON] and colleagues [22] (Figure 3). Due to logistical constraints, only the exterior of the helmet was modelled, thus thickness analyses, such as those done in [PERSON] and colleagues [3], were not possible.
## 2 Cross-Domain Collaborative Task Allocation Model
### Encoder-Decoder Scheme  
Cross-domain collaborative task allocation represents a typical NP-complete combinatorial optimization problem characterized by a discrete solution space with variable intervals. The presence of numerous constraints within the model significantly hampers search efficiency and limits the applicability of the initial population. If the penalty function method is used to judge all of the constraints, and the constraint judgment factor is set to make the fitness function diverge when the constraints are not satisfied, the search efficiency will be greatly affected [37; 38]. To adapt to the constraints of metaheuristic intelligent algorithms on operators, this research designs a decision variable encoding and decoding scheme for integer programming problems. This scheme achieves a readable solution space for task allocation and equal-interval continuous encoding. This scheme was successfully applied to various metaheuristic intelligent optimization algorithms.  
Integer matrix coding can intuitively display the results of task allocation and realize the parallel calculation of various groups in the swarm intelligence algorithm. In this research, the allocation matrix was defined based on the idea of the Hungarian algorithm to realize the continuous movement of each operator and establish the initial population that is in line with the constraints through matrix coding. The updated decision matrix was guaranteed to meet the constraints. Then, each allocation matrix was transformed and decoded into a 0-1 planning allocation problem to obtain the allocation scheme. In the task allocation scenario, six surface agents \(W_{1\sim 6}\) transport twelve load agents \(M_{1\sim 12}\) to execute an attack on four target agents \(T_{1\sim 4}\), under the relay of six aerial relay control agents \(A_{1\sim 6}\). The specific coding scheme and the corresponding decoded decision matrix are presented in Figure 2 and Equation (1).  
\[x_{i}=\left\{\begin{array}{l}[x_{i1},x_{i2},\cdots,x_{ij}]^{T}=[1,1,1,3,2,1,2,2,3,4,4,4]^{T}\\ \{x_{i}(i,j)\}_{d\times c^{\prime}}=\left[\begin{array}{cccccccccccccc}1&1& 1&0&0&1&0&0&0&0&0&0\\ 0&0&0&0&1&0&1&1&0&0&0\\ 0&0&0&1&0&0&0&1&0&0&0\\ 0&0&0&0&0&0&0&0&1&1&1\end{array}\right]\\ y_{i}=\left\{\begin{array}{l}[y_{i1},y_{i2},\cdots,y_{ik}]^{T}=[3,1,2,2,3, 1,5,4,4,6,5,6]^{T}\\ \{y_{i}(i,k)\}_{d\times b}=\left[\begin{array}{cccccccccccccc}0&1&0&0&0&1&0& 0&0&0&0&0\\ 0&0&1&1&0&0&0&0&0&0&0\\ 1&0&0&0&1&0&0&0&0&0&0\\ 0&0&0&0&0&0&0&1&1&0&0\\ 0&0&0&0&0&0&1&0&0&1&0\\ 0&0&0&0&0&0&0&0&1&0&1\end{array}\right]\end{array}\right. \tag{1}\]  
where \(i=1,2,\cdots,d;j=1,2,\cdots,c;k=1,2,\cdots,b;x_{i}\) and \(y_{i}\) denote the \(i\)th target allocation or control allocation scheme; \(x_{ij}\) and \(y_{ik}\) represent the coding value corresponding to the \(i\)th load agent in the \(j\)th allocation scheme; \(x_{i}(i,j)\) and \(y_{i}(i,k)\) indicate the decision variable values corresponding to the decision pairs \(\left\{M_{i},T_{j}\right\}\) and \(\left\{M_{i},A_{k}\right\}\) in the \(i\)th allocation scheme, respectively.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Models and Methods for Sound Transmission Loss Calculation
#### 4.1.6 Biot's Theory and Equivalent Fluid Method  
Periodic structural links when applied appropriately between the sandwich panels are very effective in evaluating the STL at low and mid frequency regions. Various models for periodic structural links have been examined by [PERSON] and [PERSON] [111]. Moreover, the introduction of porous media or poroelastic materials in between these structural links of sandwich and composite panels can enhance sound reduction in the mid and high frequency regions. Two basic important theories have been used to model these poroelastic materials; they are the [PERSON]'s theory [112; 113; 114; 115] and the equivalent fluid method (EFM) [36; 116; 117]. While the [PERSON]'s theory assumes an elastic frame in the porous media such that there exist two dilatational or longitudinal (i.e., compressive) waves and one rotational (i.e., shear) wave; the EFM assumes that the porous media is equivalent to a fluid medium having density and bulk modulus. A comparison between these two approaches for fiberglass and foam cores werestudied by [PERSON] and [PERSON] [118]. In their analyses, the two approaches not only represented well the STL but also gave similar predictions.
## 3 PV-Grid Tied Charging Stations for EVs and PHEVs
### Operating Modes
#### 3.2.1 Mode 1 (PV to Battery)  
This operating mode works when the PV array provides enough energy to completely charge the EV. In this way, the MPP is obtained through the DC-DC converter on the PV stage, and it is transferred to the battery by the DC-DC converter of the DC charger stage. The main purpose of this converter is to regulate the voltage from the common DC bus to suit the charging parameters of the EV battery pack. When operating in this mode, the grid is electrically disconnected from the EV charging system. The charging by PV only mode is shown in Figure 5a.