esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Can Coatings Analysis via SPME  
The compounds detected after the extraction with SPME are summarized in Table 4. Only compounds with appropriate direct matching factors (SI) and reverse search matching (RSI) are included. For those compounds whose identification was not achievable, the most abundant \(m/z\) is specified. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Tr/min** & **Compound** & **CAS** & **Fl No.** & **SI** & **RSI** & **Sample(s)** & **TC** \\ \hline
9.61 & Propylene glycol & 57-55-6 & & 571 & 818 & BC04 & 1 \\ \hline
13.72 & 2-Butoxyethanol * & 111-76-2 & 02.242 & 866 & 925 & BC01-BC10 & 1 \\ \hline
15.37 & \(\alpha\)-Terpinene & 99-86-5 & 01.019 & 586 & 754 & BC09 & 1 \\ \hline
15.61 & Benzaldehyde & 100-52-7 & 05.013 & 631 & 865 & BC01, BC05 & 1 \\ \hline
15.85 & 2,2-Dimethyl-1,3-Propanediol * & 126-30-7 & & 821 & 892 & BC01, BC02, BC03, BC04, BC08 & 1 \\ \hline
16.11 & Octanal * & 124-13-0 & 05.009 & 505 & 701 & BC04 & 1 \\ \hline
16.28 & 1,2,3,4-Tetramethyl benzene & 488-23-3 & & 849 & 879 & BC09 & 1 \\ \hline
16.43 & Limonene * & 5989-27-5 & 01.045 & 916 & 924 & BC02, BC03, BC05–BC08 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Compounds identified via SPME GC-MS analysis. Figure 2: Chemical structure of 7,9-di-tert-butyl-1-oxaspiro[4,5]deca-6,9-diene-2,8-dione.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Xeus  
XEUS (X-ray Evolving Universe Spectroscopy) is a potential successor mission to XMM (X-ray Multi-mirror Mission), proposed by the ESA in 1996 [8]. It was incorporated into the International X-ray Observatory (IXO) project in 2008, followed by the Advanced Telescope for High-energy Astrophysics (ATHENA) for study in 2011. XEUS comprises a main detector (DSC) and a mirror spacecraft (MSC) 50 m apart to form an X-ray virtual telescope in orbit L2. The relative position measurement is carried out by the laser, as shown in Figure 4. The mission-design life of XEUS is greater than 25 years. The distance measurement accuracy is on a sub-micron scale, with an effective range of 40 cm \(\pm\) 1 m, and the surface offset measurement accuracy is 125 microns, with an effective range of \(\pm\)1 m; the roll angle measurement accuracy is 8 seconds, and the pitch and yaw angle accuracy are up to 1 seconds. The laser probe of the DSC is controlled by the system, which can measure the relative pose of the target spacecraft [9].
# A Method for Borehole Image Reverse Positioning and Restoration Based on Grayscale Characteristics
## 3 Reverse Positioning of Panoramic Imaging Probe
### Borehole Image Grayscale Feature Model
The exposure \(H\) is related to the surface illuminance [32] by the following relationship:  
\[H=E_{0}\cdot T \tag{7}\]  
where \(E_{0}\) is the surface illuminance and \(T\) is the exposure time. Figure 5: Coordinate system of borehole wall and probe. For the panoramic borehole camera system, the relationship between surface illuminance and borehole wall brightness has been derived and can be expressed as:  
\[E_{0}=\frac{\pi\tau}{4{(\frac{f}{d})}^{2}}B \tag{8}\]  
where \(\tau\) is the transmittance coefficient of the lens, \(f\) is the focal length of the lens, and \(d\) is the diameter of the lens. Based on (4) to (8), for a uniform borehole wall, the grayscale value of a point on the borehole image can be calculated as:  
\[ \begin{array}{l}D(\theta,h)=\gamma\lg-\frac{\lambda}{(\frac{\pi}{g})^{2}}\\ \lambda=\frac{\pi\tau TK\cos\pi}{4{(\frac{f}{d})}^{2}\tau_{B}^{2}}10^{\frac{ \eta}{\eta}}\end{array}  \tag{9}\]  
where the value of parameter \(\lambda\) is related to equipment parameters, scanning line radius, and borehole wall reflectance. For borehole imaging equipment, the contrast coefficient \(\gamma\) of the CMOS camera is \(\gamma=1\). Cylindrical coordinates are established with the borehole axis \(O_{B}\) at depth \(h=0\) as the pole, the true north direction as the polar axis, and the borehole axis as the \(z\)-axis. Then, when the position of the panoramic imaging probe is \((kr_{B},\theta_{0},h)\), for a point \((\theta,h)\) on the borehole image, it holds true that:  
\[D(\theta,h)=\lg\frac{\lambda}{{(\frac{S}{r_{B}})}^{2}} \tag{10}\]  
where \(S\) is a function of \(k(h)\) and \(\theta_{0}(h)\). Equation (10) is the three-dimensional position image grayscale feature model of panoramic imaging probes proposed in this paper.
# Metaverse Meets Smart Cities--Applications, Benefits, and Challenges
## 5 Results
### Potential Applications in the Smart City  
Table 1 provides an overview of fields of metaverse applications in a smart city context and the corresponding applications. In addition, it addresses the stakeholders responsible for providing the metaverse infrastructure and the individual components, as well as those who benefit significantly from the applications. Further details on each field are provided below. Section 5.2 addresses the stakeholders in metaverse applications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Field of Application** & **Application** & **Stakeholders** \\ \hline \multirow{6}{*}{City Planning \& Simulation} & Urban planning & Administration, planning offices, public authorities \\ \cline{2-3}  & Risk planning & Administration, public authorities \\ \cline{2-3}  & Digital City Twin & Administration, industry, building yard \\ \cline{2-3}  & Environmental monitoring & Administration, legislator \\  & and management & \\ \cline{2-3}  & Planning tool for renewable energies and & \\  & potential savings & Administration, citizens, authorities \\ \hline \multirow{3}{*}{Administration \& Optimization} & Monitoring of Smart Buildings & Administration, property manager \\ \cline{2-3}  & Tracking the use of resources and & \\ \cline{2-3}  & consumption optimization & Administration, building yard \\ \hline \multirow{6}{*}{Citizen Services \& Participation} & Digital administration services & Administration, citizens \\ \cline{2-3}  & Digital forms & Administration, citizens \\ \cline{2-3}  & Virtual customer centers & Administration, citizens \\ \cline{2-3}  & Digital citizen ID & Administration, citizens \\ \cline{2-3}  & Citizen participation and virtual voting & Administration, citizens, politics \\ \cline{2-3}  & Provision of city-related & \\ \cline{2-3}  & real-time information & Citizens, tourists \\ \cline{2-3}  & Provision of information on measures and & \\ \cline{2-3}  & funding opportunities & Citizens, industry, clubs \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Smart city metaverse applications.
## 5 An Informational Complex of the TIEN Shan Mountain Station  
Experimental installations of the type \"Hadron-55\", \"Storm installation\", \"Horizon -T\", \"EAS Radio Emission\", \"Groza\", \"MAC2\" (installation for registration of earthquakes), and others, which contain a scintillation carpet, a neutron monitor, an underground monitor, a calorimeter with a gamma block and neutron detectors, Cherenkov and remote scintillation detectors, a scintillation spectrometer, and a number of other subsystems, are currently operating at the station. They are combined into the following items, for which a general scheme of the network infrastructure has been developed with the ability to connect them to a single dedicated local network:  
* Point \"Dormitory\", where the center of the local network is located with network and server equipment.
* Point \"Elling\", the center of the storm water installation.
* Point \"IPT\", a center of the \"Hadron-55\" installation with a calorimeter, gamma-block, and neutron detectors.
* Point \"Horizon\", the registration center of the \"Horizon-T\" installation.
* Point \"Bunker\" site, here the remote detectors of the \"Horizon-T\" installation with an autonomous registration system are located.
* Point \"Stone Flower\" site, with the remote detectors of the \"Horizon-T\" installation with an autonomous registration system.
* Point \"Neutron Super Monitor\".
* Point \"Underground\" with muon detectors and an underground neutron monitor located at a depth of 20 m of water equivalent.  
The communication between the subsystems is performed by combining fiber-optic lines into a network, which increases the reliability of communication and the speed of data transmission over the network. The application of a fiber-optic line has increased the resistance of the local network to adverse environmental influences such as precipitation, lightning, and static electricity.  
The application programs for working from a local network are subroutines, which by using special libraries form queries to databases, transfer the requested records to the program content, and process them in accordance with the specified criteria. The processed data are represented in text and/or graphical form. Using the Linux operating system, programming languages C, C++, Python, Java, and Java script, the programming interfaces and modules have been developed and debugged, allowing for the transfer of database records into program content.  
Developed and debugged software support ensures the normal functioning of the entire infrastructure and uninterrupted client access to the database of the combined system. The complete information obtained in the course of the experiments carried out at the Tien Shan station is accumulated in a common database which has an open Internet access for general use of these data for analysis by the participants of third-party scientific organizations [25].
## 4 Evaluation Methods
### Assessing the Effectiveness of Reduction  
While performance metrics are important for evaluating the proposed model, it is even more crucial to verify the effectiveness of the proposed rejection metric. The proposed model is designed to reduce uncertainty, and it identifies and removes the worst predictions based on the uncertainty metric before producing the final output. Therefore, it is necessary to evaluate by how much the worst predictions have been reduced. To measure this, we set the percentage error and Z-score between the actual values and the prediction as the  
Figure 5: Statistical analysis for determining the optimal threshold for balancing prediction acceptance and reliability. (**a**) Relationship between \(p\)-value and threshold, with \(0.36\) identified as the optimal threshold at a \(p\)-value of \(0.05\). (**b**) The acceptance rate as a function of threshold, where \(0.36\) corresponds to a \(72\%\) acceptance rate.  
evaluation metrics. The percentage error is a measure of the deviation of a measurement from the actual value, expressed as a percentage. The formula is as follows:  
\[\text{Percentage Error}=\frac{y_{i}-\hat{y_{i}}}{y_{i}}\times 100 \tag{8}\]  
This metric has the advantage of expressing both overestimation and underestimation in a regression model. Thus, it is useful for providing insights into the measurement's inaccuracy and error distribution. We also adopt the Z-score to evaluate the models in the effectiveness of the uncertainty reduction. The Z-score provides a statistical measurement that describes the relative error from the mean of a group. It is calculated using the following formula:  
\[\text{Z-Score}=\frac{1}{n}\sum\frac{\hat{y_{i}}-\hat{y}}{\sigma} \tag{9}\]  
where \(\sigma\) is standard deviation. The Z-score can be used to evaluate by how far the model's predictions deviate from the actual values, thus providing a measure of uncertainty. This method allows for an assessment of the reduction in error through the implementation of uncertainty quantification techniques.
# Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns
## 2 Materials and Methods
### Statistical Analysis of Structure-Function Coupling  
To test whether the laterality of the strong connections matched each patient's diagnosis, we first split the structural and functional connectomes into left and right hemispheres (Figure 1, Step 3, b) and removed cross-hemisphere electrode pairs. For example, if an electrode pair contained two electrodes in the left hemisphere (i.e., FP1-F3) or one left hemisphere and one central electrode (FP1-Fz), the electrode pair was kept. All electrode pairs that crossed from one hemisphere to the other (i.e., F3-F4) were removed. To test whether the highly connected electrode pairs from the structural and functional connectomes were congruent, we first computed the z-scores for all electrode pairs from all connectomes (Figure 1, Step 3, b, i). The z-score arrays were: a. 1 \(\times\) 1D array per hemisphere for the structural connectome and b. 5 \(\times\) 1D arrays (for each 1 s time window) per seizure, per hemisphere for the functional connectomes. Lastly, the structural and functional zscores from each hemisphere were displayed in parallel format for analysis (Figure 1, Step 3, b, ii).  
To preserve only the most robust connections to represent high coherence between two electrodes, a z-score threshold of 2 (i.e., two standard deviations from the mean) was chosen for the structural connectome, and a threshold of 1.8 was chosen for the functional connectomes. Next, the z-scores from both connectome types were compared per one-second window from each seizure. If the same electrode pair from the structural and functional connectomes contained a z-score between 1.8 and 2 (or greater than 2), that electrode pair was classified as showing high structure-function coupling (termed \"coupled electrode pairs\").  
The senior neurologist provided a \"laterality\" score for each patient based on whether the most frequently observed seizure onset zone was consistently restricted to one hemisphere. A laterality score of zero represented poorly lateralised seizures, whilst highly lateralised seizures received a score of three. If overall, the patient had late-lateralising seizures, they were classified as being non-lateralised at onset (i.e., a score of 0-1). Each patient was also assigned an \"expected onset zone\", predicated on the most frequently observed onset zone observed in all of a patient's recorded seizures, including seizures that were poorly or non-lateralised. The neurologist reviewed the raw EEG to confirm whether the electrode pair with high structure-function coupling was congruent with the expected seizure onset zone. All z-scores and statistical analyses were produced in SPSS v28 (Armonk, NY, USA: IBM Corp).
## 3 RSSI Signal Based MLT Location Estimation  
Figure 1 depicts the WSN system employed in this investigation. More fixed nodes will produce more reliable data, but they will also increase the cost and complexity of the system. To select location coordinates in a rectangular-shaped range, we chose four nodes, which is a simple and minimal number [1;2;6]. At the predetermined positions, there were four fixed nodes (FNs): FNs 1 (0, 3), 2 (3, 3), 3 (0, 0), and 4 (3, 0). The computational and receiving components of the system were combined into a receive station node (RSN). Within the restricted area, a mobile node (MN) of unknown identity was located. The FNs, which were situated at predefined points, sent RSSI signals to the MN. A mathematical equation based on the information received by the MN was used to estimate the MN's coordinates. We developed an indoor positioning system based on RSSI. It consists of three steps: gathering the RSSI measurements using a communication protocol that has been designed, converting the RSSI measurements into distances using a PLE, and using a multi-signal algorithm.
# A Novel Method for Constructing the S-Box Based on Spatiotemporal Chaotic Dynamics
## 5 Performance Analysis of the Constructed S-Box
### Strict Avalanche Criterion  
The work in [5] firstly introduced the Strict Avalanche Criterion (SAC). If an S-box has the SAC property, all the output bits will vary with half the probability when complementing a single input bit. To check the SAC property, we always employed the dependence matrix. For an S-box satisfying the SAC property, all the values in its dependence matrix was close tothe optimal value of \(0.5\).  
We can estimate the offsets of the dependence matrix by:  
\[S(g)=\frac{1}{n^{2}}\sum_{1\leq r\leq n}\sum_{1\leq w\leq n}\left|\frac{1}{2}- Q_{r,w}(g)\right|, \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S-Boxes** & **Max** & **Min** & **Average** \\ \hline The obtained S-box & 108 & 102 & 104.5 \\ S-box based on CML & 106 & 100 & 103 \\ S-box proposed in [11] & 108 & 100 & 103.25 \\ S-box proposed in [13] & 109 & 103 & 104.88 \\ S-box proposed in [14] & 106 & 100 & 103 \\ S-box proposed in [16] & 106 & 100 & 103.25 \\ S-box proposed in [17] & 108 & 102 & 104.75 \\ S-box proposed in [18] & 108 & 98 & 103 \\ S-box proposed in [19] & 112 & 108 & 109.25 \\ S-box proposed in [20] & 108 & 104 & 105.8 \\ S-box proposed in [21] & 110 & 102 & 105.5 \\ S-box proposed in [22] & 108 & 102 & 105.25 \\ S-box 2 proposed in [23] & 108 & 102 & 106 \\ S-box proposed in [24] & 106 & 96 & 103 \\ S-box proposed in [25] & 110 & 106 & 108.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the comparison of nonlinearity.  
where \(Q_{r,w}(g)=2^{-n}\sum_{x\in B^{n}}g_{w}(x)\oplus g_{w}(x\oplus e_{r})\), \(e_{r}=[\theta_{r,1}\theta_{r,2}\ldots\theta_{r,n}]^{T}\), \(\theta_{r,w}=\begin{cases}0,\ r\
eq w\\ 1,\ r=w\end{cases}\), and \([\cdot]^{T}\) denotes the transpose of a matrix.  
Table 4 shows the obtained dependence matrix. These results were between 0.6406 and 0.4219, and the average value was 0.4980, which was close to the ideal value of 0.5. Moreover, Table 5 analyzes the SAC property of different S-boxes. It can be noted that the mean value of SAC of the obtained S-box is closer to the ideal value 0.5 than that of other S-boxes. Therefore, in comparisons with other S-boxes, the obtained S-box had better SAC performance.
## 4 User Study
### Measurements
[PERSON] et al., (1999, 2003) achieved four major components of spatial presence, involvement presence, experienced realness, and general presence through factor analysis with two experiments after constructing a survey questionnaire with a review of many prior studies. Spatial presence means not looking at a screen or manipulating a virtual object outside the screen, but rather a feeling of manipulating and moving things directly within a virtual space. In other words, it means a sense that does not recognize a medium such as HMD or TV and seems to exist physically in the virtual space and is deeply related to the concept of non-medium. Involvement presence relates to the obliviousness to reality and the perception of presence through paying attention to the virtual environment and experience in it. Experienced realness refers to the measurement of how the virtual environment isrealistically perceived. Finally, general presence can be regarded as a result of integrating all of them, meaning the concept of \"being there\": a feeling that a physical body exists somewhere else rather than where it really exists. Among these, however, experienced realness was excluded because its questions for measurement are composed of items of vividness. Three representative questions from the other three presence factors respectively are selected and taken as measurement. Following the survey on those three questions of the presence, a reliability analysis was conducted and the results satisfied the reliability, as shown in Table 4.  
For measuring the degree of anxiety, the short form of STAI that was developed by [PERSON] and [PERSON] (1992) [40] was used in the present study. As mentioned earlier, [PERSON] and [PERSON] (1992) developed a scale that reduced the state anxiety items of [PERSON] et al. 's full-form STAI, which measure the degree of anxiety that a person feels in the current situation, to only six items, for use when it is inappropriate to use the full form of STAI developed by [PERSON] et al. (1983). The short-form scale developed by [PERSON] and [PERSON] (1992) was used in the present study because the full-form STAI has too many items for measurement purposes. As the form was originally developed according to a 4-point scale, it was replicated here so as to provide continuity and secure the reliability of the results. The contents of the questionnaire can be found in Table S1 (See Supplementary Materials).
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 4 The Spatial Optimized Dynamic Path Planning Algorithm
### Control the Scale of the Road Network  
A common method for controlling the scale of the road network is to frame a search area in the road network by ellipse, circle, and polygon according to the coordinates of the starting node, which can be regarded as the search area of the algorithm. However, there is no guarantee that all nodes in the designated area are connected to each other for those methods [20], so a common shortcoming of these methods is that they may not find an optimal path in the selected search area. For example, as the A* algorithm is heuristic, it cannot consider some of the best paths that, to avoid congestion, move to an area not explored by A*.  
In order to solve this problem, this paper proposes taking the connected sub-graph of the divided road network to be the search area when controlling the scale of the road network to ensure that there is at least one path in the search area. Therefore, the efficiency of the algorithm is guaranteed, and the robustness of the algorithm is ensured at the same time.
## 4 Discussion
### Moment-Rotation Envelope Curves  
The moment-rotation envelope curves of different specimens are compared in Figure 14, which are averaged for comparative purposes. Figure 15 defines the key parameters in the moment-rotation relationship of the joints. In this study, the yield points of all specimens were determined using the same criterion to conduct a comparative analysis among the specimens. Therefore, a general yield moment method [24], one of the commonly used defining methods, was adopted in this study, where point B is the equivalent yield point. The moment and the rotation corresponding to point B are the yield moment (\(M_{y}\)) and yield rotation (\(\theta_{y}\)) of the connection. The moment and the rotation corresponding to the highest point of the \(M\)-\(\theta\) curve are defined as the measured flexural resistance (\(M_{\text{max}}\)) and the corresponding rotation (\(\theta_{\text{max}}\)), respectively. With reference to the definitions presented by [PERSON] et al. [5] and [PERSON] et al. [25], \(M_{\text{f}}=0.85\,M_{\text{max}}\) is the failure moment of the connection, and the corresponding rotation is the failure rotation (\(\theta_{\text{f}}\)), which represents the rotation ability of a joint in this paper. Table 4 summarizes the main results for each key point of the specimen.
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 2 Antenna Design Methodology
### Evolution of the Antenna
The coupling can also be reduced by placing a conductive shield between the antenna and the body.