esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology and Empirical Analysis
### Data Collection and Sample Screening  
During the formal questionnaire phase, 214 senior front-line dispatchers (average length of service = 13.3 years, SD = 6.8, min = 5, max = 25) from Beijing, Tianjin, Guangzhou, Shijiazhuang, and Suzhou participated. To ensure data quality, participants completed the questionnaire in a quiet, distraction-free meeting room under the supervision of duty managers or higher-level supervisors. The demographic characteristics of the survey participants, including age, length of service, and educational background, are summarized in Table 3.  
The formal questionnaire tested the initial research hypotheses, with questions presented in random order. Participants rated their agreement with descriptive statements on a 7-point Likert scale. Questionnaire items were fine-tuned to account for regional differences in terminology.  
Questions 2, 4, 9, 11, 13, 15, 16, 17, and 18 (see Appendix B, Table A1) were rerese-sored. Samples were excluded if more than two dimensions of automation trust exhibited conflicting reverse scale scores or if response times were excessively long (indicating interruptions) or short (indicating careless responses). After screening, a final valid sample of 160 was obtained, with an average response time of 1408 s (SD = 529).
## 5 Performance Comparison and Validation
### Parameters Configuration  
In this study, the node communication energy consumption adopts the space energy loss model, and the simulation parameter configuration is shown in Table 5.  
The initial energy of the sensor nodes involved in this paper is 0.5\(j\), and the energy of _sink_ nodes is unlimited. According to the node types and business requirements in Table 1, the network model is constructed proportionally to verify the performance of the adaptive routing model in this paper. In the comparative analysis before, we introduced a few concepts about performance indicators. (1) FND (the time at which the first node dies); (2) HND (time when half of the nodes die); (3) CP Index (Comprehensive Performance Index); the utility of a high-speed railway monitoring network is determined by its life cycle and real-time performance. In this paper, we build a complete evaluation model for life cycle and real-time performance:  
\[\text{U}=\lambda\times\frac{L}{\max(L)}+(1-\lambda)\frac{\min(T)}{T}. \tag{23}\]  
In the table, \(U\) is the comprehensive performance index, \(L\) is the life cycle, and \(T\) is the delay.
# Properties and Functionality of Cereal Non-Starch Polysaccharides in Breadmaking
## 5 Conclusions  
This review summarizes the current state of knowledge regarding the role and impact of NSPs in bread baking, as well as outlines missing information on the above issues. A review of the literature showed that NSPs have interesting properties from the point of view of the baking industry. The solubility of NSPs from cereal grains depends on their molecular structure. NSPs from cereal grains have hydrophilic properties and are able to bind high amounts of water--from 0.38 g to 15 g of water/g d.b. NSPs from cereal grainsform viscous solutions and are responsible for the viscosity of different kinds of dough. In the presence of NSPs from cereal grains, the surface tension decreases by up to 20 mN/m. The molar mass of NSPs from cereal grains is in the range of 200,000-2,340,000 g/mol. In cereal grains, the dominant NSPs are AXs and \(\beta\)-glucans. To date, properties of AX and \(\beta\)-glucan are better characterized, but there is a lack or incomplete information on the majority of the above-mentioned properties for NSPs such as galactomannans, arabinogalactans, xyloglucans, and pectin polysaccharides from cereal grains.  
The second part of this review summarizes information regarding the role of NSPs in bread making. Even a small share of the NSPs in flour can have a positive effect on the basic parameters of dough, such as water absorption, dough yield, as well as volume, moisture, texture, and aging of bread. The addition of NSPs with a specific molecular structure to the dough may increase water absorption and dough yield, which also results in the desired, higher moisture content of the bread crumb. High molar mass cross-linked AXs or partially hydrolyzed low molar mass AXs have a positive effect on the volume and other properties of bread. Research was also carried out on the effect of the addition of \(\beta\)-glucans and galactomannans, and their positive effect on the hardness of the wheat bread crumb was demonstrated. Moreover, according to literature data, NSPs can effectively inhibit the digestion of starch, lipids, and proteins in the small intestine and, therefore, also prevent obesity or diabetes. In addition, NSPs may also show immunomodulatory effects.  
The structure of NSPs determines their properties and functionality in technological processes. However, research, including the role of NSPs and their molecular structure in breadmaking, are complicated and still scarce. The knowledge concerning cereal NSPs, especially modified ones and of defined structure, is incomplete and further research is needed to characterize their functionality in breadmaking.  
Conceptualization, K.B. and A.B.; writing--original draft preparation, A.B.; writing--review and editing, K.B.; visualization, A.B.; supervision, K.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Evaluation Experiments
### Experimental Setup  
In this paper, the Mininet-Wifi tool proposed in [25] is used for emulation purposes. Mininet-Wifi is an open source tool that extends the well-known Mininet emulator [[http://mininet.org/](http://mininet.org/)]([http://mininet.org/](http://mininet.org/)) accessed on 24 August 2022 to implement SDNs with virtual wireless access points and stations. Within the Mininet-Wifi framework, the Ryu controller is considered. Traffic between nodes is generated by the _iperf_ command, whereas the number of bytes sent and received by the access point is captured by the _cat/proc/net/dev_ commands. The main technical aspects of the emulation are summarized in Table 1.  
The SDN controller estimates the transmission (\(E_{tx}\)) and receiving (\(E_{tx}\)) energies as [44]:  
\[\begin{split} E_{tx}&=(Time+\frac{Byte}{DataRate })Power_{tx},\\ E_{rx}&=(Time+\frac{Byte}{DataRate})Power_{rx}, \end{split} \tag{1}\]  
where _Time_ is the transmitter and receiver time; _Byte_ is the number of transmitted bytes at each second; \(DataRate\) is a specific number of bytes transmitted per second; and \(Power_{tx}\) and \(Power_{rx}\) are the transmission and receiving power, respectively.
## 2 Numerical Model
### Model Setup  
The computational complexity of a three-dimensional (3D) fracturing model usually varies in magnitude compared to a two-dimensional (2D) fracturing model. Therefore, compared to the 2D fracturing model, the 3D modeling process has appropriately simplified the model. The main parts that deal with changes include the following: 1 reducing the geometric size of the model and increasing the layer thickness of the model; 2 During mesh generation, the largest possible mesh was used, and after multiple geometric modeling and mesh generation, a model with suitable mesh quality and geometry was selected for subsequent numerical simulation modeling and fracturing analysis. Based on this, the specific conditions set for this 3D simulation model are as follows: 1 The target reservoir has a significant tendency toward a natural bedding structure, so the construction of the bedding network is achieved through Python programming. 2 Based on field data, the depth of the target block in the simulation typically falls within the range from 4018.5 m to 4263.5 m, with a fracturing construction interval usually set at 60 m. For ease of simulation calculations, it is assumed that the reservoir area size in the simulation is 60 m \(\times\) 60 m \(\times\) 3 m. 3 During fracturing, fluid will flow from the wellbore into the target reservoir. fp3d2 elements are used to simulate the fluid pressure drop and flow distribution in the wellbore, and the fpc3d2 element is used to simulate the frictional resistance when the wellbore fluid flows into the target area of the reservoir. By binding nodes, the simulation process of fluid flowing from the wellbore to the target reservoir can be achieved. In addition, considering the randomness of perforation spacing during the fracturing construction of the target reservoir, 38 random perforations are also set in the simulation (Figure 2b). 4 Based on the on-site data collected, the target block displays typical characteristics of carbonic basin subsidence. Therefore, by aligning the x, y, and z directions with the maximum horizontal principal stress, minimum horizontal principal stress, and vertical principal stress, respectively, the impermeable model boundaries are defined. The model is constrained by applying directional displacements, ensuring an accurate representation of the in situ stress conditions and preventing fluid flow across the boundaries. 5 Based on the on-site data from the target reservoir, representative model parameters are selected. The benchmark calculation example focuses on single-cluster fracturing, assuming a reservoir thickness of 3 m. The injection parameters include a displacement rate of 18 m\({}^{3}\)/min and a total injection time of 3600 s. The in situ stress conditions are characterized by a minimum horizontal stress of 98 MPa, a maximum horizontal stress of 116 MPa, and a vertical stress of 112 MPa. The initial pore pressure is set to 90 MPa. Furthermore, the remaining rock material parameters are derived from the mechanical testing data of shale rocks obtained from the Lu214 block in the Sichuan Basin (as detailed in Table 1).
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 3 Design of Trajectory-Tracking System
### Introduction of Pure Pursuit (PP) Algorithm  
In the PP algorithm, the target point g in tracking path is the target point between the forward-looking distance of the center of the vehicle body and the path. As shown in Figure 3, the target point \(g\) belongs to one of the points along the entire travel path. The forward-looking distance \(L_{f}\) is calculated using Equation (6):  
\[L_{f}=k_{f}*v+L_{fm} \tag{6}\]  
Figure 1: The schematic diagram of mobile robot hardware platform.  
Figure 2: The schematic diagram of the differential-wheel model.  
where \(k_{f}\) is the custom speed weight, \(v\) is the linear velocity of the mobile robot and \(L_{fm}\) is the minimum forward-looking distance limit.  
The algorithm uses the PD controller to follow the path calculates the angle deviation according to the current position of the robot and the forward-looking distance point g, and then keeps the robot moving on the trajectory through the PD controller. The control structure block diagram of the PD controller is shown in Figure 4. In the PP algorithm, the forward-looking distance can impact to path tracking accuracy and may cause the mobile robot to oscillate, shown in Figure 5.  
Figure 4: The control structure block diagram of PD controller.  
Figure 5: The impact of forward-looking (foresight) distance on generating the tracking error in the Pure Pursuit algorithm. A longer forward-looking distance represents smoother path tracking, and a shorter forward-looking distance give accurate tracking, but the PD controller is more challenging to adjust.  
Figure 3: The definition of the target point \(g\) (blue circle) in Pure Pursuit algorithm.
## 7 Simulation and Results
### Increasing Number of Hops
#### 7.1.1 Packet Success Ratio  
As shown in Figure 18, hotline assisted routing shows much more reliable results compared to all wireless routing. We can notice that as the number of hops is increasing there is a decrease in the packet success ratio in both the cases. However, packet drop rate is much higher in the case of all wireless routing. One of the main reasons is that WSN nodes, following their duty cycle, go to periodic  
Figure 18: Packet success ratio _versus_ number of hops. Hotline assisted routing has fewer hops from source-to-sink due to routing through gateways.  
sleep state for energy savings and therefore for multi-hop communications there are more chances that nodes sleeping schedule will clash. We minimize the packet drop in the case of hotline assisted routing with the help of gateway to gateway wired communication. Hotlines bypass the erroneous wireless links and make every gateway just one hop away from all other gateways. Therefore, the proposed topology enhances the packet success ratio manifolds as compared to traditional all wireless routing as shown in Figure 18.
## 5 LBV of Key Hydrocarbon Components in Aviation Fuel
### LBV of Aromatic Hydrocarbons in Aviation Fuel
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Aromatics**} & \multicolumn{3}{c}{**Experimental Conditions**} & \multirow{2}{*}{**LBV MeasurementMethods**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Ref.**} \\ \cline{2-2} \cline{5-6}  & **T (K)** & & & & \\ \hline Benzene & 450 & 304 kPa & Spherical flame method & 2005 & [103] \\ Toluene & 450 & 304 kPa & Spherical flame method & 2005 & [103] \\ Ethylbenzene & 450 & 304 kPa & Spherical flame method & 2005 & [103] \\ m-Xylene & 450 & 304 kPa & Spherical flame method & 2005 & [103] \\ n-Propylbenzene & 450 & 304 kPa & Spherical flame method & 2005 & [103] \\ Toluene & 298, 400, 470 & 1 atm & Stagnation flame method & 2010 & [93] \\ Benzene & 353 & 1 atm & Stagnation flame method & 2012 & [104] \\ Toluene & 353 & 1 atm & Stagnation flame method & 2012 & [104] \\ o-Xylene & 353 & 1 atm & Stagnation flame method & 2012 & [104] \\ m-Xylene & 353 & 1 atm & Stagnation flame method & 2012 & [104] \\ p-Xylene & 353 & 1 atm & Stagnation flame method & 2012 & [104] \\
1,3,5-Trimethylbenzene & 353 & 1 atm & Stagnation flame method & 2012 & [104] \\
1,2,4-Trimethylbenzene & 353 & 1 atm & Stagnation flame method & 2012 & [104] \\ n-Propylbenzene & 353 & 1 atm & Stagnation flame method & 2012 & [104] \\ Toluene & 298, 358, 398 & 1 atm & Heat flux & 2014 & [87] \\ Benzene & 358, 423 & 1, 2, 5, 10, 20 atm & Spherical flame method & 2017 & [105] \\ Toluene & 358, 423 & 1, 2, 5, 10, 20 atm & Spherical flame method & 2017 & [105] \\ Ethylbenzene & 358, 423 & 1, 2, 5, 10, 20 atm & Spherical flame method & 2017 & [105] \\ sec-Butylbenzene & 423 & 1, 2, 5, 10 atm & Spherical flame method & 2020 & [107] \\ Pentylbenzene & 473 & 1, 2, 5, 10 atm & Spherical flame method & 2021 & [108] \\ Isobutylbenzene & 423 & 1, 2, 5, 10 atm & Spherical flame method & 2021 & [106] \\ n-Butylbenzene & 423 & 1, 2, 5, 10 atm & Spherical flame method & 2021 & [106] \\ tert-Butylbenzene & 423 & 1, 2, 5, 10 atm & Spherical flame method & 2021 & [106] \\ Toluene & 338 & 1 atm & Heat flux method & 2022 & [97] \\ Benzene & 403 & 1, 5 atm & Spherical flame method & 2023 & [101] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Information on LBV testing of different aromatic hydrocarbons. Figure 10: Illustration of the LBV of aromatics at atmospheric pressure under temperatures of around 353 (green) 423 (red) and 473 (blue) K. The data come from the literature in Table 7.
## 8 System Design
### Onboard Processing Design, Software and Operations
#### 8.5.1 Onboard Processing Hardware Design  
The onboard processing system on OpenOrbiter consists of two computers working in tandem. The flight computer, which will run the operating software for the spacecraft, is always on and runs on a low-cost Raspberry Pi computer. Spacecraft, such as OpenOrbiter, with significant processing requirements can add to the OPEN base design of the Raspberry-based onboard processing. OpenOrbiter has selected to use GumStix WaterStorm COM units for its payload processing. Separate storage exists on both the Raspberry Pi and the GumStix WaterStorm COM units. On OpenOrbiter, the payload processing unit will only be powered on when a job is being run on it. Figure 3 depicts the configuration of onboard processing for OpenOrbiter.
## 4 Results and Discussion
### Product Structure and Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Start** & **1** & **2** & **3** & **4** & **5** & **6** & **STOP** & **RESULTING SEQUENCE** \\ \hline
1 & 1 & 12 & 123 & 1234 & 12345 & 123456 & 1234567 & 12345678 & 12345678 \\
2 & 1 & 12 & 123 & 1234 & 12345 & 123456 & 1234568 & 12345678 & 12345687 \\
3 & 1 & 12 & 123 & 1234 & 12345 & 123457 & 1234567 & 12345678 & 12345768 \\
4 & 1 & 12 & 123 & 1234 & 12345 & 123457 & 1234578 & 12345678 & 12345786 \\
5 & 1 & 12 & 123 & 1234 & 12345 & 123458 & 1234568 & 1234568 & 12345867 \\
6 & 1 & 12 & 123 & 1234 & 12345 & 123458 & 1234578 & 12345678 & 12345876 \\
7 & 1 & 12 & 123 & 1234 & 12346 & 123456 & 1234567 & 12345678 & 12345678 \\
8 & 1 & 12 & 123 & 1234 & 12346 & 123456 & 1234568 & 12345678 & 12345687 \\
9 & 1 & 12 & 123 & 1234 & 12346 & 123467 & 1234567 & 12345678 & 12346587 \\
10 & 1 & 12 & 123 & 1234 & 12347 & 123457 & 1234567 & 12345678 & 123457568 \\
11 & 1 & 12 & 123 & 1234 & 12347 & 123457 & 1234567 & 12345678 & 12345786 \\
12 & 1 & 12 & 123 & 1234 & 12347 & 123467 & 1234567 & 12345678 & 12347568 \\
13 & 1 & 12 & 123 & 1236 & 12346 & 123456 & 1234567 & 12345678 & 12345678 \\
14 & 1 & 12 & 123 & 1236 & 12346 & 123456 & 12345678 & 12345678 & 12364587 \\
15 & 1 & 12 & 123 & 1236 & 12346 & 123467 & 1234567 & 12345678 & 12364587 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
252 & 1 & 17 & 167 & 1467 & 13467 & 123467 & 1234567 & 12345678 & 17643258 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected feasible assembly sequences generated due to design constraints. Figure 4: Digraph of the structural constraints of the forklift door assembly states. In the learning process of the neural network, the weight values for all neurons are adjusted. This has an impact on the obtained results because the weights can weaken (negative values) or strengthen (positive values) the signals transferred by individual layers of the network. Table 3 presents the weight values generated for the analyzed RBF network. Table 4 summarizes the actual and expected assembly time prediction values, whereas Figure 7 is a graphical interpretation of their dependencies. A set of verification data containing previously unused input and output data was selected for the analysis. The results of the analysis confirm the effectiveness of the prediction performed by the RBF 3-8-1 neural network. The obtained results, both expected and obtained at the network output, are comparable. The operation of the network was tested on the basis of 10 random assembly sequences and the result of assembly time was obtained for each of them. Based on the results presented in Table 4, it can be indicated which of the assembly sequences was characterized by the shortest assembly time, which is therefore the optimal solution.
## 2 Experimental and Calculation Details
The processing parameters used for ablation are given in Table 2. The surface morphologies of the diamond coating were observed by field emission scanning electron microscope (Zeiss, GeminiSEM500). Confocal Raman spectrometer (Renishaw, inVia Reflex) was employed to evaluate the carbon phase of the coating. A white-light (WL) interferometer ([PERSON], ContourGT-K) was used to measure the average roughness of the laser-irradiated regions and the material removal rate.
## 5 Conclusions  
This study contributes to the field of physics education and immersive learning by demonstrating the potential of IVR serious games to support the teaching and learning of complex scientific concepts, specifically projectile motion. The integration of \"Parabolic Basketball VR\" into instructional strategies resulted in significant improvements in students' conceptual understanding, as reflected in the high normalized learning gain (0.82). Additionally, the serious game fostered higher engagement and motivation, providing an interactive and experiential approach that complements traditional instructional methods.  
The findings highlight that IVR environments, when designed with pedagogical intent, can serve as effective tools for knowledge acquisition, allowing students to actively experiment, predict outcomes, and immediately observe results. This interactive engagement helps bridge the gap between theoretical knowledge and practical application. Furthermore, the UCD approach applied in the development of the game ensured intuitive gameplay mechanics, aligning with the learning preferences of digital native students transitioning from adolescence to early adulthood.  
While the results are promising, several areas for future research and development have been identified:  
* Enhancing adaptive learning mechanisms: Future iterations of \"Parabolic Basketball VR\" could incorporate adaptive difficulty levels that respond to student performance, personalizing the learning experience and reinforcing conceptual understanding dynamically;
* Longitudinal studies on learning retention: Further research should explore whether the conceptual gains observed persist over time, assessing long-term retention and knowledge transfer regarding real-world problem-solving scenarios;* Expanding usability and cognitive load assessment: As IVR-based learning tools continue to evolve, it is essential to investigate cognitive load implications, ensuring that interactive elements enhance, rather than hinder, conceptual processing. Future usability studies should also examine a broader participant base, including individuals with varying levels of experience with IVR technologies.  
These considerations reinforce the potential of IVR serious games as a transformative complement to physics education, providing a bridge between theoretical learning and experiential engagement. As IVR and serious game methodologies advance, their integration into STEM curricula may contribute to improving cognitive engagement, retention, and conceptual mastery in physics education.
# A Compact Triple Band Antenna Based on Multiple Split-Ring Resonators for Wireless Applications
## 2 Construction of the Proposed Multi-SRR-Based Antenna
### Split-Ring Resonators Loaded Antenna  
The design process of the proposed multi-SRR-based antenna is shown in Figure 3. This antenna is based on a structure consisting of four octagonal rings with gaps that are interconnected to achieve multiband functionality. Each ring is sized to resonate at a specific frequency. A reduced ground with an area of 23 mm \(\times\) 1.6 mm is placed on the bottom of the substrate. The antenna is fed through a transmission line and fabricated on an FR4 substrate with a relative permittivity of 4.4 and a dissipation factor of 0.002. All the design parameters are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ws** & **Wf** & **R3** & **R4** & **Wn** & **Wt** & **Wm** & **R8** & **R5** \\ \hline
23 & 3 & 7.94 & 7.14 & 1.42 & 2.43 & 1.33 & 3.06 & 5.67 \\ \hline
**Ls** & **Lf** & **Lm** & **Lp** & **Wr** & **Lt** & **R7** & **Ground** & **R6** \\ \hline
24 & 2.5 & 5.84 & 4.17 & 0.83 & 2.5 & 3.4 & 1.6 & 5.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters in mm.  
Figure 2: Split-ring resonator unit cell. (**a**) Reflection coefficients (\(S_{11}\)) and transmission coefficients (\(S_{21}\)) of the metamaterial unit cell, (**b**) permeability and (**c**) current distribution.