esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Mathematical Formulation  
#### 3.2.1 Model Parameters  
The MILP was developed to support SR delivery logistics. In this section, we introduced the developed SR-MILP. The parameters and variables that may be involved in the following model are described as follows:  
_g,h_: Index of task  
_k_: Index of robot  
_s_: Index of station  
_f_: Index of transportation frequency  
\(\Omega_{H}\): Set of tasks  
\(\Omega_{K}\): Set of robots  
\(\Omega_{F}\): Set of transportation times  
\(\Omega_{SB}\): Set of robots starting station  
\(\Omega_{SE}\): Set of robots ending station  
\(\Omega\):\(\Omega_{SB}\cup\Omega_{SE}\cup\Omega_{H}\)  
_U_: Time from zero to full power  
\(T_{k}\): Maximum working power of the robot  
\(T_{kb}\): Initial power level  
\(T_{kf}\): Decision variable, representing the power consumption in the first transportation  
\(P_{g}\): Task processing time  
\(D_{g}\): Task demand  
\(Q_{k}\): The maximum transportation capacity of the robot  
\(V_{k}\): The speed of robot k  
\(d_{gh}\): The distance between two tasks  
\(X_{ghkf}\): Binary decision variable, during the first transportation, if the robot processes the task or returns to the service station to charge after the task; otherwise it is 0  
\(Y_{gkf}\): Binary decision variable, if the task is assigned to the robot in the first transportation; otherwise it is 0  
\(C_{gkf}\): A non-negative real number decision variable, which is the time when the robot starts its transportation task for the first time or charging at the service station  
Figure 2: The composition of the control center system.  
\(R_{gkf}\): A non-negative integer decision variable, the amount of product the robot loads after completing the task.
## 2 Module Design
### Design of the Thermal Control System
#### 2.2.3 Software Design of the Thermal Control System  
The software designed for the automated system was embedded in the STM32 microcontroller and programmed in C programming language based on a Keil development environment. It was made up of six function modules: a signal acquisition and process module for temperature measurement, an LCD12864 display module, a PID temperature control module, a PWM fan control module, a serial communicating module and a system monitoring and protecting module.  
In the temperature control process, due to the time-varying, nonlinearity and uncertainty factors of the laser and external environment, the voltage exerted on the TEMs was calculated based on a parameter self-tuning incremental PID algorithm. The design of this PID algorithm is shown as below:  
\[\Delta U(k)=K_{p}\times[e(k)-e(k-1)]+K_{i}\times e(k)+K_{d}[e(k)-2\times e(k-1) +e(k)] \tag{2}\]  
(1) \(|\mathrm{e}|>E_{41}:K_{p}=K_{p1},K_{d}=K_{d2},K_{i}=0\);  
(2) \(E_{a2}>|\mathrm{e}|>E_{41}:K_{p}=K_{p2},K_{d}=K_{d1},K_{i}=K_{i2}\);  
(3) \(|\mathrm{e}|<E_{a2}:K_{p}=K_{p1},K_{d}=K_{d1},K_{i}=K_{i1}\).  
The relationships among the PID parameters are as follows: \(K_{p1}>K_{p2}>\)0, \(K_{d1}>K_{d2}>0\), \(K_{i1}>K_{i2}\) > 0, \(E_{a1}>E_{a2}>\)0. \(K_{p1}\), \(K_{p2}\), \(K_{d1}\), \(K_{d2}\), \(K_{i1}\), \(K_{i2}\), \(E_{a1}\) and \(E_{a2}\) are constants. When \(|\mathrm{e}|\) is larger, we choose a larger \(K_{p}\) and smaller \(K_{d}\) to promote the tracking performance of the system, setting \(K_{i}=0\) to avoid larger overshoot when the system responds. When \(|\mathrm{e}|\) is of medium size, we set smaller \(K_{p}\) and \(K_{i}\) values to decrease the system response overshoot. When \(|\mathrm{e}|\) is smaller, we set larger \(K_{p}\) and \(K_{i}\) values to decrease the steady error and a medium-sized \(K_{d}\) to avoid system vibration near the set value.
## 4 Use Cases
### Attention Pattern  
MultiScaleAnalyzer provided intuitive visualization methods to identify attention patterns among students. For example, from the overview in Figure 2, it is evident that S9 and S1 were low-performing students, while S7 and S15 were high-performing students. By selecting students' names, the aggregated data view 1--referred to as \"AOI Vis\" (area-of-interest-based visualization)--revealed clear distinctions in attention patterns between these two groups.  
The low-performing student S1 exhibited a relatively low number of fixations, which were distributed intermittently (Figure 3). A considerable portion of these fixations were directed toward non-relevant areas, reflecting a tendency toward inattentiveness and disengagement. Besides this, S1's task completion times for certain questions were notably short, suggesting a tendency to rush through tasks without sufficient cognitive engagement.  
Figure 2: Overview ordered by performance.  
Similarly, S9, another low-performing student, demonstrated a slightly higher number of fixations, with longer durations. However, the attention pattern remained inconsistent and fragmented (Figure 4). S9 alternated between periods of focus on the task and episodes of visual wandering, during which no fixations appeared or fixations fell on irrelevant areas. These patterns highlighted distinct challenges in maintaining sustained attention and effective task engagement among low-performing students.  
In contrast, the high-performing student, S7, demonstrated sustained and stable attention throughout the task (Figure 5). Fixations were consistently focused on key informational areas, reflecting a deliberate and systematic approach to problem-solving. This attention pattern indicated an active process of reading and comprehending the problem statements, analyzing equations, and formulating solutions, thereby showcasing effective cognitive engagement and task management strategies.  
Figure 4: AOI-based attention pattern of S9.  
Figure 3: AOI-based attention pattern of S1.  
MultiScaleAnalyzer revealed students' different attention patterns in their problem-solving processes. However, it should be noted that not all high-performance students demonstrated sustained attention patterns, nor did low performance necessarily equate to the absence of positive attention patterns. Other factors, such as prior knowledge, task familiarity, and external distractions, may also influence students' performance outcomes.
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
## 2 Related Works
### Tourist Support by ICT
#### 2.2.3 BLE Beacon  
IoT is now becoming popular, and there are researches using IoT for sightseeing. One of the most popular technologies of IoT is a beacon using Bluetooth Low Energy (BLE) [32]. BLE has a function to send an \"advertising message\" to notice a BLE device's existence. The outline of the behavior of BLEadvertising is described in Figure 1. BLE has 40 channels, and a beacon uses three of them (channel 37, 38, and 39) to send an advertising message in the prefixed interval. An advertising message can reach about 100 m on the street and 20 m in a forest in summer [33].  
In an advertising message there are two pieces of crucial information: one is UUID that is a unique id of a BLE device (16 octets), and the other is Major/Minor flags (2 octets each). Such information is used to indicate the location of a beacon. There are many types of research to use BLE beacon for indoor navigation such as [34; 35]. However, there are not so many studies using BLE beacon outdoor to provide sightseeing information.  
In addition, the work in [36] discusses on ubiquitous indoor navigation without a smartphone. In Europe, there are some projects to use IoT for sightseeing. One example was TAG CLOUD (Technologies lead to Adaptability and lifelong enGagement with culture throughout the CLOUD) [37]. This is an information providing service that combines a smartphone with a QR code or IC tag at famous places in Europe (Alhambra in Spain, Trendaim in Norway, The Barber Institute of Fine Arts in the UK, etc.). However, the TAG CLOUD requires access to the cloud, and its usability is limited in suburbs and buildings where such access is difficult.
## 5 Influence of Angular Velocity on Hydrodynamic Ram Effect
### Influence of Angular Velocity Direction on Hydrodynamic Ram Effect
Finally, the kinetic energy of the liquid in the boundary layer is insufficient to maintain the downstream flow. Thus, boundary layer separation occurs. The shape of the boundary layer is symmetrical and the maximum thickness lies behind the debris, as shown in Figure 15a. When the debris rotates around the \(y\) axis, the surrounding liquid hydrazine generates a clockwise vortex around the \(y\) axis [24]. The velocity direction of the vortex below the debris is the same as that of liquid, and the velocity of the liquid increases. The separation of the boundary layer accelerates, the separation point moves backwards, and the shape of the boundary layer is no longer symmetrical. The velocity direction of the vortex above the debris is opposite to that of liquid, and the velocity of the liquid decreases. According to the Bernoulli equation, the decrease in liquid velocity leads to the increase in pressure, and the increase in liquid velocity leads to the decrease in pressure. The pressure difference above and below the debris generates the Magnus force, which is shown in Figure 15b. Under the action of the Magnus force, the trajectory of debris deflects to the negative direction of the \(z\) axis, which is consistent with Figure 14.  
Figure 14: Debris trajectory when the debris penetrated the satellite tank with the angular velocities of \(w_{x}=5\) rad/\(\mu\)s, \(w_{y}=5\) rad/\(\mu\)s, and \(w_{z}=5\) rad/\(\mu\)s.  
Figure 15: Flow patterns for liquid hydrazine past the debris without rotation (**a**) and the debris with rotation (**b**). The debris trajectory when the debris penetrated the satellite tank with the angular velocity of \(w_{x}\) = 5 rad/\(\mu\)s is shown in Figure 14. When the debris rotated around the \(z\) axis, the surrounding liquid hydrazine generated a clockwise vortex around the \(z\) axis. The velocity direction of the vortex on the left side of the debris was opposite to that of the liquid flow, the velocity of liquid decreased, and the pressure of the liquid increased. However, the velocity direction of the vortex on the right side of debris was the same as that of the liquid flow, the velocity of liquid increased, and the pressure of the liquid decreased. The pressure difference on the left and right sides produced a transverse Magnus force. Under the action of the Magnus force, the trajectory of debris deflected to the negative direction of the \(y\) axis.
## 6 Conclusions  
Unlike half duplex-based systems, the amount of intercell interference in a full duplex-based system is almost double. In fact, there are two types of intercell interference in the case of full duplex  
Figure 8: The CDF of the average transmit power under different target SINR variance.  
Figure 7: The CDF of the energy efficiency (EE) under different target SINR variance.  
downlink; they are downlink-to-downlink interference and uplink-to-downlink interference, and this is also the case for the full duplex uplink (i.e., downlink-to-uplink interference and uplink-to-uplink interference). This double amount of interference limits the performance of the full duplex schemes in large networks to the extent that it may destroy the merit of the full duplex. In this paper, we proposed a game theoretic solution which guarantees QoS achievement in the case of feasible target SINRs, and provides best effort service otherwise. The game was formulated to perfectly reflect the system, the existence of the Nash equilibrium was proven, and an enabling distributed algorithm was developed. Finally, the simulation results were presented to show the effectiveness of the proposed solution.  
In order to further improve the performance of full duplex schemes in large networks, future research effort should be given to the area of multi-objective optimal solutions (i.e., goal attainment and multi-objective genetic algorithm). Moreover, our game based solution will be generalized to include massive MU-MIMO systems [27; 28].  
The author would like to thank [PERSON] for the thorough review of the manuscript and for the valuable comments that indeed lift the quality of this work.  
The author declares no conflict of interest.
# Investigating the Potential Use of EEG for the Objective Measurement of Auditory Presence
## 3 Results
### Experiment 2  
In Experiment 2, data for one participant were initially ruled out since she stated herself that she could not discriminate between the two programs. Then, data for eight participants were excluded during the estimation of the PAF for the reason outlined in Section 3.1. Thus, 16 sets of valid data were collected, as shown in Table 3 (the dichotomous data are not shown in this table).  
It can be observed from Table 3 that the trend that \"presence score for Program A was higher than that for the other program, and the PAF for Program A was lower than that for the other program\" from Experiment 1 also held true in Experiment 2, though it was not as evident as in Experiment 1. This accords with the fact that the difference between Programs A and C was smaller than that between Programs A and B; that is, Experiment 2 had a more stringent condition than Experiment 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Participant** & **Group** &
\begin{tabular}{c} **Presence Score** \\ **(A > B)** \\ \end{tabular} & **PAF (A < B)** \\ \hline
2 & I & 1 & 1 \\
3 & I & 0 & 0 \\
4 & I & 1 & 1 \\
5 & I & 1 & 1 \\
6 & I & 1 & 1 \\
7 & II & 1 & 0 \\
8 & II & 1 & 1 \\
9 & II & 1 & 1 \\
10 & II & 1 & 1 \\
11 & II & 1 & 1 \\
12 & II & 1 & 1 \\
13 & II & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data in the dichotomous format for the McNemar test.  
A paired \(t\)-test was carried out and showed a significant difference in the presence score between the two programs (\(p\) = 0.016). The difference in the PAF between the two programs was substantial and close to significant (\(p\) = 0.051). Similarly, the correlation between the change in presence score and the PAF was examined using the McNemar test, and no significant difference was found (\(p\) = 1.000). The Pearson correlation coefficient was 0.590 (\(p\) = 0.016) and the value of kappa was 0.590 (\(p\) = 0.018). These results indicated that the correlation observed in Experiment 1 also applied to Experiment 2. That is, using the presence score (subjective measurement) and the PAF (objective measurement) to determine the level of presence yielded significantly similar results.
## 2 Material and Methods
### Simulation Analyses  
#### 2.2.1 Material Model  
The simulation process considered the strain-hardening effect on the rivets and plates. Plastic strains were generated in both the rivet and the plate during the tensile tests; therefore, an isotropic elastic-plastic material model was used. The power-hardening model can be used to characterize its material properties [20].  
The power-hardening model can be expressed by Equation (6):  
\[\sigma_{y}=\left[1+\left(\frac{\dot{\varepsilon}}{C}\right)^{\frac{1}{p}} \right]k\big{(}\varepsilon_{yp}+\varepsilon_{p}\big{)}^{n}, \tag{6}\]  
where \(\sigma_{y}\) represents the yield stress, \(C\) and \(p\) are the Cowper-Symonds constants for the aluminum alloy, equal to 6500 s\({}^{-1}\) and 4, respectively, \(k\) and \(n\) are the strength coefficient and the hardening index, respectively, \(\varepsilon_{yp}\) is the elastic yield strain, and \(\varepsilon_{p}\) is the effective plastic strain.  
The plates were tested, and plate simulations were performed to obtain the plate parameters, and the material parameters were obtained using the continuous response surface method. The plate was 2 mm thick, and the finite element model is shown in Figure 4. The material parameters of the 2024-T4 and LY10 aluminum alloys are shown in Table 2, where \(k\) and \(n\) are the same as in Equation (6). PR is [PERSON]'s ratio, and \(\varepsilon_{\text{max}}\) indicates the failure strain.
## 2 State-of-Art Review
### The Effect of Varying RFA Incorporation Ratio on Concrete Performance
#### 2.2.5 Carbonation Resistance
The water content is one of the main factors that affects RFA concrete's carbonation. Therefore, the w/c ratio should be considered at first, especially when the incorporation level of RFA exceeds 40%. Figure 8: Detail of different particles densities in the same RAC mix. Figure 9: Relative carbonation depth for various RFA incorporation levels regardless of the age of mixes. [PERSON] and [PERSON] [89] measured the carbonation depth at different ages for concrete samples made with 30% and 100% of RFA. Their study confirms that carbonation depth increases as the incorporation of RFA increases. The carbonation depth at 14, 21 and 91 days increased by 100%, 100% and 70%, respectively, when fine NA were fully replaced. From the above observations, it follows that the carbonation rate of concrete with RFA relative to conventional concrete decreases for later ages (Figure 11). Hence, given a conventional concrete mix has an expected 90 days carbonation depth of \(\rm D_{90D}\), the mixes with incorporation % X would have a carbonation depth \(\rm Y_{cd}\) in Equation (4). \[\rm Y_{CD}=D_{90D}+0.041X \tag{4}\]
## 3 Results and Discussion
### Color  
The L*, a*, b* values of raw chicken breast fillet 24 h post-mortem were 58.4, 0.8, and 9.1, respectively (Table 2). Color parameters are usually used as an indicator of the doneness of cooked meat which directly impacts the appearance and attractiveness of the product [8; 19]. The color is mainly affected inside the muscle by myoglobin content, oxidative state of myoglobin, muscle fiber orientation, space between the muscle fibers, packaging conditions, Millard reactions, and pH [48; 49]. Color parameters of chicken breast fillets cooked at different temperature-time combinations are presented in Table 3. The L* value (lightness) was not affected by cooking temperature, time, and their interaction (\(p>0.05\)). A similar result was reported by [PERSON] et al. [29] in sous vide chicken breast cooked at different combinations of temperature (60 and 70 \({}^{\circ}\)C) and time (60, 120, and 180 min). In contrast to this result, [PERSON] et al. [23] reported that sous vide pork cheeks cooked at 60 \({}^{\circ}\)C had a higher L* compared to those cooked at 80 \({}^{\circ}\)C. The authors concluded that samples cooked at lower temperatures preserved more water during cooking which might be released to the surface during the slicing process before color measurement. On the other hand, the chicken breast color can be classified into pale (L* > 53), dark (L* < 46), and normal (46 < L* < 53) based on the L* value [33]. In our study, sous vide chicken breast fillets in all combinations of temperature and time showed a pale appearance.  
The a* value (redness/greenness) ranged from 1.29 to 1.95. The low a*value in poultry meat is mainly due to the presence of white muscle fibers with low myoglobin content [50]. In this study, a* value was affected by cooking temperature and cooking time (\(p<0.05\)). Control samples cooked at 100 \({}^{\circ}\)C revealed a lower a* value than those cooked at 60 \({}^{\circ}\)C and 70 \({}^{\circ}\)C (\(p<0.05\)). A similar result was reported by [PERSON] et al. [28] and [PERSON] et al. [51]. The pink color in poultry meat is evidence of a poorly cooked product. [PERSON] et al. [52] defined a subjective pink threshold at a* = 3.8 in chicken breast fillets. In our study, a* values were under this threshold level at all different temperatures and time combinations. In a general context, a* value is conversely linked to the degree of myoglobin thermal denaturation in cooked meat [23]. Myoglobin thermal denaturation happens quickly with increasing temperature which can directly interact with by-products of lipid oxidation leading to a reduction in a* value [50].  
The b* value (yellowness/blueness) ranged from 14.65 to 15.64. The b* value was not affected by cooking temperature, time, and their interaction (\(p>0.05\)). In contrast to our result, [PERSON] et al. [29] reported that b* value was affected by cooking temperature in sous vide chicken samples cooked at different combinations of temperature (60 and 70 \({}^{\circ}\)C) and time (60, 120, and 180 min).
## 2 Communication Requirements
The applications that will be supported include but are not limited to on-capsule voice communication, station public address, wireless data communication for Hyperloop staff at platforms, capsule departure data communications, augmented reality data communication, and real-time translation of speech data communication. **Business Communication Applications**: passengers onboard also expect to access the internet for business and entertainment such as on-vehicle video conference, online games, chatting, live broadcast, etc. These telecommunication services only focus on passengers with no safety consequences requiring a high data rate and good QoS for multimedia information systems and on-demand streaming services [5]. Typically provided by telecommunication operators, the Hyperloop passenger experience will exceed those of other transportation industries passenger experience in terms of immersiveness and connectivity. Capsules will have augmented reality windows [11] to help the passenger feel like they are looking outside in the real world although being in an enclosed tube. The applications supported include information help points for the public, emergency help point for the public, wireless internet on-capsule for passengers, and wireless internet for passengers on platforms. In Table 1, we give a summary of the requirements of communications. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **GSM-R** & **LTE-R** & **802.11 AX (WiFi-6)** & **5G NR** \\ \hline
**Frequency band** & 876–880 MHz & 450/800/1400 MHz & 2.4/5 GHz & 3.5/28/34/66 GHz \\  & 912–925 MHz & & & \\
**Channel Bandwidth** & 0.2 Mhz & 1.4–20 MHz & 20–160 MHz & 1.25–400 MHz \\
**Max power** & 50 dBm & 50 dBm & 23/27 dBm & 50 dBm \\
**Data Rate** & 172 Kbps/channel & 50/10 Mbps & 600 Mbps & 4 Gbps/channel \\  & & & (80 MHz 1 SS) & \\  & & & 9.6 Gbps & \\  & & & (160 MHz, 885) & \\
**Modulation** & GMSK & BPSK/QPSK/ & BPSK/QPSK/ & QPSK/16-256 QAM/ \\  & & 16 QAM & 16-1024 QAM/OFDM & CP-OFDM \\  & & CP-OFDM & & \\
**FFT SIZE** & - & 256/512/1024 & 256/512/1024/2048 & 256/512/1024/2048 \\
**Max Subcarrier spacing** & - & 15 KHz & 312.5 KHz & 240 KHz \\
**Access** & TDMA/FDD & PDD & TDD & TDD/FDD \\
**Data Transmission** & Circuits switching & Packet Switching & Packet switching & Packet switching \\
**MIMO** & no & 2 \(\times\) 2 & 3 \(\times\) 3 & 2 \(\times\) 2 \\
**Mobility** & 350 km/h & 500 km/h & 300 km/h & 500 km/h \\  & & (special band and & Up to 1200 km/h (special \\  & & configuration) & configuration on confined & configuration/confined \\  & & Custom network & environment) & environment) \\
**Handover time** & 300 ms & - & - & 0 \\
**Handover procedure** & hard & Soft: no data loss & Soft: no data loss & Soft: no data loss \\
**End to end latency** & 100 ms & 20 ms & 10 ms & 1–10 ms \\
**Jitter** & 100–300 ms & 3 ms & 1 ms & 10–100 \(\mu\)s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of communications for critical/performance and business applications.