esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Randomization and Intervention Characteristics  
Following the process of providing informed consent and completing participant screening, individuals were randomly assigned to three different groups (1:1:1) via EPIDAT 4.2 software as follows: Group 1 (CE), Group 2 (HIIT), and Group 3 (Control Group, CG).  
The participants in both the CE group and the HIIT group engaged in supervised exercise sessions via an ergometer bike. The intensity for each participant was set on the basis of their maximum heart rate (HRmax) and expressed as a percentage of HRmax, which was determined by a cardiopulmonary exercise test conducted at the beginning of the study. - CE Group: These participants exercised on the ergometer bike for 40 min, three times per week. The exercise intensity was maintained at 50-60% of their VO\({}_{\text{2 max}}\) and 60-70% of their HRmax and was rated at 11-13 on the Borg scale, ensuring that no respiratory discomfort occurred. Additionally, they took part in strength training twice a week, performing exercises such as bench presses, leg presses, leg curls, rowing, triceps dips, and pec pull. Each session consisted of 15 repetitions per exercise, with a load set at 60-65% of their one-repetition maximum (1 RM), which was assessed at both the start and conclusion of the intervention. - HIIT Group: This group participated in three weekly sessions, starting with a 10-minute warm-up at a moderate intensity (50-60% VO\({}_{\text{2 max}}\), 60-70% HRmax, and a rating of 11-13 on the Borg scale without respiratory discomfort). After the warm-up, they completed four high-intensity intervals, each lasting 4 min, with intensity levels of 85-90% of VO\({}_{\text{2 max}}\) and 90-95% of HRmax, and they were rated 15-17 on the Borg scale, accompanied by mild respiratory discomfort. Between each interval, there was 3 min of active recovery at 50-70% HRmax. Each session ended with a 3-minute cool-down at moderate intensity (50-60% VO\({}_{\text{2 max}}\), 60-70% HRmax, and 11-13 on the Borg scale). The total duration of each session was 40 min. - CG Group: Participants in this group received general advice on the benefits of physical exercise for cardiovascular health, following standard guidelines. They were encouraged to engage in 30 min of walking on most days of the week and to maintain their usual daily physical activities throughout the study [28]. The intervention lasted for 3 months. The participants were considered adherent if they attended at least 70% of the scheduled sessions. Attendance was consistently tracked and documented throughout the study. In cases where participants could not fully comply with the training schedule for unforeseen reasons, the training period could be extended by up to 4 weeks to allow for completion. Figure 1 provides a flowchart illustrating the participant journey, from screening and inclusion/exclusion criteria to randomization, baseline testing, and follow-up.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 5 Performance Evaluation
### Influence of Different Value of Time Segment Length
This is because the topology duration in the initial visibility calculation results was close to 300 s. Therefore, when the time segment was less than or equal to 300 s, there was almost no loss of visible time segment when aligning the start and end time of the visible time segments to the equal-length time segments. When the time slice was greater than 300 s, during the alignment process, many available visible time segments were partially or completely cut off, resulting in decreased average ISL duration. Figure 6 shows the experimental results for maximum delay and average delay under different time segment lengths. We can see, from the figure, that when the time segment was less than or equal to 300 s, the maximum delay and average delay were consistent. When the time segment length was greater than 300 s, the maximum delay and average delay increased, while the network performances decreased as the time segment length increased. This further confirms the previous statement: when the time segment length was greater than 300 s, during the process of alignment to the equal-length time segments, many available visible time segments were partially or completely cut off. The number of ISLs built in the network decreased, thus increasing the maximum delay and average delay. The experimental results indicate that, in order to improve the stability of the network topology as much as possible without reducing the network transmission performance, the time segment length should be set to 300 s, which allows for a better trade-off between topology stability and network performance. Therefore, we used 300 s as the time segment length of LPTSO FSA for the Walker Delta 32/4/1 constellation.
## 5 Results and Discussion
### Average End-to-End Delay  
Average end-to-end delay (ETE) is calculated to study the performance of MA route planning protocols to capture and aggregate sensory recordings and deliver results to the sink. The number of aggregated sensory recordings and the length of MA migration path are two key parameters that influence ETE. As Figure 5 shows, MINDS increases ETE compared to the benchmarks, especially in large and crowded networks where the data density proportion is high. This is because of the protocol's ability to find a greater number of event regions to collect data records. Indeed, delay is increased as MINDS spends a greater deal of time to collect a greater number of data records and report the results to the sink.  
ZMA reduces ETE as compared to MINDS. This is because of utilising bottom-up MA movement through which mobile agents are forwarded from the middle of event regions to collect data records. That way, MAs do not need to establish a journey from the sink to the event regions; they collect sensory data from each region and deliver the aggregated results to the sink. ZMA assumes that each (zone leader) LDS dispatches an MA to a data zone for data collection. This will increase the parallelism degree of MA data collection and reduce ETE when a greater number of MAs move to collect sensory data samples. However, MINDS slightly underperforms when the data density is low and the network deployed is small. This is because of the ability of MINDS to minimise path hop-count from the sink to LDSs. MAs do not need to reactively establish a path; they migrate through the minimum hop-count routes which are already available. This reduces ETE when a small number of LDSs have sensory data to be collected.  
ETE is reduced in TBID compared to ZMA and MINDS. This is because TBID works according to an address-centric fashion, and therefore has no ability to find the event regions (or LDSs) throughout the network. That way, TBID leaves a number of LDSs non-visited, especially if the network is large and crowded. Moreover, TBID offers no minimum hop-count paths to forward MAs to collect and aggregate sensory recordings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Range** & **Parameter** & **Range** \\ \hline Simulation time & 3600 s & network initialisation & 100 s \\ repetition & 250 & ambient noise and obstacles & disabled \\ communication range & 200 m & node distribution model & random \\ LDS distance & 500 m (grid) & Area size & 5.5 km \(\times\) 5.5 km \\ MAC & CSMA/CA & Communication protocol & IEEE 802.11 \\ mobility pattern & minimum traffic & Data density & 25\%, 50\%, 100\% \\ Node count & 256, 1024, 4096 & MA speed & 30 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.
# Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
## 3 Results
### Numerical Experiments of the Layered Model
#### 3.2.3 Numerical Experiment Based on Petrophysical Relationship Constraints
Next, the applicability of the multi-parameter FWI strategy based on rock physical constraints proposed in this paper was verified in a complex geological model.
## 2 Mechanical Modeling of the ISU System
### Kinematic Analysis
#### 2.2.3 Calculation of the Side Force Applied to the Cylinder Wall  
The loads acting on each component of the suspension system are illustrated in Figure 6. These loads were induced by the compression and expansion of nitrogen gas due to the wheel movement, as well as the pressure drop that occurs when the hydraulic fluid passes through the damper.  
The side force \(F_{cs}\) applied to the cylinder wall and the force \(F_{con}\) acting on the connecting rod can be expressed as:  
\[F_{con}=\frac{F_{P}-\mu F_{cs}}{\cos(\theta_{3})} \tag{19}\]  
\[F_{con}=F_{\text{con}}^{PR}+F_{\text{con}}^{c} \tag{20}\]  
\[F_{cs}=(F_{P}-\mu F_{cs})\text{tan}(\theta_{3}) \tag{21}\]  
where \(F_{P}\) represents the parallel force acting on the main piston, \(\mu F_{cs}\) denotes the friction force induced by the side force \(F_{cs}\), \(F_{\text{con}}^{PR}\) corresponds to the force acting on the connecting rod, calculated based on the real gas model, and \(F_{\text{con}}^{c}\) represents the damping force acting on the connecting rod.  
The friction coefficient \(\mu\) between the piston and the cylinder wall was set to zero to simplify the nonlinear force equilibrium analysis.  
Figure 6: Relationships of loads on linkages.  
A system of two nonlinear equations with a single unknown variable was obtained by solving Equations (19) and (20) simultaneously. The solution to this system provided the value of \(F_{cs}\).  
The connecting rod remained aligned with the central axis of the main cylinder in both the static and the F.J. positions within the local coordinate system, as shown in Figure 3. This structural design ensured that the side force was reduced to zero both in the initial installation state of the ISU system and when the nitrogen gas pressure reached its maximum value at the F.J. position.  
To achieve this, the crank length \(L_{2}\) can be determined using the moment equilibrium equation about the rotational center of the arm in the static position, using the following expressions:  
\[P_{ni}A_{o}L_{2}\text{cos}\ \frac{1}{2}\theta_{FJ}=m_{s}\,gL_{1}\text{cos}\ \theta_{ST} \tag{22}\]  
\[L_{2}=\frac{NL_{1}\text{cos}\ \theta_{ST}}{P_{ni}A_{o}\text{cos}\ \frac{1}{2} \theta_{FJ}} \tag{23}\]
## 4 Results and Discussion
### Divergence of Attributes
#### 4.3.6 Inspections  
Conducting inspections is a critical activity in the construction of any project. It typically involves significant time spent on traveling to the site, accessing relevant information, and sometimes consulting with other experts [53]. The ability to conduct inspections using AR for communication might ease the challenges related to the inspector or engineer's physical presence. Several papers mentioned that AR application could facilitate inspections [10, 17, 35, 45, 47, 54]. However, [PERSON] et al. (2021) mentioned that not all types of inspections could be conducted using AR, specifying that complex and in-depth inspections still needed a physical presence on site [46]. By reviewing the context of each paper, we noticed that in-depth inspections were mentioned as a challenge only when interviewing experts through a questionnaire [46], whereas the remaining papers that mentioned using AR to conduct inspections was a benefit were conducted in uncontrolled environments [10, 17, 35, 45, 54, 55, 56] where practitioners used AR on site [57]. Although exploring the perceptions of future users is important, this does not mean that their insights are 100% true and might not change while implementing the application [58, 59]. This indicates that conducting inspections using AR is independent of the contexts in which the user exists in.
## 4 Proposed Localization Method  
The evaluation of the CRLB in Section 3 has demonstrated the potential of calibration targets in improving localization accuracy. In what follows, we will proceed to develop a novel closed-form solution for the aforementioned practical localization scenario where the positions of transmitters and receivers are inaccurate but calibration targets are used to refine the transmitter/receiver position and enhance the localization accuracy. After that, a theoretical analysis will be performed to show that the proposed solution achieves the CRLB when satisfying some mild conditions.
# A Study on the Normalized Delineation of Airspace Sectors Based on Flight Conflict Dynamics
## 5 Validation and Analysis of Sectorization Algorithms
### Busy/Idle Time Slots  
The flight radar data of Xiamen regional sector from 13 December 2021 to 19 December 2021 were collected as a sample, a total of seven days of operation data, during which there is no strong interference time, and it can be assumed that the data used in this paper are the radar data under normal operation. Record the radar flight data in 10 min intervals, calculate the total nodes, average number of nodes, average point strength, average weighted agglomeration coefficient, network density, and network efficiency of the flight conflict network at each step, and obtain the timeseries of network indicators as shown in Table 3. Timeseries of network indicators.  
The integrated network metrics for intercepts 1-1009 are plotted as a line graph as shown in Figure 7, with the red lines showing the values for the busy and idle time slot divisions.  
According to the data in Figure 7, ignoring the differences, the comprehensive network indicators have a certain periodicity in the process of time change, and the overall trend is \"low plateau period--high plateau period--low plateau period\". Taking the criteria of busy time and idle time as the boundary, it can be divided into the stage of high value of comprehensive network indicators and the stage of low value of network indicators, and roughly deduced that the daily busy time is from 10:00 to 20:00, and the idle time is from 0:00 to 10:00 and from 20:00 to 24:00.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Records** & **Total** & **Average** & **Average Point** & **Average** & **Network** & **Network** & **Integrated Network** \\
**Number** & **Nodes** & **Node** & **Strength** & **Aggregation Factor** & **Density** & **Efficiency** & **Indicators** \\ \hline
1 & 10 & 0.3800 & 0.1025 & 0.0836 & 0.0121 & 0.0089 & 1.6842 \\
2 & 12 & 0.4023 & 0.1347 & 0.0985 & 0.0164 & 0.0095 & 2.0123 \\
3 & 11 & 0.3940 & 0.1294 & 0.1003 & 0.0148 & 0.0092 & 1.8520 \\
4 & 12 & 0.4136 & 0.1408 & 0.1254 & 0.0167 & 0.0102 & 2.0206 \\
5 & 9 & 0.3721 & 0.1066 & 0.0932 & 0.0119 & 0.0076 & 1.5269 \\... &......... &......... &......... &......... &......... &......... \\
1007 & 8 & 0.3811 & 0.0987 & 0.0741 & 0.0106 & 0.0062 & 1.3653 \\
1008 & 11 & 0.3932 & 0.1183 & 0.0927 & 0.0152 & 0.0086 & 1.8489 \\
1009 & 13 & 0.4105 & 0.1489 & 0.1264 & 0.0209 & 0.0106 & 2.1800 \\ \hline \end{tabular}
\end{table}
Table 3: Timeseries of network indicators.  
Figure 6: Simulation of the Xiamen control sector.
## 2 Experimental Methodology  
### Preparation of jointed Rock Mass Specimens  
Finite limestone, a medium-strength sedimentary rock from Finike in southern Turkey, was used in this study to fabricate jointed rock mass specimens. To fabricate the jointed rock mass specimens, an intact rock block measuring 360 mm \(\times\) 360 mm \(\times\) 250 mm, free from bedding planes or joints, was cut into replicate blocks of uniform thickness and bound with cement mortar layers to simulate joint planes. As an example, a joint spacing of 30 mm was achieved by cutting the intact rock block into 12 replicate blocks (360 mm \(\times\) 250 mm \(\times\) 30 mm), which were then bound using 2 mm thick cement mortar layers. The assembled blocks were placed in a steel mold, and the remaining space was cast with concrete. Figure 2 provides a clearer understanding of the fabrication process and the dimensions of the jointed rock mass specimens.  
The uniaxial compressive strength (\(\sigma_{c}\)), elastic modulus (\(E\)), and Poisson's ratio (\(\
u\)) of Finike limestone, cement mortar, and concrete, which constitute the jointed rock mass specimens, were determined in accordance with ASTM D7012-14 [33]. The tensile strength (\(\sigma_{t}\)) and density (\(\rho\)) were additionally measured following ASTM D3967-08 [34] and ASTM C97/C97M-15 [35], respectively. Table 1 lists the physical and mechanical properties of these materials.  
The cement mortar used to simulate the artificial joint planes had significantly lower strength than the intact rock, allowing the specimens to realistically represent natural jointed rock masses. As the joint planes were identical in all specimens, factors such as joint roughness and strength were reasonably excluded from the analysis.
## 3 Results and Discussion
### Quasi-Static Compression Testing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Structure** & **\(\rho^{\bullet}\)+** & **\(\rho^{\bullet}\)/\(\rho_{\text{S}}\)+** & **\(\sigma^{\bullet}\)+** & **\(\sigma^{\bullet}\)/\(\sigma_{\text{S}}\)+** & **E\({}^{\bullet}\)+** & **E\({}^{\bullet}\)/\(\rho_{\text{S}}\)+** \\  & **[kg/m3]** & **[-]\(\times\)10\({}^{-2}\)** & **[kN/m3]** & **[-]\(\times\)10\({}^{-2}\)** & **[kN/m2]** & **[-]\(\times\)10\({}^{-2}\)** \\ \hline CP & 78.8 \(\pm\) 0.6 \({}^{\text{a}}\) & 6.60 \(\pm\) 0.05 \({}^{\text{a}}\) & 181.4 \(\pm\) 0.1 \({}^{\text{a}}\) & 4.6 \(\pm\) 0.3 \({}^{\text{a}}\) & 15.5 \(\pm\) 4.3 \({}^{\text{b}}\) & 2.56 \(\pm\) 0.73 \({}^{\text{b}}\) \\ BCC & 139.1 \(\pm\) 2.8 \({}^{\text{b}}\) & 12.10 \(\pm\) 0.24 \({}^{\text{b}}\) & 167.5 \(\pm\) 0.1 \({}^{\text{a}}\) & 4.1 \(\pm\) 0.8 \({}^{\text{a}}\) & 1.8 \(\pm\) 0.5 \({}^{\text{a}}\) & 0.29 \(\pm\) 0.08 \({}^{\text{a}}\) \\ FCC & 139.2 \(\pm\) 9.1 \({}^{\text{b}}\) & 12.12 \(\pm\) 0.79 \({}^{\text{b}}\) & 357.4 \(\pm\) 0.3 \({}^{\text{b}}\) & 8.8 \(\pm\) 2.7 \({}^{\text{b}}\) & 16.3 \(\pm\) 3.0 \({}^{\text{b}}\) & 2.71 \(\pm\) 0.50 \({}^{\text{b}}\) \\ BCC + CP & 155.8 \(\pm\) 6.7 \({}^{\text{c}}\) & 13.6 \(\pm\) 0.58 \({}^{\text{c}}\) & 938.8 \(\pm\) 0.6 \({}^{\text{c}}\) & 23.2 \(\pm\) 4.1 \({}^{\text{c}}\) & 40.5 \(\pm\) 5.6 \({}^{\text{c}}\) & 6.72 \(\pm\) 1.33 \({}^{\text{c}}\) \\ FCC + CP & 182.9 \(\pm\) 8.2 \({}^{\text{d}}\) & 15.8 \(\pm\) 0.71 \({}^{\text{d}}\) & 1244.0 \(\pm\) 1.6 \({}^{\text{d}}\) & 30.7 \(\pm\) 6.3 \({}^{\text{d}}\) & 53.8 \(\pm\) 8.9 \({}^{\text{d}}\) & 8.92 \(\pm\) 1.81 \({}^{\text{d}}\) \\ Solid & 1153.9 & 1 & 40,601.2 \(\pm\) 5.2 & 1 & 600.8 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mechanical properties of the different lattice structures.
## 3 Results
### S. pratensis S10 Promoted Wheat Plant Growth  
Based on the above results, the PGP effects of S10 were tested in wheat plants. As shown in Figure 2, the plant growth-promoting effect increased with the increased concentrations of S10 and then declined at a high concentration. The optimal effect was observed at a concentration of \(10^{8}\) CFU/mL. When compared with the control, the quantitative results indicated that the shoot length, root length, fresh weight, dry weight, root activity, and chlorophyll content of the wheat seedlings inoculated with S10 spore suspension (at \(10^{8}\) CFU/mL) were increased by 17.89, 23.08, 24.99, 26.98, 44.21, and 18.72%, respectively (Figure 2B-F).  
Figure 1: Evaluation of plant growth-promoting traits of _S. pratensis_ S10 in vitro. (**A**) Hydrogen cyanide (HCN) production. (**B**) Siderophore production. (**C**) Phosphate solubilization activity. (**D**) 1-aminocyclopropane-1-carboxylic acid deaminase (ACC) activity. Left is control and right indicates ACC production. (**E**) Ammonia production. Left is control and right indicates ammonia production. (**F**) Indole-3-acetic acid (IAA) production. Left is control and right indicates IAA production. (**G**) Nitrogen fixation ability.  
Figure 2: The wheat plant growth-promoting activity of _S. pratensis_ S10. (**A**) Promoting effects of strain S10 on wheat growth and development in pot experiment. Overall appearance of wheat shootsand roots. Photos were captured at 20 days post inoculation (dpi) with different concentrations of S10 spore suspension. (**B-F**) Lengths, fresh weight, and dry weight of wheat roots and shoots as well as root activity (TTC) and chlorophyll contents of plants were measured at 20 dpi with different concentrations of S10 spore suspension. Data are presented as mean \(\pm\) SE of three independent experiments consisting of three biological replicates (\(n\) = 50, \(p\) < 0.05, ANOVA, Duncan's test). Lowercase letters on bars indicate significant differences. SDW represents wheat plants treated with sterile distilled water used as a control. 1 \(\times\) 10\({}^{7}\), 1 \(\times\) 10\({}^{8}\), and 1 \(\times\) 10\({}^{9}\) indicate the different concentrations of S10 spore suspension (CFU/mL).
# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 5 Discussion  
We can find the overall result of correlation is between 0.331 and 0.370 in Table 2. There are three main reasons why the correlation is so low.  
(1) The retargeted image database is complex. This database includes lots of irregularly-textured areas, such as grass, water, or trees, and the images in database are retargeted by removing, inserting, or optimizing pixels (or patches) to preserve image content, so the size of the retargeted image is very different from the original image. By this way, the images contains either dense information or global and local structures that may be damaged during resizing.  
(2) PLCC, SRCC, and KRCC are three main statistical correlation coefficients, which describe the linear or rank correlation between subjective score and algorithm score. However, the subjective score of retargeted images is obtained by averaging many scores according to 210 participants, which affects the linearity or rank of subjective score and algorithm score.  
(3) This paper uses a reduced reference retargeted image quality assessment algorithm, which only uses part of the reference image information, rather than all the information, so the correlation is relatively low.  
We could find that our algorithm was better than all the given FR IQA algorithms for retargeted images. The reason was that our algorithm not only considered the local metric (Local EMD) but also considered the global saliency features (Global EMD), while the FR IQA algorithms for retargeted images only used the local metrics. We also found that our algorithm was better than all the given RR IQA for non-retargeted images algorithms. That was because our algorithm did not depend on the same size between reference image and retargeted image, while the compared RR IQA algorithms often need the same sizes between two images. When the two sizes were different, the non-retargeted algorithms would compare their common regions directly. As a matter of fact, they only evaluated a part of images.  
Because GEMD just used ten visual saliency features, its evaluation results were not better than many other algorithms. However, it provided a good complement to LEMD and improved the overall evaluation results.