esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Retronasal Aroma Release Analysis Using PTR-TOF-MS  
The retronasal aroma release profile of the NCS was analyzed by ten panelists (three males and seven females, aged 22-61 years) using PTR-TOF-MS. The study adhered to the research ethics guidelines of Kyushu Sangyo University and verbal consent was obtained from all participants prior to the experiment. NCS in liquid form (15% \(w/v\)) was analyzed because the liquid form may less impede the physical flow of the sample in the panelists' mouths compared to the powder form. Retronasal analysis was performed using a PTR-TOF 1000 ULTRA (Ionicon Analytik, Innsbruck, Austria) equipped with a capillary inlet hose and nose- and mouth-piece adapters. The MS detection limit was set at 10 ppt-V, with data recorded at one-second intervals. The ionization was performed at an E/N ratio of 122 Td (drift temperature, voltage, and pressure at 120 \({}^{\circ}\)C, 479.9 V, and 2.30 mbar, respectively). The panelists were asked to drink 20 mL water from a disposable paper cup, hold the water in their mouth for 5 s, and swallow it. After swallowing, they were required to put on a nose-piece adapter, immediately exhale for 2 s from the nose, and then inhale through the nose for 2 s. Exhalation-inhalation breathing was performed for seven consecutive cycles. After a 10 s break, the panelists were instructed to consume 20 mL of NCS solution and perform 2 exhalation-inhalation breathings through the nose (in-nose retronasal) for seven times. Afterward, they were asked to rest for 5 min and rinse their palates with water by gargling five times to prevent the NCS solution from remaining in their mouths. The same process was repeated for the in-mouth retronasal measurement, where the panelists were instructed to consume water or NCS solution and breathe through the mouth into a mouth-piece adapter. The room temperature during the experiment was maintained at 24 \({}^{\circ}\)C.
## 5 Conclusions  
This research develops a novel DRL-based approach called Graph-STNN for mobile robot autonomous exploration in unknown environments. We modeled the exploration problem as an exploration graph that considers the historical trajectories, frontier waypoints, landmarks, and obstacles. We employ GCN as a spatial encoder to extract topological structure information, TCN as a temporal encoder to extract sequence information of trajectories, GRU to capture dynamic state change of spatiotemporal information to obtain the current state feature. The simulation experiment shows that our approach could choose a better target point by considering obstacle avoidance. Besides, the results also show that the method can promote the exploration efficiency by both using temporal and spatial information. In addition, it performs better robustness and generalization by using hybrid global information. Next, we are going to validate the network on physical robots, and DRL-based path planning and heterogeneous graphs will be considered.  
Conceptualization, Z.Z. (Zhiwen Zhang), C.S., P.Z., Z.Z. ([PERSON]) and H.Z.; methodology, [PERSON] ([PERSON]), C.S. and P.Z.; software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]) and P.Z.; investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), C.S., P.Z., Z.Z. ([PERSON]) and H.Z.; supervision, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China, grant number U1913202, and also supported by National Key R&D Program of China, grant number YFC20170806503.  
Not applicable.  
The data presented in this study are available at [[https://github.com/zhangzw-nudt/Graph-STNN_Exploration](https://github.com/zhangzw-nudt/Graph-STNN_Exploration)]([https://github.com/zhangzw-nudt/Graph-STNN_Exploration](https://github.com/zhangzw-nudt/Graph-STNN_Exploration)) (accessed on 22 July 2021).  
This study is a part of the M.S. research of the corresponding author. We are grateful to anonymous reviewers and editors for their suggestions.  
The authors declare no conflict of interest.  
Figure 16: The test result of our autonomous exploration algorithm in Stage. (**a**) The Stage scene. In this scene, the black squares are obstacles, the green square is the robot’s position, the blue squares are the robot’s historical trajectories, and the light blue area is the radar coverage area. (**b**) The explored map. In this map, the black grids are obstacles, the white grids are the free areas, and the dark green grids are unknown areas. The blue curve is the robot’s historical trajectories and the blue line indicates that a closed loop has been detected. Red and green curves are the robot’s current path.
## 3 Data Experiment
### Numerical Simulation  
Ansys Workbench was adopted to perform the numerical simulation for the thermal analysis. Given the axisymmetric nature of the LNG storage tanks, a 2D numerical model was used for steady thermal analysis, as shown in Figure 6. The numerical model involves the steel inner tank, reinforced concrete outer tank, perlite, LNG, and gas. The material  
Figure 4: An exemplar of multi-fidelity machine learning with MLP.  
Figure 5: Site view of LNG storage tank.  
parameters used for numerical simulation are listed in Table 2. These parameters were selected based on previous investigations [33; 34]. The assumptions of the numerical simulation include that (a) the temperature of LNG is \(-162\)\({}^{\circ}\)C, (b) the environmental temperature has uniform effects on the outer tank, and (c) there is no thermal contact resistance. The boundary conditions of the model include that (a) the convective heat transfer coefficient between air and the outer tank is 0.0267 W/(m\({}^{2.\circ}\)C), (b) the heat flux at the model bottom is 0.47 W/m\({}^{2}\) (18 \({}^{\circ}\)C), and (c) the convective heat transfer coefficient for sunshine is 1367 W/(m\({}^{2.\circ}\)C).  
relationship between perlite settlement and temperature field is shown in Figure 8, with the same environmental temperature and LNG level (30 \({}^{\circ}\)C and 40 m, respectively). It was shown that the perlite settlement will significantly influence the temperature field of outer tanks. Such a relationship is fundamental for the proposed data-driven approach to realise the intelligent identification of thermal insulation integrity.  
This study is ahead of the service of the LNG storage tank, so there is no real-world data to be utilised as high-fidelity data. Nevertheless, to validate the efficacy of the proposed multi-fidelity framework, 32 data points that were also from the numerical simulation were adopted as high-fidelity data. The high-fidelity data was systemically biased (compared to low-fidelity data) by using a different coefficient of thermal conductivity (0.036 W/(m\({}^{\circ}\)C)) for perlite. To ensure the scarce nature of high-fidelity data, only 32 data points were generated as high-fidelity data, which is far less than the 1848 low-fidelity data. Such considerations ensure that the adopted high-fidelity data has the essence of real-world data, compared to low-fidelity data. As a result, the dataset including both low- and high-fidelity data was established, based on the parameter combinations which are listed in Table 3.  
Figure 8: Relationship between temperature field and perlite settlement.  
Figure 7: An exemplary result from the numerical simulation.  
As shown in Figure 9 (presented case: 1 m perlite settlement and 30 m LNG level), the comparison of low- and high-fidelity data presents their discrepancies, which is a challenge for machine learning generalisability.
## 3 TCM Protocol  
### Network Model and Hypothesis  
The schematic network model in this paper is shown as Figure 1. Marine environmental monitoring is its application background. In the monitoring area, nodes on the sea surface may move with different speed and direction in a certain range under the impulse of the waves and winds. The mobile monitoring ship is just like the mobile sink in the network system. Since the mobile WSN has totally different features from a static WSN, and it is relatively more complex, for discussion convenience, we pose some hypothesis for the network model:  
1. The physical properties for all the nodes like the configurations of energy and hardware are the same.
2. All the nodes are on the same plane and their original positions are random.
3. All the nodes can move in the monitoring area with a random speed and direction.
4. The sink nodes have more abundant resources than the ordinary sensor nodes, such as communication range and storage space.  
Figure 1: The schematic model of the network.  
The mobile sink moves in the network and communicates with the nodes in its communication range, and it will collect the data fused by cluster heads along the planned route. It takes on the topology establishment and maintenance tasks. The base station is fixed and it communicates with the cluster heads, in fact, it can transmit the information collected by the sensor nodes to the control center but the mobile sink cannot.
## 5 Conclusions  
This paper has presented a dual-band and low-profile single-element Minkowski-Sierpinski fractal antenna and a high-gain, directional four-element (2 \(\times\) 2 configuration) Minkowski-Sierpinski fractal antenna array resonating at 4.17 and 5.97 GHz. The single-element antenna was initially designed using two different substrates, i.e., a high-end Rogers TMM4 and a low-end FR4 substrate. In order to feed the fractal antenna array, a Wilkinson power divider based on delta-stub was also designed to achieve a wide band and equal power split from the input ports to all four elements of the fractal antenna array. It is important to observe here that most of the previous dual-band antenna arrays presented in Table 3 radiate in the boresight at only one frequency and the other frequency is null, but the proposed four-element fractal antenna array from this work has a radiation pattern in the boresight at both resonant frequencies with high gain and good bandwidth. Due to this unique feature of the radiation pattern, the proposed antenna array can act as an ideal candidate for currently deployed high-efficiency IEEE 802.11 ax WLANs (Wi-Fi 6E), the upcoming 5G wireless systems, the IoV, and FANETs system, where V2V/V2I and U2U/U2B wireless communication links are heavily dependent on the directional beam-steering capabilities of the antenna arrays. The proposed antenna array shows very good agreement between the simulated and measured results. The results show that the proposed structure is a promising candidate for IEEE 802.11 ax WLANs (Wi-Fi 6E) and future 5G wireless communication. In the future, this work can be extended by reducing the overall size of the array using the combination of all the components on the single ground plane and removing the coaxial cable and other non-essential components to check and study the overall behavior of the antenna array system.  
[PERSON] and [PERSON] coined the idea, led, supervised, and evaluated the entire project, edited the write-ups, designed and simulated the proposed antennas. [PERSON], [PERSON] and [PERSON] performed the experimental measurements. [PERSON] completed the initial draft of the paper; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] then analyzed, evaluated, and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Deanship of Scientific Research, Islamic University of Madinah, Madinah, Saudi Arabia, Grant Number 171\".  
Not Applicable  
The authors would like to acknowledge the funding from the Deanship of Scientific Research, Islamic University of Madinah, Madinah, Saudi Arabia, under the Research Grant No. 171.  
The authors declare no conflict of interest.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 4 Approaches for Preventing Sticking of MXenes
### Integration with Polymer Matrices or Carbon Nanomaterials  
One effective approach to prevent sticking in MXene electrodes is to integrate MXenes within polymer matrices or carbon nanomaterials. This strategy provides physical separation and dispersion stability for MXene flakes, thereby reducing their tendency to agglomerate.
## 2 Materials and Methods
### The Model and Components
#### 2.1.2 Brayton Cycle  
A compressor is a turbo machine. The compressor increases the pressure of the compressible fluids. The ratio of the outlet pressure to the inlet pressure is known as the compressor pressure ratio, which shows the increase in pressure in the system. The mass balance of the compressor is defined as  
\[\dot{m}_{int}=\dot{m}_{out}. \tag{5}\]  
The energy balance is given as  
\[\dot{m}_{int}h_{int}=\dot{m}_{out}h_{out}+P. \tag{6}\]  
In order to calculate the required performance,  
\[P=\dot{m}C_{p}(T_{\cdot})T_{int}\eta_{comp}, \tag{7}\]  
where \(C_{p}\) stands for the specific heat capacity of air and at the average temperature, and  
\[T_{\cdot}=\frac{(T_{int}+T_{out})}{2}. \tag{8}\]  
For isentropic efficiency of the compressor,  
\[\eta_{isen,comp}=\frac{(h_{out,isen}-h_{int})}{(h_{out}-h_{int})}. \tag{9}\]A combined cycle powerplant comprises the gas turbine as a section of the system. The enthalpy of the flue gas is transformed into mechanical energy by the gas turbine, which in turn drives the generator that produces the electrical energy. The other output of the gas turbine allows the waste heat from the turbine to pass out. This waste heat can be passed out to a heat recovery steam generator (HRSG) which is used to produce steam, as well as drive a steam turbine. Various fuels can be used to operate a gas turbine. These fuels include natural gas (which is used for the system in this paper), crude oil, and biogas. The fuel range of a gas turbine can be extended to include coal and also biomass by applying the integrated gasification combined cycle (IGCC) [26].  
Considering that heavy-duty gas turbines have a much higher inlet temperature, in comparison with the steam turbine, they need cooling of their first row of blades. Due to this cooling, the mathematical model of the gas turbine is different from that of the steam turbine. Hence, the whole gas turbine has to be separated into different sections due to the presence of air cooling. The combustion gas mixes with the air cooling from a section, and this is computed in the mass balance equation of the turbine section. Founded on actual data, [PERSON] and [PERSON] [27] proposed a formula for the calculation of the mass flow of the cooling air needed at nominal load:  
\[\dot{m}_{cool}=\dot{m}_{air}\left(3.1817\cdot 10^{-4}T_{inl}-0.2454\right), \tag{10}\]  
where \(\dot{m}_{air}\) is the total air mass flow rate, while \(T_{inl}\) is the inlet temperature in \({}^{\circ}\)C.  
At partial loads, [PERSON] and [PERSON] [28] developed the following equation:  
\[\dot{m}_{cool}=\dot{m}_{cool,nom}\frac{p}{p_{nom}}\sqrt{\frac{T_{nom}}{T}}, \tag{11}\]  
where the subscript \(nom\) denotes the nominal state.
# An Equal Precision Programmed Cymometer Design Using Low-Power Technique
## 2 Working Principles
### Frequency Measurement Design Methods  
For the measurement frequency value, assuming that the standard frequency is \(f_{s}\), the count value of the measurement signal is \(N_{x}\), and the value of the standard signal is the count value set in the main preset gate time \(T\), which is \(N_{s}\); the measurement frequency \(f_{x}\) is established as the following formula [18; 19]:  
\[f_{x}=\left(f_{s}\times N_{x}\right)/N_{s} \tag{1}\]  
It can be seen from Formula (1) that if the error of standard frequency \(f_{s}\) is ignored, the possible relative error \(\delta\) of equal precision frequency measurement is shown in Formula (2):  
\[\delta=\left(|f_{xx}-f_{x}|/f_{xe}\right)\times 100\% \tag{2}\]  
where \(f_{xx}\) is the accurate value of the measured signal frequency.  
In the measurement, since the start and stop times of \(f_{x}\) counting are triggered by the rising edge of the signal, there is no error in the \(f_{x}\) counting \(N_{x}\) within the preset gate time \(T(T=N_{x}T_{x})\). There is a counting error of \(N_{s}\) for \(f_{s}\), that is \(\mathrm{\Delta}N_{s}\mathrm{\,|\,}\leq 1\), and the measurement frequency is shown in Formula (3) [20]:  
\[f_{xe}=[N_{x}/(N_{s}+\ \mathrm{\Delta}N_{s}\ )\ ]\times f_{s} \tag{3}\]  
Substituting Formulas (1) and (3) into Formula (2), the relative error \(\delta\) can be optimized as Formula (4):  
\[\delta=\left|\mathrm{\Delta}N_{s}\right|/N_{s}\ \leq 1/N_{s}=1/(T\times f_{s}) \tag{4}\]  
It can be seen from the Formula (4) that the relative error of the measured frequency is independent of the size of the measured signal frequency and is only related to the preset gate time and the standard signal frequency, that is, the equal precision measurement of the entire test frequency band can be achieved. The longer the time, the higher the standard frequency, and the smaller the relative error of frequency measurement [21; 22].
# Effect of Contralateral Cervical Glide on the Suprascapular Nerve: An In Vitro and In Vivo Study
## 3 Results  
### Anatomical Study  
The CLCG resulted in a proximal glide of the SNe toward the spine measured proximal to the SNo (see Figure 3A) and distal to SGN (see Figure 3B). The Sne's proximal average movement was 1.85 mm at the SNo level and 1.45 mm at the SNG level (95% CI). Summarized mean displacements are presented in Table 1.  
Similarly, pulling the infraspinatus muscle resulted in a distal glide of the SNe toward the infraspinatus fossa (see Figure 3C), with an average movement of 3.21 mm at SNo level (95% CI). The movement at the SNo was particularly prominent when pinching the infraspinatus muscle, showing that this maneuver pulled the SNe toward the muscle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cadaveric Study** & & \multicolumn{2}{c}{**In Vivo Study**} \\ \hline \multicolumn{2}{c}{Contralateral cervical glide observed at the suprascapular notch.} & 1.85 (1.73, 1.98) mm & Horizontal & 1.18 (0.79, 1.31) mm \\ \multicolumn{2}{c}{} & Vertical & 0.39 (0.23, 0.54) mm \\ \hline \multicolumn{2}{c}{Infraspinatus muscle contraction observed at the suprascapular notch (simulated in the dissection study).} & 3.21 (2.93, 3.46) mm & Horizontal & 1.34 (1.05, 1.75) mm \\ \multicolumn{2}{c}{} & Vertical & 0.75 (0.47, 1.02) mm \\ \hline \multicolumn{2}{c}{Contralateral cervical glide observed at the spinoglenoid notch.} & 1.45 (1.35, 1.56) mm & Horizontal & 1.65 (1.08, 2.00) mm \\ \hline \multicolumn{2}{c}{This table summarizes the average estimated movement of the suprascapular nerve. For the cadaveric study, the mean movement is presented along with its 95% confidence interval. For the in vivo study, a trimmed mean—excluding 5% of extreme values—is shown, also accompanied by its 95% confidence interval. All values reported correspond to the mean or trimmed mean with their respective 95% confidence intervals.} \\ \end{tabular}
\end{table}
Table 1: Suprascapular nerve displacement.  
Figure 3: Dissection images of the anatomical results. Anatomical structures are marked as follows: 1: suprascapular nerve (SNe); 2: omohyoid muscle; 3: superior transverse scapular ligament (STSL); 4: suprascapular notch (SNo); 5: superior ring (a silicone ring situated around the SNe before the STSL); 6: infraspinatus muscle; 7: scapula; 8: inferior ring (a silicone ring situated around the SNe motor branch); 1a: motor branch into the infraspinatus muscle; 1b: motor branch into the supraspinatus muscle; contralateral cervical glide (CLCG). (**A**) A CLCG observed at the SNo. (**B**) A CLCG observed at the infrascapular fossa. (**C**) A simulated infraspinatus muscle contraction observed at the SNo. (**D**) Anatomical variation of the SNe.  
During dissection, an anatomical variation was observed in four of the twelve shoulders (see Figure 3D), where the SNe was divided into its motor branches for the supraspinatus and infraspinatus muscles before entering the SNo. While this variation is clinically relevant, it did not affect the movement of the nerve.
# A Review of Prediction Methods for Ultra-Low Cycle Fatigue Damage of Steel Piers Under Earthquakes
## 3 Conclusions  
1. The traditional CDM model is not highly accurate in predicting ultra-low cycle fatigue damage in steel and its welded specimens. Although this model can estimate the fatigue process and life of welded joints under seismic conditions, it fails to incorporate the effects of equivalent plastic strain on damage increments and lack a reliable method for determining the critical value of damage and fracture, leading to discrepancies between predictions and experimental results.
2. Current prediction methods lack a mixed-element partitioning criterion for steel bridge piers that accounts for the coupling effects of ultra-low cycle fatigue and local buckling. The modeling approach is not universally applicable and poses challenges for practical engineering applications.  
* Prediction limits for ultra-low cycle fatigue damage should not rely solely on crack initiation. The hysteresis model and skeleton curve utilized for evaluating the seismic performance of steel bridge piers do not incorporate the effects of crack propagation and its interaction with local buckling, which deviates from experimental findings.  
Conceptualization, Q.T. and H.C.; methodology, Q.T. and H.C.; validation, S.C. and X.L.; formal analysis, Q.T. and S.C.; investigation, S.C. and X.L.; writing--original draft, Q.T. and S.C.; writing--review and editing, X.L. and H.C.; supervision, Q.T.; funding acquisition, Q.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of China, grant numbers 52168023 and 52368023, and Natural Science Foundation of Jiang Xi, grant number 20224 BAB204059.  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
###### Abstract  
The potential of a nut residue, activated only by physical means, to simultaneously adsorb Sr2+, Mn2+, Pb2+ and As5+ from contaminated waters was investigated. Mono-metal and multi-metal adsorption behavior and adsorption capacities by the solid material were compared for a range of initial metals concentrations, adsorbent dose, contact time and solution pH. The mechanisms of adsorption were examined by conducting structural, mineralogical and chemical analyses and applying two isotherm models to simulate experimental data. In the case of mono-metal adsorption, the maximum uptake of Sr2+ by the biochar was 12.3, of Mn2+ 23.8, of Pb2+ 24.7 and of As5+ 12.4 mg/g. In the case of multi-metal adsorption, the uptake of each metal was enhanced as the total ion flux density increased, revealing that there was no competition between these ions for biochar's sorption sites. For an adsorbent dose of 2 g/L, the maximum adsorption capacity of biochar for Sr2+, Mn2+, Pb2+ and As5+ could be raised up to 47.6, 49.9, 25 and 48.7 mg/g, respectively. Potential adsorption mechanisms were chemical complexation or electron coordination between the metals and the biochar matrix, as well as mineral precipitation on the adsorbent surface.  
multi-metal adsorption; nut residue; biochar; kinetics; isotherms; mechanisms Article
# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 4 Experimental Results  
### Experimental Setup and Data  
The training and validation datasets for the object detection network were obtained from the publicly accessible URPC 2019 underwater object dataset. This dataset encompasses different categories of underwater objects, namely sea cucumbers, sea urchins, scallops, starfish, and aquatic plants, totaling 3765 training samples and 942 validation samples. Subsequently, all samples were amalgamated and randomly allocated into training, validation, and test sets in an 8:1:1 ratio. This stratification was employed for the algorithmic network and the evaluation of its efficacy.