esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Evaluation
### Experiment in University  
#### 4.1.1 Experiment Design  
The experiment was conducted to investigate the usefulness, usability and of the R-WOSARLS system and learners' satisfactory of R-WOSARLS. There are 12 participants that were university students and their majors were unrelated to astronomy, but they were confirmed as having an interest in observing seasonal constellations, and they often go to the planetarium for experiencing astronomical phenomena. The participants are requested to complete a number of constellation observational tasks during different times of the day. Example experiment scenes are shown in Figure 10.  
Once each task has been completed, a question about the system is provided to the participants in order to assess the usefulness of the R-WOSARLS corresponding to the task. After the participants have completed all six tasks and the relevant questions regarding the usefulness of the R-WOSARLS system, participants are given six questions about its usability and their overall satisfaction. These questions are listed below.  
The questionnaire items about the usefulness of R-WOSARLS are listed as below:  
1. The latitude and longitude grid lines in the AR view interface are useful to observe and understand the azimuth and elevation angle of a typical seasonal constellation in the sky in real time.
2. The body scale (hand) search function in the R-WOSARLS is helpful to search for the targeted constellation in the real sky.
3. The function of \"OneDay\" in the R-WOSARLS is helpful to observe the movement of a one-day orbit of the typical seasonal constellation in the sky.
4. The function of \"DisplaySeasonConstellationStarry\" in the system is helpful to observe and study the spring constellations, summer constellations, autumn constellations, and winter constellations, as well as spring triangle, summer triangle, autumn quadrangle, and winter triangle in the south starry sky.  
Figure 10: The participant was observing constellations outdoors using the system (**left**); the participant was completing the tasks and answering questionnaires on the R-WOSARLS (**right**).  
* The function of \"Universe View\" in the system is helpful to understand the relationship of the Sun-Earth-seasonal constellation, and the mechanism of a typical seasonal constellation in different seasons from the universe's viewpoint.  
Questionnaire items about the usability and leaners' satisfaction of the R-WOSARLS are listed as below:  
* The operation of R-WOSARLS is easy for me.
* The interface of R-WOSARLS is clear and easy for me to understand.
* When I am operating the system, the response time of the system is short.
* Using the R-WOSARLS to observe seasonal constellations is interesting.
* The R-WOSARLS can help me enhance my motivation for observing seasonal constellations.
* In the future, I have the intention to use the same kind of system for astronomy learning.
# Low-Complexity Microclimate Classification in Smart Greenhouses: A Fuzzy-Neural Approach
## 1 Introduction  
Modern agricultural practices increasingly rely on controlled environment agriculture techniques, such as hydroponic greenhouses, which offer efficient resource utilization and enhanced plant growth [1]. However, achieving optimal crop yield requires precise regulation of environmental conditions, posing a significant challenge in this field. In this context, climate prediction systems play a crucial role in dynamically adjusting temperature and humidity levels within greenhouses (GHs) to maintain ideal growing conditions [2]. More specifically, the objective is to predict the future microclimate to enable timely interventions, thereby preventing plant diseases or unfavorable growth conditions [3].  
In the literature, the most commonly adopted solutions rely on Neural Networks (NNs) [4, 5, 6]. However, when these networks are implemented at the extreme edge, challenges arise due to their dimensionality and complexity. This is primarily because extreme edge devices have limited computational power and are subject to constraints related to data transmission at high data rates due to battery life limitations [7, 8]. Consequently, this has led to a growing need for solutions that reduce the complexity of NNs for such applications. A more detailed discussion on this topic will be presented in Section 2.  
The communication technologies typically employed in these applications include LoRa [9; 10] and ZigBee [11], given the limited coverage of cellular networks in rural areas where GHs are usually installed [12]. In this regard, explainable AI provides greater credibility to the technology compared to conventional AI models and enhances the interpretability of system-generated recommendations and commands for farmers [13]. Indeed, traditional AI methods, such as Deep Neural Networks (DNNs), often require complex decision-making processes [14], even for experts.
## 4 Conclusions  
In summary, we used a supercell model based on empirical parameters and first-principle calculations to study the effects of different contents of each component on the structural stability and mechanical properties of Co-Cr-Fe-Ni HEAs. The valence electron concentration, enthalpy of mixing, entropy of mixing, atomic size difference, and \(\Omega\) indicate that the Co-Cr-Fe-Ni HEAs studied in this work may be a single FCC solid-solution phase, and all alloys are mechanically stable at the ground state. The structural stability increases with increasing Co/Fe content and with decreasing Cr/Ni content. The equilibrium lattice constants increase with increasing Cr/Ni content and with decreasing Co/Fe content. The mass density increases with Co, Fe, and Ni content and decreases with Cr content. The ductility may enhance with increasing Cr/Ni content and decreasing Co/Fe content. The stiffness improves with increasing Co/Fe contents or decreasing Cr/Ni contents. Certain components of the alloy significantly affect its performance; specifically, Cr affects the structural stability, Fe impacts the equilibrium lattice constant, Cr affects the mass density, Fe affects the ductility, and Ni impacts the stiffness. The results show that the stiffness and structural stability of Co-Cr-Fe-Ni HEAs are improved, while the ductility may be weakened with increasing Co/Fe content or decreasing Cr/Ni content. The structural stability and mechanical properties may be related to the strength of the metallic bonding and covalent bonding inside the Co-Cr-Fe-Ni HEAs, which, in turn, is determined by the change in element content.  
Figure 4: **(a)** Pugh ratio B/G, **(b)** Poisson’s ratio \(\
u\), **(c)** Cauchy pressure \(C_{12}\)–\(C_{44}\), **(d)** Bulk modulus \(B\), **(e)** shear modulus \(G\), and **(f)** Young’s modulus \(E\) of Co-Cr-Fe-Ni HEAs with the \(x\) value of elements.  
**Author Contributions:** Conceptualization, H.L., C.X., L.L., and C.Z.; Investigation, H.L., C.X., L.L., and C.Z.; Methodology, H.L., C.X., L.L., and C.Z.; Software, L.L.; Validation, H.L.; Visualization, H.L.; Writing--original draft, H.L.; Writing--review and editing, H.L., C.X., L.L., and C.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Foundation of the Key Laboratory of Earthquake Forecasting, the Institute of Earthquake Forecasting, CEA (Grant No. 2019 IFE0101-1), and the National Natural Science Foundation of China (Grant Nos. 41174071, 11874001 and 41573121).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare they have no competing financial interests.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 3 Trajectory Optimization  
In this section, we introduce a new set of auxiliary variables to reformulate the original optimization problem. Subsequently, we derive the lower bounds for Equations (9) and (10). However, the reformulated problem remains non-differentiable and, thus, intractable using standard convex optimization methods. To overcome this problem, we apply hypograph theory to further transform the problem into a standard convex form, which can be solved easily.
# An Improvement of the Load Transfer Method for Energy Piles Under Thermo-Mechanical Loads
## 1 Introduction
[PERSON] [16] (2012) continues to use [PERSON]'s method and improves the load transfer model so that the progressive nature of the deformation trend at the interface between pile and soil is taken into account. He makes an in-depth study of the thermal-force-coupled load-bearing characteristics of energy piles, but the calculation results of such a method still do not reflect the pile top constraint. [PERSON] [17] (2019) used a load transfer model in the form of an exponential function to simulate the interaction between the pile and the surrounding soil and improved [PERSON]'s method to make the method perform better in terms of convergence, but still the calculation results do not correctly reflect the constraint at the top of the pile. All the above scholars have improved the load transfer method of energy piles established by [PERSON] and have performed analytical studies on their load-carrying performance. However, it is found that this method cannot accurately reflect the constraint of the pile top, especially when the pile top is free; the pile top axial force obtained by this method is not zero, i.e., additional temperature stress is generated at the free pile top, and such calculation results cannot be explained. Therefore, this paper analyzes the reasons for this phenomenon and further improves the load transfer method for energy piles, which is called the iterative algorithm for the elimination of unbalanced forces at the pile top, so that the calculation results can more accurately reflect the load transfer mechanism of energy piles. Moreover, the improved method proposed in this study is universally applicable to energy piles of any configuration.
## 5 Crack Detection Automation Algorithm Design and System Development for PC Members
### Smart Quality Inspection Process Based on Automated Crack Detection  
The on-site smart quality inspection process (\"To-Be' Process in Figure 11 below) using CNN-based automated crack detection is as follows.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **mAP** & **FPS** & **Batch Size** & **Boxes** &
\begin{tabular}{c} **Input** \\ **Resolution** \\ \end{tabular} \\ \hline Faster R-CNN & 73.2 & 7 & 1 & 6000 & 1000 \(\times\) 600 \\ Fast YOLO & 52.7 & 155 & 1 & 98 & 448 \(\times\) 448 \\ YOLO & 66.4 & 21 & 1 & 98 & 448 \(\times\) 448 \\ SSD 300 & 74.3 & 46 & 1 & 8732 & 300 \(\times\) 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Deep Learning-based Object Detection Model Performance.  
Figure 10: CNN-based Crack Image Analysis and Detection Process.  
First, a mobile device, such as a smartphone or tablet PC capable of capturing video images of PC members brought to the site and recording inspection results is prepared; second, after selecting and setting the target PC member for quality inspection, the mobile device-based camera records surface images of the PC member to be inspected; third, after the captured video image is transmitted to the web server, the crack detection information is automatically produced, enabling the operator to grasp this information in real-time through the mobile device. Moreover, the same crack detection information on PC members is available through the field office computer in real-time. Lastly, the crack detection information is reviewed and recorded on the inspection checklist, which completes the on-site quality inspection process.  
Through this automated crack detection-based quality inspection process, the accuracy and efficiency of the PC member quality inspection process can be enhanced. Likewise, the web server-based database storage and management enables more efficient and convenient management of present and historical inspection information.
## 5 Conclusions  
PAW generated from a SDBD reactor can be used to enhance _Arabidopsis_ growth in the early seedling stage. The PAW treatments were shown to affect the root hair density mediated by the _COBL9_, _XTH9_ and _XTH17_ genes. The maximal observed condition for _Arabidopsis_ root development was identified as PAW5 in which potential target genes were up-regulated while root development was suppressed by the PAW19 treatment condition. The findings of this study will provide new insights at the molecular level into developmental stage plants and accelerate the study of applying plasma technology to crop plants.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)]([https://www.mdpi.com/2076-341](https://www.mdpi.com/2076-341)) 7/11/5/2240/s1, Supplementary Figure S1: PAW regulates number of root hair in (Col-0) Arabidopsis (a) DW, (b) PAW5, (c) PAW7, (d) PAW12, (e) PAW19. Seedlings were grown on solid media for 5 days. Bar = 100 \(\upmu\)m. Figure S2: PAW regulates number of root hair in (WER:GFP/_cobl9-1_) _Arabidopsis_ (a) DW, (b) PAW5, (c) PAW7, (d) PAW12, (e) PAW19. Seedlings were grown on solid media for 5 days. Bar = 100 \(\upmu\)m. Table S1: List of primers used in this study.  
**Author Contributions:** Conceptualization, Y.K.L., I.A.L., S.B.K. and R.A.P.; experiment and data curation, D.H.K., R.A.P., J.Y.P. and S.J.P.; writing--original draft preparation, D.H.K., R.A.P. and Y.K.L.; writing--review and editing, R.A.P. and Y.K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by R&D Program of \"Plasma Advanced Technology for Agriculture and Food (Plasma Farming) (EN2025)-1711124797\" through the Korea Institute of Fusion Energy(KFE) funded by the Government funds, Republic of Korea, and a grant from the 'National Research Foundation of Korea (grant no. NRF-2016R1C1B2015877) funded by the Ministry of Science, ICT & Future Planning' to S.J.P.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Performance Evaluation
### The Impact of Approaching and Leaving Movement  
This subsection analyzes the effect of the start point of an MN on the sensing indicator DIF in MACD [27] and momentum in MRSS when the MN is approaching and leaving its associated AP respectively. In the simulation, an MN moves at 1 m/s in a WLAN environment and at 13 m/s in a WiMAX environment as shown in Figure 4(a). Table 2 shows the configured radio propagation parameters. In order to identify the user motion, the approach threshold is set as 1 dBm and the leaving threshold is set as -1 dBm. When the value of DIF or momentum is larger than the approaching threshold, the user motion is recognized as the approaching state. When the value of DIF or momentum is lower than the leaving threshold, the user motion is recognized as the leaving state. We configure \(\alpha\), \(\beta\) and \(k\) as \(\alpha=0.16\) and \(\beta=0.04\) in MACD and as \(\alpha=0.16\) and \(k=32\) in MRSS.  
Figure 8: The effect of approaching movement in the WLAN environment.  
Figure 9: The effect of approaching movement in the Mobile WiMAX environment.  
Figures 8(a) and 8(b) show the effect of DIF and momentum when an MN approaches an AP in WLAN. Figures 9(a) and 9(b) show the effect of DIF and momentum when an MN approaches an AP in a Mobile WiMAX environment. The results demonstrate that DIF and momentum are related to the starting position when the MN approaches the AP. When the distance between an MN and its associated AP is large, the value of the measured RSS is small, which results in a small change of the measured RSS. On the other hand, when the MN is close to the AP, the change of the measured RSS is large. As shown in Figures 8(a)-9(b), the momentum value can quickly cross the approaching threshold as the distance between the MN and the AP decreases. That is, the MRSS method can easily detect the user motion. Moreover, the approaching motion detection speed of the MRSS method is faster than that of the MACD method.  
Figures 10(a)-11(b) show the measured DIF and momentum when an MN leaves an AP in a WLAN environment and in a Mobile WiMAX environment respectively. These results also display that the rate of DIF and momentum change declines when the starting position of a user is farther away from the AP, which results in a larger delay for both MACD and MRSS to detect the leaving of the MN. Besides, the simulation results also prove that MRSS can detect the user leaving state faster than MACD. In Figures 10(b) and 11(b), the MACD method can not quickly detect the leaving state of the MN, thus the MN can not trigger a handoff before it moves out the cell.  
In the above simulation, the radio propagation strongly affects the behavior of MRSS. As the distance between the MN and the AP increases, the sensitivity of MRSS with a fixed \(\alpha\) and \(k\) declines. This property also confirms that the use of a dynamic \(\alpha\) and \(k\) for identifying the user motion is necessary.  
Figure 10: The effect of leaving movement in the WLAN environment.
# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 5 Simulation Results and Discussion  
A simulation environment was constructed using a real city as a model, and numerical analysis was performed in MATLAB. The parameters are shown in Table 2. We executed downlink communication from one access UAV to multiple UEs, and we considered a mmWave UAV-BS network in an environment where multiple IRSs were deployed, thus aiming at coverage expansion. The communication path between the transmitter and receiver at those higher frequencies in 5G is easily blocked due to the existence of obstacles or blockages [42]. In that case, IRSs are introduced to artificially create communication paths to avoid blocking. A nearby undamaged ground BS was installed at 139.68676\({}^{\circ}\) E, 35.69350\({}^{\circ}\) N on the roof of a 209-meter-high building located in the upper left corner of the environment, which simulates the Shinjuku Metropolitan Government Office area in Tokyo. This BS established a wireless backhaul link with the access UAV, which provided signals to all UEs scattered in the area \(\Omega\), thereby utilizing the appropriate IRS according to Algorithm 1. IRSs were deployed on all four sides of a building that met the specified height criteria \(\kappa\) for a total of \(M\) IRSs. We assumed that the power reflection by the IRS occurred only once and that the IRS had a passive BF and could arbitrarily change the direction of the reflected wave. Glass and wood typically have low reflectivity. Concrete and metal have high reflectivity, but for mmWaves, most of them are rough surfaces, so diffuse reflections occur, and the energy appears to be dispersed in multiple directions. Therefore, we assumed a reflection coefficient of 1 at the IRS, and there was no reflectionassumed outside of the IRS. The UAV knows the area map and the position of the IRS in advance. In contrast, the position of the UE is communicated to the UAV via the uplink control plane and shared from the UAV to the IRS with the UAV position information. Time division multiaccess (TDMA) was employed for multiplexing multiple UEs, and one IRS was used for one UE in a given time slot. The numerical analysis program consisted of three main phases: tentative placement of buildings, the IRS and UEs, an overlap check between buildings and UEs, and an SNR calculation for each UE. Let \(L\) denote the number of buildings in the target area \(\Omega\) and \(P\) denote the number of candidate UAV placement points. A UE is repositioned and checked \(q\) times until the overlap with all buildings is eliminated. \(q\) varies from UE to UE. For phase 3, we tried to connect all UEs to all IRSs and select the ones that matched the criteria according to Algorithm 1, which requires approximately \(N\times M\times P\) steps of computation. Overall, the computational complexity of this numerical analysis is on the order of \((L+M+N)+N\times M^{q}+N\times M\times P\). The computation on our computer--whose specs are a 12 th Gen. CPU, an i7-12700 intel core, 2.1 GHz, and 16 GB RAM--took over 20 h.
## 2 Results and Discussion
### Chemical Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Compounds** & **CAS** & **Quantitative Methods** &
\begin{tabular}{c} **Quantitative** \\ **Ion (w/iz)** \\ \end{tabular} & **Slope** & **Incept** & \(\mathbf{R}^{2}\) & **Concentration Range** & **LOD** & **Recovery** \\ \hline Terpenes & & & & & & & & & \\ Terpenechene & 586-62-9 & HS-SPME-GC-MS & 136 & 1,5706 & 0.0072 & 0.9866 & 33.32-106.68 & 22.72 & 87.57 \\ a-Terpenechen & 98-55.5 & HS-SPME-GC-MS & 90 & 8,0613 & 0.0010 & 0.983 & 12.31-394.00 & 7.10 & 88.48 \\ Linidad & 57-70.76 & HS-SPME-GC-MS & 71 & 21.0550 & 0.0016 & 0.999 & 2.14-292.12 & 1.40 & 107.36 \\
4-Terpenechene & 562-74.3 & HS-SPME-GC-MS & 136 & 4,3520 & -0.0022 & 0.9976 & 0.42-239.00 & 0.35 & 104.30 \\ Chromiled & 106-22.9 & HS-SPME-GC-MS & 81 & 15,4540 & -0.0007 & 0.9979 & 4.05-205.20 & 3.20 & 110.11 \\ Corant Action & 307-20.1 & HS-SPME-GC-MS & 320 & 1,6239 & 0.0001 & 0.9986 & 0.19-394.88 & 0.18 & 95.22 \\ (E)-Neurological & 40716-66.3 & HS-SPME-GC-MS & 64 & 1,7483 & 0.0039 & 0.9980 & 2.00-2046.00 & 1.07 & 109.63 \\ p-DunnScreen & 2369-685-7 & HS-SPME-GC-MS & 121 & 4,0375 & -0.0164 & 0.9909 & 3.77-341.11 & 2.02 & 119.21 \\ Lactone & & & & & & & & \\ Butylstone & 96-48.0 & HS-SPME-GC-MS & 42 & 1.1398 & 0.0211 & 0.9981 & 96.00-3072.00 & 90.00 & 100.89 \\ Sulfate & & & & & & & & \\ MMP* & 1987-52.7 & GC-FFPD & 134 & 0.0117 & -0.0001 & 0.9985 & 2.58-39.21 & 2.05 & 122.32 \\
3-Methyl-High-Propolatable & 3268-93.0 & GC-FFPD & 104 & 1.3862 & -1.7757 & 0.9978 & 0.73-73.16 & 0.68 & 93.10 \\ MMP* & 1539-84.1 & GC-FFPD & 132 & 0.0056 & 0.0027 & 0.9959 & 10.02-308.23 & 8.34 & 85.26 \\
3-Methyl-High-Propanol & 555-10.2 & GC-FFPD & 106 & 4,3258 & 0.0283 & 0.9977 & 107e-3444.00 & 57e.96 & 111.35 \\
3-NHA* & 1369542-6 & GC-FFPD & 116 & 0.1133 & \(-\)0.1214 & 0.9994 & 50.25-1000.21 & 47.12 & 121.46 \\ Param & & & & & & & & \\ Fatural & 98-01-1 & HS-SPME-GC-MS & 39 & 1.3240 & \(-\)0.0589 & 0.9905 & 33.70-17.256.00 & 25.28 & 102.74 \\ Acids & & & & & & & & \\ Acctic Acid & 64-19-7 & LLME-GC-MS & 60 & 14.8070 & 7.9390 & 0.9996 & 109.44-28.016.64 & 98.50 & 120.23 \\ Botanoic Acid & 107-92.6 & LLME-GC-MS & 60 & 6.4966 & -4.1557 & 0.9939 & 9.24-205.64 & 7.39 & 109.78 \\ Octanoic Acid & 124-07-2 & LLME-GC-MS & 60 & 7,2381 & \(-\)14.4220 & 0.9681 & 33.72-436.16 & 30.35 & 99.83 \\ Pyraytes & & & & & & & & \\ IPMP* & 3228-40:2 & SIDA & 137 & 7.5261 & \(-\)0.6684 & 0.9833 & 1.45-2970.00 & 1.41 & 87.60 \\ IBMP* & 5588-56:7 & SIDA & 124 & 0.5800 & 0.0535 & 0.9896 & 10.29-21.072.00 & 100.67 & 92.50 \\ \hline \hline \multicolumn{10}{l}{\(R^{2}\): correlation coefficient. LOD: limit of detection. SIDA: stable isotope dilution analysis. ‘ ‘m’: \(\text{p-L-1}\). SEL, TYP, NT, DA and DST are the abbreviations for five young vineards in the Loes Platen region.} \\ \end{tabular}
\end{table}