esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Input Data and Results
#### 3.1.1 Input and calculate Data  
The data and decision indicators are taken from the Chengdu rail transit network planning report (2016-2050). Seven decision indicators (i.e., \(C_{1}\)-\(C_{7}\)) were selected to form initial decision indicators matrix (Tables 5 and 6). The number of categories was divided into five: extremely urgent, more urgent, urgent, not urgent, and least urgent. The weighted decision matrix based on the Rough Set of the decision table is presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Line**} & \multicolumn{6}{c}{**Indicator**} \\ \cline{2-7}  & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(C_{4}\) & \(C_{5}\) & \(C_{6}\) & \(C_{7}\) \\ \hline Line 1 & 2.75 & 937.93 & 3.69 & 2.4873 & 16.46 & 0.280591291 & 0.085790 \\ Line 2 & 2.17 & 754.35 & 3.80 & 2.5275 & 17.62 & 0.125724324 & 0.090079 \\ Line 3 & 2.84 & 1544.21 & 4.02 & 2.5877 & 6.33 & 0.079717956 & 0.079061 \\ Line 4 & 2.19 & 1015.59 & 3.63 & 2.3241 & 7.59 & \(-\)0.06363601 & 0.067603 \\ Line 5 & 3.65 & 1668.81 & 4.91 & 2.5195 & 1.27 & 0.274976983 & 0.060436 \\ Line 6 & 3.38 & 2262.90 & 4.72 & 2.5783 & 2.53 & \(-\)0.292315108 & 0.072636 \\ Line 7 & 4.97 & 1083.80 & 3.30 & 2.2613 & 30.38 & 1.006768672 & 0.132170 \\ Line 8 & 2.72 & 1204.10 & 3.65 & 2.2780 & 1.27 & 0.168429513 & 0.076736 \\ Line 9 & 2.43 & 1627.48 & 4.19 & 2.2217 & 7.59 & 1.043929557 & 0.065091 \\ Line 10 & 1.83 & 1163.53 & 3.29 & 2.2461 & 15.19 & \(-\)0.042388455 & 0.016284 \\ Line 11 & 1.43 & 326.16 & 2.58 & 2.4593 & 1.27 & 0.210285834 & 0.095867 \\ Line 12 & 1.37 & 731.40 & 2.30 & 2.4556 & 1.27 & \(-\)0.521358532 & 0.071406 \\ Line 13 & 1.49 & 1693.21 & 3.73 & 2.2085 & 8.86 & 0.101369899 & 0.009523 \\ Line 14 & 1.40 & 1331.85 & 3.57 & 2.2417 & 3.80 & \(-\)0.056742955 & 0.000000 \\ Line 15 & 2.08 & 749.41 & 2.89 & 2.3135 & 3.80 & \(-\)0.564448979 & 0.033854 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Initial decision matrix for the construction sequence of Line 1-15.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Inclator**} & \multicolumn{1}{c}{**Specification**} \\ \hline \(C_{1}\) & Passenger flow intensity \\ \(C_{2}\) & Passenger flow turnover \\ \(C_{3}\) & Peak hour maximum sectional flow \\ \(C_{4}\) & Traffic location coefficient \\ \(C_{5}\) & Coverage of passenger flow distribution center \\ \(C_{6}\) & Network accessibility contribution degree \\ \(C_{7}\) & Urban location coefficient \\ \hline \hline \end{tabular}
\end{table}
Table 5: Decision indicators for Chengdu (extracted from the Chengdu rail transit network planning report (2016–2050)).  
The start time is set as 28 December 2005; the end time is set as 2050. The total scale of the network will be 2450.4 km, and the total construction period will be 44 years, so \(S_{0}\) is set to 0, \(\beta\) is set to 99.6%. According to the construction period of the lines in operation, \(T_{ol}\) is set to 5.8.
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 1 Introduction  
As computing and communication technologies have developed rapidly, UAVs (Unmanned Aerial Vehicles) have received much attention not only in research fields, but also in real-life applications including forest fire monitoring [1], highway traffic monitoring [2], etc. Since UAVs are operated without human beings, they are useful especially in hazardous or inaccessible environments. Much recent research has focused on operating multiple UAVs in order to accomplish a given task efficiently, such as reconnaissance, target tracking, surveillance, etc. [3; 4; 5; 6; 7; 8].  
One of important and common missions of multi-UAVs is the cooperative reconnaissance to search a target in a given open area. Thus, many studies have been conducted on efficient cooperative reconnaissance with multiple UAVs [3; 4; 9; 10; 11; 12]. The reconoitering methods with autonomous flights are classified into the path planning method and the random mobility method [3]. In the path planning method, each UAV routes its own pre-planned path so that UAVs reconnoiter their own determined regions. On the contrary, the random mobility method has no pre-planned paths for UAVs' movements. Each UAV decides its reconnaissance path dynamically based on random mobility models [3; 4; 9; 10; 11; 13; 14; 15; 16]. This random mobility method can overcome unexpected events, such as UAV failure, and also provides unpredictability of reconnaissance to targets. In this paper, we focus on the random mobility method to enhance the reconnaissance performance.  
Although random mobility-based cooperative reconnaissance generally shows good coverage rate, it has one limitation in terms of reconnaissance balance. For example, Random Waypoint [17] shows that most of UAVs' movements are focused on the center area. Pheromone Repel model [9] also shows high reconnaissance rate in a specific area because UAVs fly similarly based on the shared information. Thus, we propose a new random mobility model to overcome the imbalance problem. The proposedmodel divides the scanning area into \(N\) zones and guides UAVs to fly from one border to the other border with a zigzag pattern based on visiting counts.  
The remainder of the paper is organized as follows. In Section 2, we deal with related work on mobility models and the research motivation. The proposed model is explained in Section 3. Section 4 presents the simulation environment and the result of the experiment. Finally, Section 5 provides the conclusions and future work.
## 3 Field Measurements
### Overview
#### 3.1.1 Measurement of Track Settlement  
For measurements of track irregularities, Track master (LR-S100), one of the track-geometry-measurement systems, was used. Track-irregularity measurements using the Track master allowed for the direct measurement of the deformation in the rail in direct contact with the wheel. This method presents the most accurate assessment of track displacement with respect to the adjacent excavation. As shown in Figure 6, the track irregularities were measured using the Track master over a total length of 100 m, and the measurements were conducted on the left and right rails of the HSR T1 and HSR T2 sections.  
In the case of excavations adjacent to serviced railway lines, it is a common practice to install railbed-settlement-monitoring sensors (RM sensors) to ensure the structural safety of the railway infrastructure and the security of the stable operation of the rolling stock with respect to adjacent works based on track deformation measurements [5; 16].  
As shown in Figure 7a, the RM sensor installed on the ballasted track supported the track panel at locations in which the rail and the railroad tie were bound with the reinforcing beams formed by the rail on the top of the railroad tie at the center of the track gauge, thus preventing the track panel from deflecting and measuring the level of settlement of the track panel. As shown in Figure 7b, the main components of the RM sensor include the rail beam--composed of multiple lightweight rails (37 kg/m)--and a settlement pin with a built-in RM sensor that can measure the settlement of the line. The settlement pin was inserted and fixed in the upper subgrade layer under the ballasts to  
Figure 5: Locations of field measurements and target section used for numerical analysis.  
Figure 6: Track master.  
measure the relative displacement between the railroad tie and the subgrade. Therefore, it was easy to measure the settlement of the track caused by the deformation of the roadbed owing to adjacent excavations.
## 4 Discussion
### Feature Importance
The natural moisture content influences the pore water pressure, effective stress, and the ease with which particles can rearrange under loading conditions. Soils with higher moisture content tend to have weaker interparticle forces and facilitate greater compression. The feature importance analysis suggests that the compression index (\(C_{c}\)) is governed by a combination of the following features:  
* Mineralogical properties (the LL, the PI, and specific gravity) affect plasticity and particle interaction. * Structural characteristics (the initial void ratio) influence particle arrangement and porosity. * Hydrological conditions (the natural moisture content) impact pore water dynamics and effective stress. These findings align with the classical consolidation theory, where \(C_{c}\) is a function of both soil composition and initial structural conditions. Machine learning models not only confirm these geotechnical principles but also provide quantitative evidence of the relative importance of each factor.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 4 Evaluation and Analysis
### Performance Metrics  
We consider three metrics in our evaluation. The first metric was adapted for the purposes of our simulation scenario, while the remaining two metrics are commonly used in literature.  
* _Packets Delivered (per day):_ This is the main performance metric. The number of packets delivered to the base station is used rather than the more standard delivery rate metric (i.e., percentage of packets that are delivered to the destination). The reason is that the number of generated packets can vary for different nodes and routing protocols, as nodes do not generate packets when they run out of energy or their energy is below a fixed threshold. We normalize the metric per simulation day, so it is independent of the duration of the simulation.
* _Overhead Ratio:_ This is defined as the ratio of total number of packets transmitted in the network and the number of packets delivered to the base station successfully. \[\text{Overhead Ratio}=\ \frac{(\text{R}-\text{D})}{\text{D}}\] (16) where R is the number of messages relayed during the simulation and D is number of messages delivered to the base station.
* _Average Latency:_ This is calculated as the average time required for a packet to be delivered to a base station. \[\text{Average Latency}=\sum_{\text{k=1}}^{\text{Nr}}\frac{(\text{Tr}-\ \text{Tc})}{\text{Nr}}\] (17)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Simulation Parameter** & **Value** \\ \hline
9 & Receive energy & 0.002 J \\
10 & Sensing energy & 0.75 J \\
11 & Message generation interval & 4–6 min \\
12 & Redrange energy & 30 J \\
13 & Number of base station & 2 \\
14 & Number of nodes & 20 \\
15 & Transmit range & 100 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
where Tr is the time when the packet is received, Tc is the time when the packet is created and Nr is the number of received packets. Other possible metrics of interest which can be derived from the above metrics include:
* _Number of initiated messages_: This is just the same as the number of messages transmitted, since messages are only sent when a link is established, and in this simulation they are always successfully received.
* _Average Hop Count_: For successfully received messages this is the same as (1 + overhead ratio).
* packets delivered), since undleivered packets are dropped once the time-to-live expires. Buffers are sufficient large that all packets are retained until their TTL is reached.
## 5 Test and Result Analysis
Due to different initial angles of inclination, automatic levelling can level the platform within 7 s, and manual levelling can level the platform within 11 s. This is fast enough for the targeted application. The automatic levelling system takes less time than manual levelling and has a faster response time. During the levelling process, the inclination of the mobile platform in the X- and Y-directions decreases. After levelling, the tilt difference of the mobile platform in the X- and Y-directions is very small and within the levelling error range. At the same time, the smaller the initial inclination of the mobile platform is, the shorter the total levelling time. Figure 13 shows that during the dynamic levelling process, due to the subsequent lifting effect of the electric push rod, the tilt angle of the mobile platform during driving is improved, and the angle after levelling is significantly smaller than that before levelling. However, due to the short and continuous tilt time of the mobile platform during driving, the tilt of the mobile platform cannot be fully adjusted within the accuracy range. Therefore, the designed levelling mechanism of the safflower-picking mobile platform has a good levelling effect and can meet the requirements of safflower picking. Figure 11: Comparison of automatic and manual levelling time. Figure 12: Performance indicators of test results.
# Local Stiffness Assessment of Geogrid-Stabilized Unbound Aggregates in a Large-Scale Testbed
###### Abstract  
This paper integrates and extends an earlier article presented at the 20 th International Conference on Soil Mechanics and Geotechnical Engineering. The generation of a stiffened zone in the proximity of a geogrid is one of the primary mechanisms of mechanical stabilization of pavement unbound aggregate layers using geogrids. This paper focuses on the quantification of the stiffened zone through a local stiffness assessment using bender element (BE) sensors. Unbound aggregate base layers were constructed in a large-scale laboratory testbed. Geogrid-stabilized layers had geogrids with different-sized triangular apertures contributing to the geogrid-stiffened zone. Shear wave velocities were measured at three different heights using BE sensors, and the vertical stiffness profiles of the mechanically stabilized aggregate layers were evaluated. In addition, the conversion method between small-strain stiffness and large-strain stiffness was established from the repeated load triaxial tests with BE pairs to transform the vertical stiffness profile into that of the resilient modulus. Furthermore, dynamic cone penetration (DCP) and light-weight deflectometer (LWD) tests were performed at multiple locations into the stabilized and unstabilized unbound aggregates. From the large-scale experimental study, the local stiffness improvement owing to the geogrid enhancement was up to 16.2% in the vicinity of the geogrid location, and the extent of the local stiffened zone evaluated through various test methods was between 15.2 cm (6 in.) and 25.4 cm (10 in.) above the geogrid.  
mechanics stabilization; unbound aggregates; geogrid; bender elements; shear wave velocity +
Footnote †: journal: Applied Sciences
## 7 A case Study in Antarctica  
### Existing Power Supply System in Zhongshan Station  
At present, there are three sets of diesel generator in Zhongshan Station. In the process of power generation, the on-duty personnel should be arranged to record and maintain the relevant parameters of the generators. Based on the data provided by the Chinese National Antarctica Research Expeditions, we aggregated the data of the existing diesel power supply system at Zhongshan Station in Antarctica in 2013, 2014 and 2015. The average monthly power supply and monthly fuel consumption of Zhongshan Station are obtained. The actual monthly load power and fuel consumption are shown in Figure 15.  
As can be seen from Figure 15, the annual power consumption of Zhongshan Station is closely related to climatic conditions. The trend of monthly load power has a good correlation with the monthly average air temperature. The peak annual power consumption of Zhongshan Station is concentrated in May, June, July and August. At this time, it is the winter and polar night in Antarctica, which the air temperature in these months is the lowest in one year. However, scientists would continue to conduct scientific investigations near Zhongshan Station. At the same time, the outside temperature is low and the demand of indoor heating is increasing. Therefore, the power consumption of Zhongshan Station will also increase. As shown in Figure 2b, the annual radiation intensity and day lengths are the lowest in this period. The power supply of Zhongshan Station mainly depended on the wind turbine and the battery. Zhongshan Station has a total load of 100 KW. The monthly average power consumption is 72,516 KW h, the monthly average fuel consumption is above 20 t. Polar day occurs from December to January in Zhongshan Station, and it can make full use of solar power to generate electricity. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline \(C_{13}\) & 470 & \(\mu\)F \\ \(C_{14}\) & 330 & \(\mu\)F \\ \(C_{15}\) & 100 & pF \\ \(C_{16}\) & 100 & pF \\ \(C_{17}\) & 0.1 & \(\mu\)F \\ \(C_{18}\) & 0.1 & \(\mu\)F \\ \(C_{19}\) & 0.1 & \(\mu\)F \\ \(R_{4}\) & 30 & K\(\Omega\) \\ \(R_{5}\) & 15 & K\(\Omega\) \\ \(R_{6}\) & 15 & K\(\Omega\) \\ \(R_{7}\) & 30 & K\(\Omega\) \\ \(R_{8}\) & 30 & K\(\Omega\) \\ \(L_{2}\) & 100 & mH \\ \(L_{3}\) & 100 & mH \\ \hline \hline \end{tabular}
\end{table}
Table 11: Relevant parameters of the solar/wind charging circuit.  
Figure 15: Actual monthly load power, air temperature and fuel consumption in Zhongshan Station.  
power consumption of the load in February, March, April, September, October, and November basically maintain the average power consumption of Zhongshan Station, which both wind and solar energy resources can generate electricity, and batteries have electricity reserves.
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 2 Biodiversity of Filamentous Fungi in Antarctica
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{1}{c}{\(\mathbf{N}\)} & \multicolumn{1}{c}{**Strain**} & \multicolumn{1}{c}{**Antarctic Samples**} & \multicolumn{1}{c}{**Origin**} & \multicolumn{1}{c}{**References**} \\ \hline
1. & _Acemonium berkeleyanum_ & Soil & Russian Research Station & [39] \\ \hline
2. & _Alternaria alternata_ & Lichens & Livingston Island & [40] \\ \hline
3. & _A. arborescens_ & Seals nasal swab &
\begin{tabular}{l} Primavera Cape/ \\ Cierva Cove \\ \end{tabular} & [41] \\ \hline
4. & _A. alternata_ & Soil & McMurdo Dry Valleys & [42] \\ \hline
5. & _A. maritima_ & Soil & Livingston Island & [43] \\ \hline
6. & _Angustimassarina populi_ & Soil & Livingston Island & [43] \\ \hline
7. & _Antarctomyces pelizarie_ & Soil &
\begin{tabular}{l} Penguin Island and \\ Robert Island \\ \end{tabular} & [44] \\ \hline
8. & _Antarctomyces_ & Water &
\begin{tabular}{l} Lakes in Admiralty Bay \\ (King George Island) \\ \end{tabular} & [45] \\ \hline
9. & _Arthrobotrys frox_ & Soil & Victoria Land & [19] \\ \hline
10. & _A. frox_ & Moss & Edmonson Point & [46] \\ \hline
11. & _Apergillus flavus_ & Soil & Russian Research Station & [39] \\ \hline
12. & _A. fumigatus_ 1-9 & Soil & Livingston Island & [40] \\ \hline
13. & _A. glaucus_ & Lichens & Livingston Island & [47] \\ \hline
14. & _A. sydowii_ & Soil & Livingston Island & [40] \\ \hline
15. & _A. tentnescensis_ & Seals nasal swab &
\begin{tabular}{l} Primavera Cape/ \\ Cierva Cove \\ \end{tabular} & [41] \\ \hline
16. & _A. versicolor_ (Vuill.) & Soil under moss & Lamplugh Island & [46] \\ \hline
17. & _Aureobasidium pullulans_ & Antarctic permafrost & McMurdo Dry Valleys & [42] \\ \hline
18. & _Cadophora malorum_ & Water &
\begin{tabular}{l} Lakes in Admiralty Bay \\ (King George Island) \\ \end{tabular} & [45] \\ \hline
19. & _Caldosporium oxysporum_ & Lichens & Livingston Island & [43] \\ \hline
20. & _C. cladosporoides_ & Antarctic permafrost & McMurdo Dry Valleys & [42] \\ \hline
21. & _C. herbarum_ & Antarctic permafrost & McMurdo Dry Valleys & [42] \\ \hline
22. & _Coelomycetes Incertaesediis_ & Permafrost sediments & Banger Oasi & [2] \\ \hline
23. & _Cordyceps farinose_ & Seals nasal swab &
\begin{tabular}{l} Primavera Cape/ \\ Cierva Cove \\ \end{tabular} & [41] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fungal strains isolated from Antarctica.
## 4 Performance Analysis of Islanding Detection Methods
### Implementation Cost  
The implementation cost is considered to compromise between the system cost and quality. The cost of passive methods is the cheapest compared to other methods. While the implementation cost of remote methods is the most expensive due to the complexity and the need for more components.
## 2 System and Channel Models  
This section will focus on a downlink FSO communication link, as depicted in Figure 1. The system includes a drone as the source and a receiver as the destination. The drone hovers in a fixed position and has a laser diode. The receiver has a photodiode (PD) that converts the optical signals into electrical signals. We have the assumption that the transmitter and the receiver are aimed at each other.  
Mathematically, the received electrical signal can be written as  
\[y=\eta Ix+n, \tag{1}\]  
where \(x\) is the optical signal emitted by drone, \(\eta\) is the optoelectronic conversion efficiency of the PD, \(I\) is the channel coefficient, and \(n\) is an additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma_{n}^{2}\), i.e., \(n\sim N\left(0,\sigma_{n}^{2}\right)\).  
In this system, optical beams inevitably undergo attenuation and flicker as they propagate through the atmosphere. The building vibration, building sway, and thermal expansion also prevent the beam from reaching the receiver aperture accurately. Moreover, orientation deviations of the drone can affect the incidence angle of light and cause communication interruptions in severe cases. Thus, we consider the influence of atmospheric attenuation, atmospheric turbulence, pointing errors, as well as AoA fluctuations on the channel model, i.e.,  
\[I=I_{aI}I_{aI}I_{pc}I_{AoA}, \tag{2}\]  
Figure 1: A drone-assisted downlink FSO communication system.  
where \(I_{al}\), \(I_{at}\), \(I_{pe}\), and \(I_{AoA}\) indicate atmospheric attenuation, atmospheric turbulence, geometric loss due to the pointing error impairments, and communication interruption because of AoA fluctuations, respectively.