esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 4 Results
### Prototype Testing in a Closed Environment  
This test uses the power supply and regulator as part of the power system. The assembled prototype is shown in Figure 23. Subsequently, the UAV with all the integrated components is weighed, as illustrated in Figure 24, obtaining a total weight of 1940.8 g. The detailed breakdown of the weight of each prototype component is presented in Table 12, allowing an analysis of the weight distribution.  
As an initial test to analyze the power management behavior of the source and controller, the system was connected to a power outlet in one of the university buildings. During the tests, the UAV oscillated between heights of 0.3 m and 0.6 m, with an approximate duration of each flight of 2 to 3 min. Voltage and current logs were downloaded and analyzed using the PlotJuggler application to assess energy consumption more accurately. Figure 25 shows the plots of voltage and current as a function of time during test flights performed in a closed environment. In the interval between 30 and 65 s of one of the flights, at a height of 0.3 m, it is observed that the voltage remains constant at 15.64 V while the current increases progressively until it reaches its maximum value of 9.12 A. However, near the second 65, voltage peaks are recorded while the current decreases. This behavior is due to the lateral movements of the UAV, which cause a change in the energy demand according to the adjustments in the speed of the motors.  
\begin{table}
\begin{tabular}{c c} \hline
**Components** & **Prices [USD]** \\ \hline
**UAV, Controller, GPS, and Telemetry Radio** & 540 \\ \hline
**Raspberry Pi Battery** & 45 \\ \hline
**Raspberry Pi** & 134 \\ \hline
**Power Supply** & 32 \\ \hline
**Voltage Regulator** & 15 \\ \hline
**\#12 Audio Cable** & 11 \\ \hline
**Total Cost** & 777 \\ \hline \end{tabular}
\end{table}
Table 11: Prices of the components of the designed prototype.  
Figure 23: Final prototype, which integrates the communication network and the power supply.  
When analyzing the time intervals of each flight, it was observed that although the voltage trend was stable during each phase of operation, its value varied between different flights. For example, Figure 26 shows a voltage of 14.97 V and a current of 11.57 A corresponding to a flight at 0.6 m altitude. In contrast, Figure 27 shows that the power consumption increased to 16.56 V and 10.01 A, corresponding to a flight at 0.4 m height.  
Figure 24: Total weight of the prototype.  
\begin{table}
\begin{tabular}{c c} \hline
**Components** & **Weight [g]** \\ \hline
**Voltage Regulator** & 91.1 \\ \hline
**Raspberry Pi Battery** & 212.6 \\ \hline
**Raspberry Pi** & 112.7 \\ \hline
**Cable** & 554.4 \\ \hline
**UAV** & 970 \\ \hline
**Total** & 1940.8 \\ \hline \end{tabular}
\end{table}
Table 12: Detailed weights of the prototype components.  
Figure 25: Variation of voltage and current consumption in a closed environment during the interval from 30 to 65 s.
## 2 Numerical Model
### Railway Culvert Model  
A railway culvert is composed of the culvert body, wing walls, and end faces. The culvert body is typically rectangular, the wing walls are isosceles trapezoid (top view), and the end faces are vertical skirt. The culvert structure and size in desert areas conform to the _TB 10002-2017 Code for Design of Railway Bridges and Culverts_[25]. A three-dimensional model of the railway culvert was established. The dimensions were 13.6 m (length) \(\times\) 3 m (width) \(\times\) 3 m (height), the subgrade was 22.7 m (length) \(\times\) 36 m (width) \(\times\) 6 m (height), and the slope was 1:1.75. The simulation domain was 142.7 m long, 36 m wide and 60 m high. The culvert was located 40 m from the start of the simulation domain to ensure a sufficient distance for wind-blown sand accumulation. The simulation domain, subgrade, and culvert model are shown in Figure 2.  
The unstructured grids with tetra/mixed mesh type were used because of the complex structure and irregular shape of the inlet and outlet of the culvert model. The grids of the culvert inlet and outlet and the track were locally encrypted, and the number of grids was about 3.3 million. The Fluent software was chosen to simulate the wind-blown sand flow around the culvert. The turbulence intensity at the culvert inlet was 5%. The boundary conditions are shown in Figure 2a. A program was used to write to describe the logarithmic wind speed profile at the domain inlet:  
\[u_{y}=\frac{u_{*}}{\kappa}\ln\frac{y}{y_{0}}, \tag{9}\]  
where, \(u_{y}\) represents the horizontal wind speed at height \(y\), \(u^{*}\) represents the friction wind speed, \(\kappa\) is the Karman constant, 0.41, and \(y_{0}\) = 5.44 \(\times\) 10\({}^{-6}\) m is the surface roughness [10].  
In addition, the Scalable Wall Functions are used near the wall of the flow field, the control equations and the boundary and initial conditions were discretized by the finite volume method, the SIMPLE pressure-velocity coupling algorithm is used to solve the  
Figure 2: The geometric model of railway culvert(Unit: m). (a)The schematic diagram of railway culvert model and its boundary condition setting, the specific structural dimensions are given in (b) the front view and (c) the right view of culvert.  
linear equations to obtain the solution of the flow field [26]. The flux calculation adopts the Second Order Upwind format, which can retain second-order accuracy.
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **Extracted Logo** & **NCC** \\ \hline \hline \multirow{2}{*}{Leena} & \multirow{2}{*}{93.3\%}  
Table 2 examines the robustness of our watermarking method through various images. The high NCC values indicate that the watermarking technique is effective at embedding and extracting the watermark even after possible distortions or manipulations to the image. The consistency in NCC values across different images means that the method is functionally robust for image content or complexity. To assess the performance of the proposed PSO-based watermarking method, a comparative analysis was conducted against other classical optimization approaches commonly used in watermark optimization. The comparison highlights the strengths and trade-offs of the proposed method. The evaluation focused on three primary metrics, PSNR, NCC, and computational cost, which collectively reflect the efficiency and effectiveness of the optimization process. The findings are summarized in Table 3. \begin{table}
\begin{tabular}{c c c} \hline
**Image** & **Extracted Logo** & **NCC** \\ \hline Homecar & & 91.1\% \\ House & & 96.1\% \\ Airplane & & 90.7\% \\ \hline Lighthouse & & 91.3\% \\ Bedroom & & 93.5\% \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._The results in Table 3 highlight the superior performance of the proposed PSO-DTCWT method compared to the GA-based, differential evolution (DE)-based, and ant colony optimization with light gradient boosting algorithm (ACO + LGBA) methods. The proposed method achieves the highest imperceptibility, with a PSNR of 80.5 dB. The PSNR significantly surpasses that of the other methods, whose values range from 46.4 to 69.25 dB. Although the PSO-DTCWT method's robustness (NCC of 92.51%) is slightly lower than that of the ACO + LGBA method (99%) and the GA-based method (97.84%), it remains competitive and sufficient for reliable watermark extraction. Furthermore, the PSO-DTCWT method demonstrates a clear advantage in computational efficiency due to parameter optimization, unlike the GA- and DE-based methods, which incur higher costs from additional processes such as DCNN-SVR correction and the embedding of extra data. These findings indicate that the PSO-DTCWT method achieves the best balance between imperceptibility, robustness, and computational cost, making it a practical and efficient solution for real-world applications.
###### Abstract  
The uniformity of remolded loess samples prepared in the laboratory directly determines the reliability of various test results. While many tests have been designed to reflect the properties of undisturbed loess, there are obvious structural differences between remolded and undisturbed loess. Therefore, it is of great theoretical and practical significance to explore a non-destructive evaluation index of the uniformity and structure of loess samples for indoor geotechnical tests. In this study, the loess from the Malan loess section of the Chanhe River in Xi'an, China, was used as the experimental material to analyze the uniformity of remolded loess samples in terms of the stress distribution, stratification range, and sample variance. Based on the test results, the feasibility of using the air permeability coefficient as an index by which to evaluate the uniformity and structure of loess samples is investigated for the first time. The results demonstrate that when the overall height of the sample is fixed, the higher the designed dry density of the sample and the lower the air permeability of the sample. Moreover, when the designed dry density of the sample is constant, with the increase in the overall height of the sample, the range of the stratified dry density of the sample gradually decreases, as does the sample variance. The more uniform the sample, the greater the air permeability, and under the same conditions, the air permeability of remolded soil is lower than that of undisturbed loess. SEM images reveal that there is a considerable difference between the structures of undisturbed and remolded loess samples. Remolded loess is characterized by the greatly reduced effectiveness of cementation between skeleton particles, which causes its structure to be weaker than that of undisturbed loess. In this paper, a quantitative index of loess structure based on air permeability is proposed, and the test results show that the index can adequately reflect the loess structure. Due to its obvious advantages of non-destructiveness, high efficiency, economy, convenience, and high speed, it has a unique advantage for the evaluation of the uniformity and structure of remolded soil samples.  
Malan loess; air permeability coefficient; laboratory test; uniformity; soil structure +
Footnote †: journal: Applied Sciences
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 3 Experimental Results and Analysis
### Analysis of Experimental Results
#### 3.3.2 Quantitative Analysis
\begin{table}
\begin{tabular}{c c c} \hline
**Tracking Algorithm** & **Norm Precision** & **Success** \\ \hline Transtfpn-T & 0.738 & 0.649 \\ TransT & 0.624 & 0.541 \\ TrSiam [16] & 0.718 & 0.629 \\ HCAT [30] & 0.687 & 0.593 \\ ViTCRT [28] & 0.678 & 0.646 \\ E.T.Track [32] & - & 0.591 \\ SiamTPN [13] & 0.683 & 0.581 \\ TaTrack [17] & 0.661 & 0.578 \\ DiMP [31] & 0.642 & 0.560 \\ SiamRPN++ [5] & 0.570 & 0.495 \\ SiamMask [6] & 0.552 & 0.467 \\ SiamAttn [29] & 0.648 & 0.560 \\ SiamFC [1] & 0.420 & 0.336 \\ TSN [33] & 0.532 & 0.325 \\ HSET [34] & 0.354 & 0.372 \\ ABTrack [35] & 0.733 & 0.634 \\ MDETrack [36] & 0.661 & 0.591 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of tracking performance between the proposed algorithms and other methods on the LaSOT dataset.
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 3 Test Results and Discussion
### Discussion on Test Results
#### 3.2.2 Load-Displacement Curves in the Failure Stage  
The typical load-displacement curves of column 1# and 5# in the failure and the whole loading process are drawn as shown in Figure 12.  
The load-carrying capability of each specimen is displayed in Table 4. Columns 1# and 6# are cast-in-situ specimens and their strengths are basically consistent. The load-carrying capability of the prefabricated columns 2#, 3#, and 7# varies due to insufficient grouting in their sleeve connection, which can be attributed to the strength of the cement mortar that is utilized to seal the edge being inadequate. Furthermore, the grout with a higher strength than concrete leaks out and diffuses at the column foot, leading to these specimens possessing different load-carrying capabilities. Column 2# generates in-plane inclination and the loading direction at its failure stage is opposite to that of the inclination, resulting in a higher load-carrying capability. On the contrary, column 7# also generates in-plane inclination but its inclination has the same direction with that of the loading at the failure phase, leading to low load-carrying capability. For column 3#, it inclines out of plane but has the largest load-carrying capability among these specimens. The possible relevant causes for that may be: (1) the severe out-of-plane inclination may give rise to the smaller axial force exerted on the column;  
Figure 12: Load-displacement curves: (**a**) column 1# in the failure stage; (**b**) column 1# in the whole loading process; (**c**) column 5# in the failure stage; (**d**) column 5# in the whole loading process.  
(2) a great deal of grout leaks out, and finally the high-strength mortar replaces concrete to bear the compression. As for column 5#, it is grouted sufficiently because of no grout running out, and finally, its yield capacity is 80% that of the cast-in-situ columns and its ultimate load-carrying capability is 70% that of the cast-in-situ columns.
## 4 Test Feeders
### PV Curve and Objective Function Parametrization  
The evaluation of the proposed optimization approach uses as the PV generation curve a typical 24 h curve reported in [22] for the city of Medellin in Colombia. The generation and demand curves are depicted in Figure 4.  
The calculation of the objective function value was made by using the parameters reported in Table 3. Some of the parameters reported in this table were adapted from references [8,28], respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(k\) & \(m\) & \(R_{\text{km}}\) (\(\Omega\)) & \(x_{\text{km}}\) (\(\Omega\)) & \(P_{\text{k}}\) (\(\mathbf{k}\)W) & \(Q_{\text{k}}\) (\(\mathbf{k}\)W) & \(k\) & \(m\) & \(R_{\text{km}}\) (\(\Omega\)) & \(x_{\text{km}}\) (\(\Omega\)) & \(P_{\text{k}}\) (\(\mathbf{k}\)W) & \(Q_{\text{k}}\) (\(\mathbf{k}\)W) \\ \hline
28 & 29 & 0.546 & 0.226 & 0 & 0 & 70 & 71 & 0.546 & 0.226 & 35.28 & 35.99 \\
29 & 30 & 0.546 & 0.226 & 35.28 & 35.99 & 67 & 72 & 0.182 & 0.075 & 56 & 57.13 \\
30 & 31 & 0.273 & 0.113 & 35.28 & 35.99 & 68 & 73 & 1.184 & 0.491 & 0 & 0 \\
31 & 32 & 0.182 & 0.075 & 0 & 0 & 73 & 74 & 0.273 & 0.113 & 56 & 57.13 \\
32 & 33 & 0.182 & 0.075 & 14 & 14.28 & 73 & 75 & 1.002 & 0.416 & 35.28 & 35.99 \\
33 & 34 & 0.819 & 0.340 & 0 & 0 & 70 & 76 & 0.546 & 0.226 & 56 & 57.13 \\
34 & 35 & 0.637 & 0.264 & 0 & 0 & 65 & 77 & 0.091 & 0.037 & 14 & 14.28 \\
35 & 36 & 0.182 & 0.075 & 35.28 & 35.99 & 10 & 78 & 0.637 & 0.264 & 56 & 57.13 \\
26 & 37 & 0.364 & 0.151 & 56 & 57.13 & 67 & 79 & 0.546 & 0.226 & 35.28 & 35.99 \\
27 & 38 & 1.002 & 0.416 & 56 & 57.13 & 12 & 80 & 0.728 & 0.302 & 56 & 57.13 \\
29 & 39 & 0.546 & 0.226 & 56 & 57.13 & 80 & 81 & 0.364 & 0.151 & 0 & 0 \\
32 & 40 & 0.455 & 0.189 & 35.28 & 35.99 & 81 & 82 & 0.091 & 0.037 & 56 & 57.13 \\
40 & 41 & 1.002 & 0.416 & 0 & 0 & 81 & 83 & 1.092 & 0.453 & 35.28 & 35.99 \\
41 & 42 & 0.273 & 0.113 & 35.28 & 35.99 & 83 & 84 & 1.002 & 0.416 & 14 & 14.28 \\
41 & 43 & 0.455 & 0.189 & 35.28 & 35.99 & 13 & 85 & 0.819 & 0.340 & 35.28 & 35.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Cont.\)  
Figure 4: Solar generation and demand curves typical for the city of Medellin, Colombia.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Parameter** & **Value** & **Unit** \\ \hline \(C_{\text{kWh}}\) & 0.1390 & USD/kWh & \(T\) & 365 & days \\ \(t_{a}\) & 10 & \% & \(t_{e}\) & 2 & \% \\ \(N_{t}\) & 20 & years & \(\Delta t\) & 1 & h \\ Cry & 1036.49 & USD/kWp & \(C_{\text{O\&}M}\) & 0.0019 & USD/kWh \\ \(p_{t}^{\text{per,max}}\) & 2400 & kW & \(p_{t}^{\text{per,min}}\) & 0 & kW \\ \(N_{pv}^{\text{max}}\) & 3 & — & \(\Delta V\) & \(\pm 10\) & \% \\ \(\alpha_{1}\) & \(100\times 10^{3}\) & USD/V & \(\alpha_{2}\) & \(100\times 10^{3}\) & USD/V \\ \(\alpha_{3}\) & \(100\times 10^{3}\) & USD/W & — & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parametrization of the objective and fitness functions.  
In the case of the NMA, we considered a population with 10 individuals, 1000 iterations and 100 repetitions.
## 3 Results and Discussion
### Creep Models Accuracy and Validation  
In terms of accuracy of data plotted in creep strain-time analysis, the adjusted regression (Adj. \(R^{2}\)) or coefficient of determination was used to fit these numerical models with experimental outputs. These Adj. \(R^{2}\) values are tabulated in Tables 2 and 3 for the [PERSON] and Burger models, respectively. From these tables, it was found that the adj. \(R^{2}\) for [PERSON]'s power law model exhibited higher values, ranging from 0.833 to 0.989 rather than the Burger model, ranging from 0.518 to 0.913. The Burger model forecasted a relationship of viscosity and time linearly, which was affected during the numerical model fitting with experimental data [51]. Thus, from the adjusted regression evaluation, the best numerical model suitable to analyse the finding of creep for wooden cross-arm was [PERSON]'s power law model. This observation showed that the wooden cross-arm experienced a steady-state creep in a long-term period and did not permit any sign of tertiary creep phase to suddenly fail [52,53] during long-term period and not permit any sign of tertiary creep phase to be suddenly failed. However, the project also required the use of the Burger model in order to examine the effect of addition of bracing arms on elastic and viscoelastic properties of wooden cross-arm. Thus, both studies were required to achieve a holistic view and analysis on creep behaviours for the existing and braced wooden cross-arms.  
These creep models ([PERSON]'s power law model and [PERSON] model) were validated by comparing their instantaneous strain with experimental results. Fundamentally, the instantaneous strains were principally proportional to the applied stress according to [PERSON]'s law. Table 5 summarises the evaluation of instantaneous strain between the experimental outcomes with two numerical models ([PERSON] and Burger models) for both existing and braced cross-arms at y3 location. Since the y3 location exhibited the most highly severe in terms of creep strain, thus, the creep strain results were compared with the numerical outputs. As seen in Table 5, all percentage errors recorded were below than 5%. This showed that all numerical models including the Findley and Burger models fitted the experimental data accurately. According to [PERSON] et al. [54], the acceptable value for percentage error when comparing the experimental outputs with numerical values should be less than 20%. When the percentage error below 20%, it displayed that the plotted experimental data were not severely deviated, and consistent within the principles proposed from the exact numerical model. In this study, the experimental data plotted to elaborate the creep properties of the wooden cross-arms were verified with precise and consistent values.  
Figure 12: \(\eta_{k}\) parameter for current and braced wooden cross-arms: (**a**) right; (**b**) left main members.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
###### Abstract  
Many regions in developing countries do not have any access to communication networks even though the number of devices connected through the Internet of Things (IoT) is increasing significantly. A small satellite platform could provide global network coverage in low Earth orbit to these remote locations at a low cost. This paper describes the overall mission architecture and the implementation of remote IoT using a 1U volume in 6U CubeSat platform named KITSUNE. In KITSUNE, one of the missions is to leverage IoT for building a network of remote ground sensor terminals (GST) in 11 mostly developing countries. This paper evaluates the capacity and coverage of a satellite-based IoT network for providing remote data-collection services to these countries. The amount of data that could be collected from the GSTs and forwarded accurately to the users determines the actual capacity of the Store and Forward (S&F) mission. Therefore, there are several proposed parameters to estimate this capacity in this study. In addition, these parameters are retrieved from the simulations, ground test results, and on-orbit observations with the KITSUNE satellite. The proposed IoT system, which is composed of the GSTs and IoT subsystem onboard KITSUNE satellite, is determined to be capable of providing valuable information from remote locations. In addition, the collected data are achieved and analyzed to monitor sensory data specific to each country, and it could help to generate prediction profiles as well.  
Keywords:small satellite; LoRa; ground sensor terminal; store and forward; satellite IoT; capacity building +
Footnote †: journal:  
_Applied_  
_sciences_
# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work  
The problem of indoor navigation can be basically decomposed into three sub-problems: positioning, environment mapping, and trajectory planning [12]. Additionally, according to ISO 17438-1:2016, a typical intelligent transport systems (ITS) indoor navigation application usually covers indoor positioning and indoor map and navigation services [13]. Navigation refers to one kind of application among others, such as tracking, asset management, and so on. We consider indoor positioning and mapping as the most fundamental technologies, whose requirements of various application situations vary from each other. In this circumstance, in this section, we only briefly introduce the existing indoor positioning and mapping technologies based on a broad review of the literature and then focus on the relevant standards.
## 3 Mathematical Model and Thermodynamics Analysis
As productivity metrics for the multi-generational systems are under study, electrical power generation (\(\dot{W}_{net}\)), freshwater productivity (\(\dot{m}_{fresh}\)), space cooling capacity (\(\dot{Q}_{cooling}\)), and home heating capacity (\(\dot{Q}_{heating}\)) are employed. The system's performance is measured by the total gained output ratio (TGOR), specific total gained energy (STG), specific total gained energy equivalent cost (STGP), gain output ratio (\(\text{GOR}_{\text{HDH}}\)), coefficient of performance of DCS (\(\text{COP}_{\text{DCS}}\)), space supply air temperature and humidity ratio (t4 and w4), and area of solar collectors (\(\text{A}_{\text{solar}}\)). In the present multi-generational system, electrical power, space cooling capacity, freshwater productivity, and hot water production are all produced simultaneously from a single solar energy source.