esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 ESTCube-Luna Preliminary Mission Requirements and Platform Design Solutions
### Electrical Power System (EPS)  
The current design of ESTCube-LuNa EPS includes 106 solar cells, placed on the four largest sides of the spacecraft, as well as on four deployable panels in X-configuration (diagonally from each of the 4U long corners). Estimated maximum power production from the solar panels on the larger nanospacecraft sides is up to 28 W and 20 W.  
The two smallest sides of the cubesat are occupied by the E-sail payload and navigation cameras without solar panels. Since ESTCube-LuNa is designed to operate with an internally fixed spin plane, there will be two seasons when, during two parts of a spacecraft spin, the sides uncovered by solar panels will point towards the Sun. While this is unfavourable, we only need to run the E-sail experiment for one-third of the time in the spin modification experiment, as detailed in Section 2.2.  
The main power consumers of ESTCube-LuNa are given in Table 4. In further ESTCube-LuNa mission analyses, special care must be taken in analysing the energy consumption while running the different power modes, for example, data transmission and ranging sessions, attitude and navigation manoeuvres, and payload operations.
## 4 Numerical Verification of Innovative Designs
### Deformation Analysis  
In accordance with Article 4.2.3 of specification [33] and article 6.2.1 of specification [34], the maximum vertical deformation of the arch rib is not over L/1000 (\"L\" denotes the span) and the vertical deformation of the main girder is not over L/800 under the action of vehicle lane load (excluding impact force). Figures 11 and 12 show the vertical displacements of the arch ribs and main girder, respectively.  
From Figure 11, it can be seen that the maximum vertical displacement of the arch rib under the vehicle load is 30 mm and the transverse displacement is 4 mm; the maximum vertical displacement under the crowd load is 20 mm and the transverse displacement is 16 mm, which meets the design requirements. Figure 11 shows that the maximum vertical  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{
\begin{tabular}{c} **Total Axial Force** \\ **(kN)** \\ \end{tabular} } & \multicolumn{2}{c}{**Secondary Arch Rib**} & \multicolumn{2}{c}{**Main Arch Rib**} \\ \cline{3-6}  & & **Axial Force (kN)** & **Share Percentage (\%)** & **Axial Force (kN)** & **Share Percentage (\%)** \\ \hline
1—1 & \(-\)28,763.56 & \(-\)3470.34 & 12.07 & \(-\)25,293.22 & 87.93 \\
2—2 & \(-\)24,516.02 & \(-\)3498.39 & 14.27 & \(-\)21,017.63 & 85.73 \\
3—3 & \(-\)21,117.33 & \(-\)3544.81 & 16.79 & \(-\)17,572.52 & 83.21 \\
4—4 & \(-\)11,881.99 & \(-\)3571.70 & 30.06 & \(-\)8310.29 & 69.94 \\
5—5 & \(-\)12,405.42 & \(-\)3549.76 & 28.61 & \(-\)8855.66 & 71.39 \\
6—6 & \(-\)13,355.12 & \(-\)3544.85 & 26.54 & \(-\)9810.27 & 73.46 \\
7—7 & \(-\)16,641.71 & \(-\)3183.11 & 19.13 & \(-\)13,458.6 & 80.87 \\ \hline \hline \end{tabular} Note: The negative values in the table represent pressure.  
\end{table}
Table 7: Axial force sharing percentages of main arch rib and secondary arch rib.  
Figure 11: The vertical displacement of the main arch rib and secondary arch rib (unit: mm): (**a**) vehicle load; (**b**) crowd load.  
Figure 12: The vertical displacement of the main girder (unit: mm): (**a**) vehicle load; (**b**) crowd load.  
deformation of the arch rib is not in the middle of the span, but near 3 L/8 and 5 L/8, and the deformation trend is similar to the \"W\" shape because the arch rib structure is spatially Y-shaped. Figure 12 shows that the maximum deflection of the main girder is 40 mm under vehicle load and 20 mm under crowd load, which meets the design requirements. The effect of both vehicle and crowd loads acting together is combined by the combination factor required by specification.
## 4 Encrypted SFC Architecture
### The Infrastructure
#### 4.1.2 Underlay Network  
A threat to this structured networking model is an underlying VXLAN-GPE network. In traditional data centres with no NSH overlay, VXLAN is not defined as an underlay network, but it constitutes an overlay network by abstracting the physical network into one big distributed virtualised switch. This implies that, if all the components in our architecture are running as VMs on one underlying distributed virtual switch, the overlaying NSH switches are unaware of the underlying Compute Node location. This compromises the structured setup of networking components on the Compute Node. We solved this problem by combining VXLAN and NSH networks into one customised virtual switch. In addition, we adopted the architectural networking principles from VMware NSX [23] and pinned the virtual switch to the Compute Node and perceived it as a hypervisor component. We simulated this structure by locking the virtual switch VM to the Compute Node and pretending that the virtual switch was not available in the hypervisor user space. According to the SFC RFC [2], introducing NSH/SFC-awareness to a virtual switch makes it a Service Function Forwarder  
Figure 5: An additional encryption overlay.  
(SFF). Hence, we defined two underlying SFFs to be responsible for each NSH layer respectively and let them also to be responsible for handling the VXLAN-GPE tunnels.
# Reasonable Support Parameters for Loess Tunnel Under Different Water Contents: A Case Study
## 3 On-Site Test of Mechanical Characteristics
### Evaluation of Design Support Parameters  
Based on the on-site monitoring results, the tunnel deformation and support structure stress monitoring results of section ZK90+056 are summarized as listed in Table 6. The rationality of the original design parameters of the support structure is analyzed, and the safety of the tunnel support structure is evaluated.  
The monitoring results show that the maximum settlement of the crown of the monitoring section is 64 mm, and the maximum horizontal convergence is 32.7 mm. These values are much smaller than the design reserved deformation of 150 mm. The maximum compressive stress of the steel rib is located at the inner left arch foot, with a compressive stress value of 149.1 MPa, accounting for 63.4% of the yield strength of the steel rib. The maximum tensile stress is located at the outer right arch foot, with a tensile stress value of 15.4 MPa, accounting for only 6.5% of the yield strength of the steel rib. The maximum compressive stress of shotcrete is located at 30\({}^{\circ}\) of the left arch waist, with a compressive stress value of 11.9 MPa, accounting for 88.1% of the design compressive strength of C25 shotcrete. The maximum tensile stress is located at the left side of the invert, with a tensile stress value of 0.8 MPa, accounting for 62.9% of the design value. Based on the above analysis, it can be concluded that the deformation of the primary support structure and the stress at each measuring point meet the safety requirements of the support structure, indicating that the support structure is safe and reliable, and has sufficient safety reserves.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Monitoring Projects**} & \multicolumn{1}{c}{\begin{tabular}{c} **Maximum** \\ **Values** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **Design** \\ **Value** \\ \end{tabular} } & \multicolumn{1}{c}{**Proportion**} & \multicolumn{1}{c}{**Position**} \\ \hline Deformation & vertical settlement & 64 & 150 & 42.7\% & crown \\ /mm & horizontal convergence & 32.7 & 150 & 21.8\% & middle bench 3–4 \\  & \begin{tabular}{c} compressive stress \\ tensile stress \\ \end{tabular} & 83.9 & 235 & 35.7\% & right arch waist 30\({}^{\circ}\) \\ \multirow{2}{*}{\begin{tabular}{c} Steel rib \\ stress/MPa \\ \end{tabular} } & \multirow{2}{*}{outer side} & \multirow{2}{*}{\begin{tabular}{c} compressive stress \\ tensile stress \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 149.1 \\ tensile stress \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 235 \\ tensile stress \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 63.4\% \\ 6.5\% \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} left side of arch foot \\ right side of arch foot \\ right side of arch foot \\ left arch waist 30\({}^{\circ}\) \\ left side of invert \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table}
Table 6: Safety evaluation of ZK90+056 section support structure.  
Figure 9: Shotcrete stress in section ZK90+056.
## 3 Review of Sensor Requirements for A3R
### Method for Sensor Selection and Limitations  
The framework for A3R described in this paper was developed using the current NATO standards for crewed United States Aerial Refueling [6] and uncrewed A3R [44; 47]. These UAs must be over 1320 pounds at Maximum Gross Takeoff Weight (MGTW), fly above 18,000 feet (ft.) Mean Sea Level (MSL), and be able to fly at any airspeed. Both the X-47B and MQ-25 mentioned in the history of A3R are Group 5 UAs.  
When determining types of sensors required for specific tasks, if a transmit or receive requirement exists, knowing the required frequency band will help determine the size, weight, power, and cost (SWaP-C) [48]. Therefore, when applicable, a frequency band will be provided for each sensor based on previous research with the understanding that new sensors could be designed to satisfy the task while operating in a different frequency band [49]. Research of sensors that a Group 5 UA could not carry due to SWaP-C or due to technology immaturity were not included in this analysis. For many of the tasks, multiple sensors could provide the required information to complete the task, and therefore, the concept of sensor fusion and concerns related to the fusion of information will be presented as well.  
Many of the sensors discussed are installed on platforms currently or previously included in the NATO inventory. Due to potential distribution limitations, only open-source articles were included in this analysis of sensors that could complete the required task. While the Lockheed Martin website lists that the F-35, the newest fifth-generation fighter in the NATO inventory, includes an active electronically-scanned array (AESA) radar, distributed aperture system (DAS), and Electro Optical Targeting System (EOTS) [50], information on those specific sensors is limited, and therefore, the concept of including an air-to-air (A/A) radar onboard a UA to provide relative location will be introduced using academic research on the current improvements to X-band radars.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
## 3 Results  
The acoustic power measurement system described in this paper has two main components: (i) the real-time data-acquisition system that measures the pressure both in the compliance (\(P_{C}\)) and the resonator (\(P_{E}\)) and (ii) the data-processing system, which scales and filters the measurements (Section 3.1) and then calculates the power delivered to the load (Sections 3.2 and 3.3).  
Figure 5: Voltage, current, and instantaneous power in a resistive element.  
The former, which builds upon a thoroughly validated setup, described in previous research [30], is based on an Arduino platform designed to accurately calculate the acoustic power of the TA-SLiCE. In that earlier work, the performance of this setup was shown to be both reliable and accurate, confirming its suitability for measuring acoustic power in thermoacoustic engines. Integrated with piezoelectric low-cost pressure sensors, the system enables the real-time measurement and transmission of sensor data, ensuring continuous monitoring of pressure variations in two key components of the system: the compliance, which functions as a dynamic capacitor, and the resonator, responsible for the acoustic oscillations.  
The latter is described in this section of the paper. Two different approaches for estimating the power delivered to the load are proposed:  
* Instantaneous power evaluation (method 1), which consists of using the instantaneous power consumed in the resistance of the equivalent RC model of the load;
* One-period average power calculation (method 2), in which the procedure established by [PERSON] et al. [17] is adapted to be used each single period for each single period of the signal.
# A New Knowledge-Distillation-Based Method for Detecting Conveyor Belt Defects
## 3 Result and Discussion
### Data Augmentation Strategy Ablation Experiments  
To prove the effectiveness of our data augmentation strategy, we used different datasets to train YOLOv5n, and compared the detection results of different datasets when using the same detection model.  
From Table 3, we can see that, when we train the model using the original data, it is difficult to train the detection model effectively due to the small number of samples; thus, the detection accuracy of the model is relatively poor. Then, we used traditional data augmentation methods, such as rotation, translation and cropping, to augment the data: the detection accuracy was improved to a certain extent. After that, we used our data augmentation method combining GAN and copy-pasting strategies to augment the dataset, and the detection accuracy was further improved. However, since the number of conveyor belt defect samples was still too small, in order to improve the detection accuracy of the defect parts, we oversampled the conveyor belt defect samples in the training set to obtain the final dataset. Finally, we obtained 7276 conveyor belt images, which contained about 12,000 scratch samples and about 1200 conveyor-belt-edge defect samples.  
After data augmentation, the number and diversity of conveyor-belt-damage samples in the dataset were significantly improved, increasing the number of samples while enriching the diversity of samples and improving the generalization ability of the detection model. The [EMAIL_ADDRESS] can reach 95.57% accuracy in detecting conveyor belt damage, which meets the requirements for detection accuracy in industrial applications.
## 3 Results and Discussion
### Monitoring Results
The displacements at the corner of STA.85 were found to range from a minimum of 4.583 mm (STA.85_3) to a maximum of 26.192 mm (STA.85_5). This was similar to the observation for STA.80, and it was much larger than the displacement value obtained from the traditional monitoring method (total station). The measurement and monitoring results of the traditional monitoring method and digital camera-based digital image photogrammetry revealed significant wall facing displacements at 18, 28, and 111 days. These were attributed to a large amount of rainfall that occurred just before the elapsed time due to the seasonal characteristics of South Korea; the rainfall penetrated the anomalous area found in the electrical resistivity survey's results, causing the wall facing's displacement. Based on the measurement and monitoring results evaluated above, the difference between the measurement results of the traditional monitoring method and digital camera-based digital image photogrammetry was calculated. Figure 11 shows the measurement position error as a function of the elapsed time. Due to the high reliability of the traditional monitoring method, which is the total station, the displacement error was defined as  
Figure 10: Elapsed time–displacement relationship depending on the position of the wall facing (monitoring for 7 months): (**a**) total station value [STA.80]; (**b**) total station value [STA.85]; (**c**) digital camera value [STA.80]; (**d**) digital camera value [STA.85]. the difference between the displacement values obtained with the traditional monitoring method and digital camera-based digital image photogrammetry. For STA.80, the two highest error levels occurred at 80 days and 174 days from the initial measurement date. STA.85 showed similar results. The largest errors occurred at positions STA.80_4-STA.80_5 and STA.85_4-STA.85_5, which means that the error was larger when the displacement was relatively large. Table 4 shows the results of the comparison conducted for 4 months using traditional monitoring and digital image photogrammetry methods (using digital cameras and cellphone cameras). The 3D coordinate data of the monitoring results using three types of devices are very large. Therefore, the values expressed in Table 4 are the displacement vectors calculated using 3D coordinates and the vector difference between the traditional monitoring method and digital image photogrammetry (digital camera and cellphone camera).
## 3 Results and Discussion
### Effect of the Blockage Ratio on Thermal Performance  
In general, the assessment of the enhancement of the heat transfer is done by calculating the thermal performance of the system. Figure 8 shows the effect of the blockage ratio (\(R_{b}\)) on the thermal performance factor (\(\eta\)) as a function of Reynolds number (\(Re\)). The downward trend of \(\eta\) corresponds to the increasing \(Re\) for all test conditions.  
Not all turbulators provide energy saving at high Reynolds numbers. The boundary layer of the heat exchanger directly influences thermal performance [26]. Any increase in the thickness of the boundary layer increases thermal resistance. This phenomenon mostly occurs in the laminar region or at a low Reynolds number. An increase in the Reynolds number because of increased flow velocity shrinks the boundary layer. A protuberance device such as a DWT is effectively disruptive at a low Reynolds number. The thermal performance of the heat exchanger with the addition of DWTs with \(R_{b}\) = 0.28, 0.35, and 0.42 and LS are in the ranges of 0.91-1.22, 0.96-1.30, 0.87-0.95, and 1.01-1.4, respectively.  
Figure 7: Effect of blockage ratio (\(R_{b}\)) on the friction factor behavior (\(f\)) with Reynolds number (\(Re\)).
## 5 Experiments  
We conduct an empirical study to demonstrate the effectiveness of the proposed methodology, with a particular focus on how the synergistic combination of VRKG, LWS, and GAT addresses both global semantic consistency and local dynamic preferences. The experimental results answer the following research questions, which are designed to validate the effectiveness of each component in overcoming long-tail sparsity and modeling dynamic user preferences:  
* RQ1: How does this paper's method perform in terms of recommendation performance compared to state-of-the-art knowledge graph methods?
* RQ2: What is the contribution of the key components of this paper's approach to model performance?
* RQ3: How do hyperparameters such as the number of iterations of LWS, number of GAT layers, embedding dimension, etc.) affect the model performance?
* RQ4: How does this paper's approach explore user preferences and provide intuitive interpretability?
###### Abstract  
The main contribution of this paper is the proposal of a six-degree-of-freedom (6-DoF) refueling robotic arm positioning and docking technology guided by RGB-D camera visual guidance, as well as conducting in-depth research and experimental validation on the technology. We have integrated the YOLOv8 algorithm with the Perspective-n-Point (PnP) algorithm to achieve precise detection and pose estimation of the target refueling interface. The focus is on resolving the recognition and positioning challenges of a specialized refueling interface by the 6-DoF robotic arm during the automated refueling process. To capture the unique characteristics of the refueling interface, we developed a dedicated dataset for the specialized refueling connectors, ensuring the YOLO algorithm's accurate identification of the target interfaces. Subsequently, the detected interface information is converted into precise 6-DoF pose data using the PnP algorithm. These data are used to determine the desired end-effector pose of the robotic arm. The robotic arm's movements are controlled through a trajectory planning algorithm to complete the refueling gun docking process. An experimental setup was established in the laboratory to validate the accuracy of the visual recognition and the applicability of the robotic arm's docking posture. The experimental results demonstrate that under general lighting conditions, the recognition accuracy of this docking interface method meets the docking requirements. Compared to traditional vision-guided methods based on OpenCV, this visual guidance algorithm exhibits better adaptability and effectively provides pose information for the robotic arm.  
robotic arm; object detection; pose estimation; recognition method +
Footnote †: journal: Applied  
18 January 2024  
Position and docking based on RGB-D Visual Guidance
## 2 Measurement Setup and Technique  
### Room Setup  
The RSoANU dataset consists of RIRs measured in the ANU Recording Studio [21] with a floor dimension of 8.5 m \(\times\) 9 m. The studio features a double-height space with a floor-to-ceiling height of 5.6 m. Two adjacent walls are fitted with variable wall paneling, offering options in both felt and wood materials, whilst the other two walls have fixed paneling. The room also includes a window and a sliding door made of glass material providing access to the control room.  
The variable wall panels set to felt and wooden configurations are shown in Figure 1a and Figure 1b, respectively. The structural details and dimensions of the felt and wooden panels can be seen in Figure 1c and Figure 1d, respectively. In the wooden configuration, angled plywood frames with a width of 0.603 m and a depth of 0.398 m alternate with slat resonators measuring 0.617 m in width and 0.137 m in depth. Note that these depths are measured from the middle of the angled surface to the wall. Behind the angled plywood surface, three layers of 50 mm insulation separated by air cavities are affixed, while one insulation layer is attached behind the slat surface. In the felt configuration, each panel has a depth of 0.268 m with two layers of 50 mm insulation behind the surface. More details on similar variable wall panel structures can be found in [22].
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 1 Introduction
Despite its importance and perhaps because of LoRa's relative novelty, very little research has been carried out on these topics. Figure 1: Linear wireless sensor networks and applications [13]. In this paper, we addressed these issues. We discuss the issue of managing contention in LoRa-based linear networks. Our results showed that the inherent interference-rejecting characteristics of LoRa lead to a very simple design approach for managing contention. We explored this approach with network widths of one, two, and three relays. We demonstrate that the approach, although simple, achieved 98% throughput for a three-hop network. We also discuss the effectiveness of a gossip-based probabilistic approach in the presence of various active relay nodes using a one-hop model. The contribution of this paper can be summarised as follows:  
* We describe a simple and effective scheme to deal with contention within relays in linear networks that makes use of the fundamental behaviour of LoRa. * We evaluated the performance of the approach in linear networks of up to three hops and up to a thickness of three nodes. * We evaluated a gossip-routing-based approach applied to linear networks. The rest of the paper is structured as follows. Section 2 presents the related work in this context. Section 3 presents the motivation for this research. Section 4 presents the network designs and experimental setup. The evaluation of the proposed design approaches and the results are discussed in Section 5. Section 6 discusses the impact of hardware imperfection on network performance. Lastly, in Section 7, we summarise our work and point to future research.
## 4 Data Diversity of UAV
### Overview of UAV Dataset Uses
#### 4.1.2 Surveillance  
In urban and rural settings, UAV datasets support advanced surveillance activities. They facilitate the monitoring of traffic, detection of illegal activities, and overall urban planning by providing real-time, high-resolution aerial views.