esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Safe and Efficient UAV Route Construction Method over Wireless Multi-Hop Network
### Method for Constructing Optimal Routes in Terms of Distance and Safety Based on OLSR  
We have proposed an OLSR-based method, in which each node constructs an optimal route that is a safe route with a short \"population-dense regions distance\", and in which the distance from the source node to the destination node, i.e., the \"total distance\", is not too long [37].
## 4 Experimental Results and Analysis
### Static Detection Results and Analysis  
#### 4.2.1 Experimental Results  
In object detection, the mean average precision (mAP) is often used as the measurement index of target recognition accuracy, and refers to the average value of recognition accuracy of all categories [26]. Table 3 shows the corresponding results before and after model improvement; mAP = 84.23% after prior box size adjustment and improvement (named K-YOLOv4). The mAP of the final improved model Bamboo-YOLOv4 combined with the soft NMS algorithm was 87.42% after training, which is 10.85% higher than that of the basic model. Figure 8 shows the comparison of detection results before and after the improvement of the soft NMS algorithm. After improvement, even small bamboo shoots that are close together can be detected.  
Figure 7: Loss curve during detection model training.  
For the pictures taken in real time, the system automatically detects the moso bamboo shoot detection frame and the corresponding coordinates. Figure 9 shows the detection results of two randomly selected images. Figure 9a on the left shows 25 moso bamboo shoots collected in Jing County on 14 April 2019, and Figure 9b on the right shows 23 moso bamboo shoots collected on Mufu Mountain on 31 March 2021.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 5 Experimental Studies  
In the experimental studies, a 6-DOF robotic manipulator (named AR5 from Asage-Robots Ltd., China) was taken as the objective. The end position of the manipulator was measured by an artifact installed at the end, which was measured by three displacement laser sensors (Keyence LK-G500) [23]. The measurement system is shown in Figure 10.  
Figure 8: Compensated angles for joint angle \(\theta_{1}\sim\theta_{6}\).  
Figure 9: Spatial coordinate errors before the error correction.  
Based on the previously established joint angle-stiffness-spatial position error model, a series of experiments were carried out to verify the effectiveness of the model. The experiments included two parts. In the first part, the space position of the manipulator was measured under the same trajectory but with different loads. Six random points were selected from the measurement range of the manipulator and the measurement system. The spatial position of each point was measured under different loads at the end of the manipulator to verify the regularity of the joint angle-stiffness-spatial position model. In the second part, an additional error compensation experiment was carried out, and the spatial error of the manipulator end was compared between the simulation results and the measured results.
# Macro-Meso Mechanical Behavior of Loose Sand under Multi-Directional Cyclic Simple Shear Tests
## 2 Bi-Directional Cyclic Simple Shear Test and Its Simulation  
### Variable Direction Dynamic Cyclic Simple Shear Apparatus  
This study used the Variable Direction Dynamic Cyclic Simple Shear (VDDCSS) system for simple shear tests, as shown in Figure 1a, which is capable of applying shear stresses in any direction on the horizontal plane by combining two perpendicular shear motors. In our experiments, a cylindrical sample with a diameter of 70 mm and a height of 17 mm (after consolidation) was used, which had a large diameter-to-height ratio (D/H = 4.12) and can reduce the non-uniformity of stress and strain in the specimen [6; 15]. The VDDCSS used two drainage lines to control drainage conditions, and the radial constraint was composed of multiple Teflon coated rings, 1 mm high each, and their stiffness meet the requirement of a \(K_{0}\)-consolidated condition, as shown in Figure 1b,c.  
In order to model the particle movements, it is necessary to let the radial boundary move freely. On the contrary, most numerical models for simple shear tests used radial constraints (parallel lines in 2D models and layered cylindrical walls in 3D models) to add shear stress and strain on specimens. The method added additional stresses on specimens and caused differences between tests and simulations. In this study, ring-shaped rigid clumps were used as radial constraints. Each clump was fixed at the height that same as corresponding rings in tests, as shown in Figure 2, and it was allowed to move freely in the horizontal plane. A clump ring was generated by 360 interconnected spheres, and 18 clump rings are sequentially generated at different heights on the radial boundary of a specimen. In order to reflect the low friction between the Teflon rings in the experiment, a small gap was left between each clump ring. Since no contact force was required within a clump ring, this model can provide accurate boundary conditions while ensuring accurate simulation results. Particle velocity distribution under new boundary conditions is shown in Figure 3, which shows the layered movement of soils without strong contacts on radial constraints.  
Figure 1: Variable Direction Dynamic Cyclic Simple Shear system: (a) apparatus; (b) mold; and (c) sample during shearing.  
In undrained simple shear tests, specimens were sheared by horizontally moving the bottom plate while the top plate was fixed in all directions. In the DEM model, samples were sheared in the same fashion. In the experiment, in order to increase the friction between the bottom plate and particles, several circular ridges were used on the contact surfaces, as shown in Figure 4a. In order to model the ridges and large friction, this study used a clump plate, as shown in Figure 4b.
## 6 Conclusions  
1. A methodology based on a combination of MoM and FEM is proposed for calculating the coupling voltage and current on buried pipelines induced by grounding currents while taking into account nonlinear polarization. The validity of the calculation method was verified by experiment.
2. Both the maximum leakage current density and maximum PSP increase as the grounding electrode current increases. The relationship between the maximum leakage current density and the electrode grounding current is linear. The corrosion area becomes more concentrated as the grounding current increases.
3. The maximum leakage current density increases exponentially when the distance between electrode and pipeline decreases. The distance between the electrode and pipeline has a significant influence on the corrosion area range. The corrosion area range increases substantially as the distance increases, whereas the influence degree decreases substantially.
4. The maximum leakage current density and PSP increase as the soil resistivity increases. The relationship between the maximum leakage current density and the soil resistivity satisfies the logarithmic function. Both the maximum leakage current density and corrosion risk area are positively correlated with the soil resistivity.  
[PERSON] conducted theoretical research, characteristics computation, and wrote the paper; [PERSON] and [PERSON] are responsible for the experiment design and data analysis; [PERSON] gave some good advice during the experiments and paper writing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the State Grid UHVDC Test Site of China for providing the experiment environment.  
The authors declare no conflict of interest.
## 5 Experimental Results
### Comparative Evaluation of faSTNT and TextNetTopics Performance
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accumulated Topic Clusters**} & \multicolumn{1}{c}{**Words**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**Specificity**} & \multicolumn{1}{c}{**F-Measure**} & \multicolumn{1}{c}{**AUC**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Cohen’s Kappa**} \\ \hline \multirow{9}{*}{**Coun**} & 1 & 4.9 & 79.3 & 61.9 & 96.3 & 74.6 & 80.1 & 94.5 & 58.4 \\  & 2 & 9.8 & 87.3 & 81.2 & 93.4 & 86.3 & 89.6 & 92.5 & 74.6 \\  & 3 & 14.5 & 90.5 & 87.5 & 93.5 & 90.1 & 93.2 & 93.0 & 81.0 \\  & 4 & 19.2 & 91.7 & 89.3 & 94.0 & 91.4 & 94.2 & 93.7 & 83.4 \\  & 6  
For instance, with the WOS-5736 dataset (refer to Figure 13), faSTNT (\(v=60\%\)) attains an F1-score of 96.9% with just 72 words, compared to 212 words for TextNetTopics, marking a 66% reduction (140 words). Similarly, for the LitCovid dataset (refer to Figure 14), faSTNT (\(v=60\%\)) reaches an F1-score of 91.0% using 108 words, while TextNetTopics requires 262 words, resulting in a 59% reduction (154 words). For the MultiLabel dataset (refer to Figure 15), faSTNT (\(v=40\%\)) achieves an 85.1% F1-score with 113 words, compared to 197 words for TextNetTopics, indicating a 43% reduction (84 words). For the arXiv dataset (refer to Figure 16), faSTNT (\(v=40\%\)) secures a 92.8% F1-score with 90 words, whereas TextNetTopics needs 170 words, achieving a 47% reduction (80 words). These results underscore faSTNT's efficiency in maintaining high classification performance while significantly reducing the feature set size, making it a robust solution for text classification tasks. Figures 17 and 18 summarize the percentages of feature reduction achieved by faSTNT compared to TextNetTopics across various feature-discarding percentages (\(\%\)%). The figures highlight the ability of faSTNT to maintain F1-score performance comparable to the maximum score obtained by TextNetTopics while discarding a significant proportion of lower-importance features. For instance, for the arXiv dataset, setting \(\%\)% to 10%, 20%, 30%, and 40% yields a steady increase of 12%, 18%, 31%, and 47% feature reduction, respectively, while maintaining an F1-score of 92.8. The superior performance of faSTNT can be attributed to several key factors. First, the use of Random Forest-based feature importance values ensures that the most significant features are retained. By discarding the least informative features, faSTNT effectively reduces redundancy and noise, enhancing the relevance of the remaining features in LDA topics, which leads to better model training and performance. Second, the integration of fastText word embeddings helps capture semantic relationships between words more effectively than the traditional LDA approach used in TextNetTopics. This allows faSTNT to form more coherent and discriminative topic clusters. Lastly, faSTNT's scoring method, which trains all topics simultaneously and considers all topic interactions, enhances the
# Study on Drone Handover Methods Suitable for Multipath Interference Due to Obstacles
###### Abstract  
Networks constructed in the sky are known as non-terrestrial networks (NTNs). As an example of an NTN, relay transmission using drones as radio stations enables flexible network construction in the air by performing handovers with ground stations. However, the presence of structures or obstacles in the flight path causes multipath interference; consequently, the propagation environment fluctuates significantly based on the flight. In such a communication environment, it is difficult for a drone to select an optimal ground station for a handover. Moreover, unlike a terrestrial network, the propagation environment of a flying drone is affected by structures and other factors that cause multipaths based on the flight speed and altitude, making the conditions of the propagation environment even more complex. To solve these problems, we propose handover schemes between drones and the ground that consider the multipath interference caused by obstacles. The proposed methods are used to perform handovers based on an optimal threshold of received power considering interference and avoid unnecessary handovers based on the moving speed, which makes the handover seamless. Finally, we develop a simulator that evaluates the cross layer from propagation to upper network protocols in a virtual space, including buildings, evaluate the communication quality of a drone flying in a three-dimensional space, and confirm the effectiveness of the proposed methods as well as the evaluation of the real environment.  
drones; handover; multipath interference; non-terrestrial networks Article
## 4 Experimental Results
### AP Density  
The exact number of training samples for each cell is displayed in Table 4. Cell 1 had the most visible APs with 120, while Cell 11 had the fewest visible APs with 34. The visibility of each AP means the number of observation from the AP compared to the total number of measurements. These 120 visible APs were _registered_ in the training data and used to extract the invisible AP sets for each cell. Figure 6 gives the example of the AP intensity and the missing data percentage for Cells 1 and 11. We can clearly see that different cells have distinct visible and invisible AP sets. The missing data percentage can be as high as 98.87% for Cell 1 and 99.92% for Cell 11 from AP 109 and AP 78, respectively. We manually removed APs with less than 1% visibility before applying the EM imputation to avoid singular covariance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RSS Properties** & **RP1 (Device 6)** & **Cell 11 (Device 6)** \\ \hline Number of scan & 400 & 4000 \\ Number of visible APs & 21 & 23 \\ Mean RSS of AP19 (dBm) & \(-66.90\) & \(-65.42\) \\ Mean RSS of AP57 (dBm) & \(-78.94\) & \(-78.95\) \\ Mean RSS of AP74 (dBm) & \(-69.33\) & \(-65.08\) \\ standard deviation of AP19 (dBm) & \(1.82\) & \(3.29\) \\ standard deviation of AP57 (dBm) & \(0.84\) & \(3.05\) \\ standard deviation of AP74 (dBm) & \(1.87\) & \(3.27\) \\ Correlation (AP19, AP57) & \(0.086\) & \(0.21\) \\ Correlation (AP19, AP74) & \(-0.27\) & \(0.50\) \\ Correlation (AP57, AP74) & \(-0.18\) & \(0.37\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation between APs (single point vs. cell).  
Figure 6: AP density and missing data percentage at Cells 1 and 11.
## 3 Simulation Results and Discussion
### Network Lifetime Simulation and Results  
We define the network lifetime as when 10% of nodes have exhausted their energy. Along with the increasing number of nodes, network lifetime decreases mainly because the increased number of sub-nodes make the source node consume more energy.  
We can see in the Figure 7 the lifetime of TSR, gossiping protocol, polling algorithm and flooding protocol decrease rapidly when the node number is increased to 20 from 10. We can see that TSR has the longest lifetime compared with the other three protocols.  
\begin{table}
\begin{tabular}{|c|c|} \hline Routing protocols & TSR, TSBR, TSBNC \\ MAC protocol & Based on TDMA \\ Area & 1,000 m \(\times\) 1,000 m \\ Forward model & Underwater communication \\ Node distribute & Random \\ Node number & 100 \\ Communication range & 10 m \\ Delivery probability & 90\% \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 7: The network lifecycle under different node number.
## 4 Discussion
### Model Accuracy  
The average \(R^{2}\) (CV) in our study was 0.68 and 0.58 at two depths, which exceeded the average \(R^{2}\) (0.49) in broad-scale SOC DSM studies, indicating QRF had better performance in the area [7]. Notably, we found more deviation in low and high SOC (Figure S1). That is because bagging ensemble algorithms aggravate the final prediction from multiple individual predictors, which can improve robustness but reduce sensitivity to extremums [49]. Compared with the plough layer, the model accuracy in the compacted layer was lower. That is because there is a higher coefficient of variation and SD in the compacted layer (Table 2), which may have negative effects on model fitness [64]. Additionally, environmental covariates were almost driven from satellites and meteorological stations, which can reflect more land surface information.
# Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications
## 3 Antenna Fabrication and Discussion of Results  
Figure 8 illustrates the fabricated prototype of the proposed antenna system with four ports. The fabrication was done using chemical etching which is a popular method to remove excess copper from the surface of a copper-coated substrate. This technique involves coating the copper surface with a photosensitive material called \"resist\", exposing it to light through a mask to create a circuit pattern, and then developing the resist to create an image of the circuit pattern.  
Figure 7: Simulated isolation coefficients between the NB antennas and WB antenna with different values of the capacitor (**a**) between P-1 and P-3 (between WB and NB), (**b**) between P-1 and P-4 (between WB and NB), and (**c**) between P-3 and P-4 (between NB and NB) and (**d**) between P-2 and P-4 (between WB and NB).  
Next, the board is placed in an etchant solution that selectively dissolves the unprotected copper areas, leaving behind the desired circuit pattern. The etchant solution usually contains chemicals such as ferric chloride, ammonium persulfate, or cupric chloride, which react with copper to dissolve it. Once the required amount of copper has been removed, the board is rinsed with water to eliminate any residual etchant solution, and the resist is removed, revealing the final circuit pattern on the copper-coated substrate. Chemical etching is a highly reliable and widely used method that enables the production of high-quality PCBs with precise circuit features. The proposed antenna was measured for its S-parameters and radiation characteristics.
## 4 Performance Evaluation
### SE and IF for the Proposed DPC Scheme with \(\lambda=1\) in the Absence of Cellular Users  
Figure 4 depicts the average SE results of the conventional WMMSE and DPC schemes and the proposed DPC scheme with \(\lambda=1\) for various area sizes in Rayleigh fading channels (i.e., \(m=1\)), where the proposed scheme with \(\lambda=1\) means that it only focuses on SE maximization with no consideration of IF, and the conventional DPC scheme to maximize SE [13] is used. In the figure, the proposed scheme has greatly better SE performance than the conventional WMMSE and DPC schemes for all the sizes of area. From those results, it is verified that the BN process, which is applied into the proposed scheme, works effectively to attain the significant SE improvement. Additionally, from the figure, it is observed that the SE performance of all the power control schemes becomes worse as the area size increases because of the path loss effect.  
Figure 5 shows the IF results of the conventional WMMSE and DPC schemes and the proposed DPC scheme with \(\lambda=1\) for various area sizes in Rayleigh fading channels, where no fairness is considered in both the conventional and proposed DPC schemes. In the figure, the conventional DPC scheme has the poorest IF, whereas the WMMSE scheme achieves the perfect fairness, i.e., \(IF=1\). The proposed DPC scheme with \(\lambda=1\) obtains better IF results than the conventional DPC because of the BN process. It is also demonstrated that the IF results for all of the schemes are not changed by the area size. It is remarkably noted that the proposed DPC works better than the conventional DPC in terms of both SE and IF. In addition, the figure illustrates that the proposed scheme is inferior to the WMMSE scheme in terms of fairness and, thus, it requires pertinent \(\lambda\) in our loss function to achieve the perfect fairness.
## 3 Positioning System Experimental Platform
### WinBus
#### 3.1.2 Global Positioning System (GPS)  
GPS is the only fully operational global navigation satellite system in the world. This system has about 24 to 32 Earth-orbiting satellites. GPS satellites are usually located at an altitude of 20,000 km from the Earth and operate at a speed of 14,000 km per hour. GPS  
Figure 1: Sensors deployed in the WinBus.  
signals cannot penetrate walls. Signals in buildings are also blocked. It is less effective when the user uses it in an area with tall buildings or dense trees. Therefore, GPS signals are rarely used directly in practical applications.  
The real-time dynamic technology uses a GNSS network composed of multiple satellite positioning reference stations to evaluate the positioning error of the area covered by the reference stations. Then, it matches the observation data of the nearest physical base station to produce a virtual base station as the RTK master station. The autonomous vehicle does not receive actual observations from a physical base station. The error-corrected virtual observation data are the information used by the applications. The WinBus integrates RTK to provide location information for applications. RTK is one of the positioning systems for the WinBus.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 1 Introduction
After performing the data imputation, the feature extraction process should be performed, which commonly includes a variety of time and frequency domain features, such as the mean, energy, correlation, entropy, frequency of maximum values, standard deviation, maximum, minimum, median, variance, 75 th percentile, inter-quartile range, average absolute difference, binned distribution, energy, Signal Magnitude Area (SMA), zero-crossing rate, number of peaks, absolute value of short-time Fourier transform, power of short-time Fourier transform, skewness, kurtosis, and power spectral centroid [9; 21]. After the extraction and selection of different features, different machine learning methods may be included in the pipeline, such as Random Forest, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Naive Bayes, Logistic regression, decision tree, K-Nearest Neighbor (KNN), among others. The rest of the paper is organized as follows: Section 2 presents the methodology implemented in this study. The consequent results are presented in Section 3 and are discussed in Section 4. This study is finalized with the conclusions in Section 5.