esa-sceva/satcom-chunk-collection huggingface dataset:
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD  
### General System Overview  
LHD are large vehicles used in underground mining. In this work a LHD model LF-11H, from the GHH Fahrzeuge manufacturer, is used. The vehicle's size is 9.71 [m] in length, 2.45 [m] in width, and 2.45 [m] in height. The LHD's navigation is based on the data provided by two laser scanners (2D LIDARs), one pointing towards the front of the machine, and the other pointing towards the back. For supervision and occasional teleoperation, two cameras give the front and back images to the control station [27]. All on-board processing is done on an industrial computer running Linux OS, while the direct machine control is handled by an internal PLC unit. The LHD and the hardware components mentioned above can be seen in Figure 1.  
In order to navigate autonomously inside the network of tunnels of an underground mine a proper representation of the mine is required, in this case a navigation map that includes the topological structure of the mine, tunnels, and intersection, as well as LIDAR measurements. The LHD, therefore, builds a map of the operation area during the system setup, using measurements from LIDAR sensors, as it navigates the tunnels. This map is then linked to a topological representation, thus giving names to all the relevant locations, henceforth referred to as \"nodes\". The map, once built, allows for route planning and self-localization of the machine. The latter is carried out by means of scan matching between current LIDAR measurements and the LIDAR landmarks stored in the map. The scan matching is implemented using the ICP (Iterative Closest Point) algorithm, and the final pose estimation is the result of applying a Kalman Filter.  
Every time a new mission in which the LHD is requested to move to a target node in the mine is executed, a topological route composed of nodes is defined, and then target poses inside each node are calculated. Afterwards, the path required to reach each target pose is computed, and a reactive control algorithm is put in charge of following the path, keeping the vehicle away from colliding with the walls of the tunnel. A model predictive control strategy is implemented because of the complexity of following the path with this articulated machine, which normally moves at a speed of between 12 and 24 km per hour inside the mine.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 4 Simulation Results
### Comparison with the State-of-the-Art Methods
#### 4.2.3 Scenario 3  
In this scenario, we examine the localization performance of different methods by varying the mean standard deviation, \(\mu_{n}\), of the RSS measurement errors. Figure 6 presents the comparison results when \(N=5\), \(T=5\), \(\mu_{m}=10\,\)deg, and \(\mu_{p}=10\,\)deg. From the figure, it can be observed that the RMSEs of the TSLS, WLS-d, and ENWLS are insensitive to changes in \(\mu_{n}\), while the LS exhibits a significant increase with the increase in \(\mu_{n}\). Different weight calculations lead to different outcomes, even though they are all WLS methods. The RMSE of the CRLB increases slightly as \(\mu_{n}\) increases, which means that the theoretical lower bound of the localization error also increases with \(\mu_{n}\). This implies that the weights of RSS measurement are either too small (in TSLS, WLS-d, and ENWLS methods) or too large (in LS methods). The RMSE of the proposed method increases slightly as \(\mu_{n}\) increases, which is consistent with the trend of the CRLB. This shows that the weights calculated by the proposed method are reasonable.
# Suppression Effect of Waterborne Polymer on Soil Used for Backfilling at Construction Site
## 5 Conclusions  
1. The contact angle between the waterborne polymer dust suppressant and the soil for backfilling at construction sites, as well as the Zeta potential of the stabilized soil for backfilling, were studied. The polymer improved the interaction between the backfill particles, the average moisture content during drying increased by 118.8%, and the stability of the backfill was significantly enhanced.
2. The wet dust suppression performance was evaluated against time, where the dust suppression efficiency was at least 60%. With a polymer mass of 1.04%, the dust suppression efficiency of PM\({}_{10}\) at 35 \({}^{\circ}\)C and 45% RH reached 78.95%, and the dust suppression efficiency lasting until day 5 was 64.47%.
3. The dry dust suppression performance was examined with compression strength at room temperature. The waterborne polymer promoted the aggregation and cohesion of fine particles below 10 \(\upmu\)m, and the strength on day 28 reached 4.97 MPa, which was 3.94 times higher than that of the watered samples. It was, therefore, concluded that the waterborne polymer could effectively resist wind-blown dust, and the stabilized soil for backfilling exhibited high stability.
4. According to the results at the construction site, the dust suppression efficiencies for PM\({}_{10}\) and PM\({}_{2.5}\) reached 67.41 and 53.68%, respectively, which was consistent with the estimated results. It confirmed the feasibility of the evaluation method used in this paper and the onsite stabilization effect of the polymer.
5. At 15 days after stabilization, the surface crust of the soil for backfilling was stable, and the plant growth was unaffected, so the polymer exhibited good environmental safety. The above results provide material and method support for dust suppression at construction sites.  
Field work and experiment, S.Y. and Z.Q.; data analysis, S.Y.; writing--original draft preparation, S.Y.; writing--revision and editing, S.Y. and F.Z.; project administration, F.Z.; funding acquisition, F.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Program of Tianjin, China (16 ZXCXSF00010), and the Science and Technology Program of Hebei Province (17273703D).  
Not applicable.  
We are grateful for the comments of the anonymous reviewers, which greatly improved the quality of this paper.  
The authors declare no conflict of interest.
## 4 Conclusions  
The current study aimed to assess the impact of hydrogen, added at concentrations of 5%, 10%, and 15%, to diesel fuel under different engine load conditions. These conditions were represented by D100, DP20, DP20H5, DP20H10, and DP20H15. The following passage presents a concise overview of the ongoing research:  
* The maximum brake thermal efficiency (BTE) observed for a DP20H15 mixture containing 15% hydrogen with 85% DP20 is 37.0%, while the lowest BTE recorded for D100 fuel is 30.75%. This indicates that the BTE has seen a 16.8% increase. The improved performance of the engine may be attributed to the reduced stiffness and density of the hydrogen sample, which facilitated the efficient fragmentation of hydrogen into smaller particles. Additionally, DP20H5 displayed a reduction in fuel consumption of 10.9% at 23.5 \(\mathrm{b}^{\circ}\) TDC at full load conditions.
* According to the available information, it has been reported that the lowest specific fuel consumption (SFC) for a mix of DP20 with 15% hydrogen is estimated to be 142.0 g/kWh. In contrast, the maximum SFC recorded for D100 fuel is reported to be 275.5 g/kWh.
* The increase in pressure inside the cylinder may be attributed to the change in temperature, which subsequently affected the quantity of hydrogen present in the mixture. At its maximum operational threshold, which reached around 6.5% for DP20H5, 17.6% for DP20H10, and 19.7% for DP20H15, the fuel mix consisting of waste-plastic oil biofuel-diesel-hydrogen exhibited the greatest pressure. This study examines the impact of the addition of hydrogen on the alteration of cylinder pressure throughout the combustion process, in comparison to the use of D100 fuel.
* The introduction of hydrogen into diesel fuel shows a potential improvement in the levels of nitrogen oxide (NOx) emissions for DP20H5, DP20H10, and DP20H15 while operating at full load settings.
* A decrease in HC and CO emissions was seen in response to the rise in cylinder temperature, concomitant with an increase in the hydrogen content within the mixture of up to 23.5 \(\mathrm{b}\) TDC injection timing. Furthermore, it was observed that the emission of HC emissions increased proportionally with the increased advanced fuel injection timing.  
Conceptualization, methodology, and writing and investigation, K.J.R.; resources, and supervision G.A.P.R.; data curation, R.M.R.; writing--review and editing, U.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Thanks to National Institute of Technology, Warangal, and G. Pulla Reddy Engineering College, Kumool for proving space for this work.  
The authors declare no conflicts of interest.
# An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning
## 6 Experimental Assessment
### Experimental Results Analysis
#### 6.5.2 Experimental Analysis of Ablation  
The study carried out ablation experiments on the LIC2019 test set to evaluate the efficacy of various components and layers, as illustrated in Table 8. To extract semantic feature information, a dilation gate attention network was employed, increasing the interaction between tokens through multiple channels, thereby leading to a significant performance enhancement compared to models that lack this module. Moreover, the inclusion of only  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Ternary Relation Extraction**} \\ \cline{3-5}  & & **P\%** & **R\%** & **F1\%** \\ \hline \multirow{4}{*}{LIC2019} & CasRel & 80.75 & 79.77 & 82.15 \\  & TPLinker & 79.34 & 78.27 & 80.43 \\  & TPLinkerPlus & 80.89 & 79.38 & 81.31 \\  & GPLinker & 81.75 & 79.96 & 82.58 \\  & Ro-DGANet & 80.83 & 81.60 & 82.99 \\ \hline \multirow{4}{*}{CHIP2020} & CasRel & 64.10 & 58.28 & 62.86 \\  & TPLinker & 64.76 & 63.81 & 63.72 \\ \cline{1-1}  & TPLinkerPlus & 65.90 & 64.11 & 65.32 \\ \cline{1-1}  & GPLinker & 65.59 & 60.97 & 63.66 \\ \cline{1-1}  & Ro-DGANet & 65.74 & 66.37 & 66.39 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of different models on LIC2019, CHIP2020 datasets.  
Figure 7: Comparison of metrics of different models on the LIC2019 dataset. (**a**) Changes in the indicators of the _p_-value; (**b**) Changes in the indicators of the R-value; (**c**) Changes in the indicators of the F1 score.  
lexical encoding in the Postag component resulted in a slight improvement in the model's performance. Similarly, the GP component introduced perturbations to the Embedding layer, which is to some extent equivalent to adding \"gradient perturbations\" to the loss and also slightly improved the model's performance. On the other hand, the Base component does not introduce any functional modules. The results in Table 8 indicate that the use of the DGANet, Postag, and GP components aids in enhancing the joint extraction of triple relation. Furthermore, it is evident that the DGANet layer exerts a more significant impact on the extraction process as the majority of the relation information is embedded in the content between the two entities.
# Lightweight Domestic Pig Behavior Detection Based on YOLOv8
## 4 Experiments and Analysis
### Model Generalization Test  
To further evaluate the advancement of the proposed algorithm in pig behavior recognition, the publicly available Comp dataset was used for additional experiments. This dataset extends the behavior categories of the current study by including the Drinking class, thereby covering more key pig behaviors and validating the generalization capability of the algorithm. The experimental results are presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **P \%** & **R \%** & **mAP@50** & **Params/M** & **GFLOPs** & **FPS** \\ \hline YOLOv8n & 96.0 & 95.5 & 98.6 & 3.01 & 8.1 & 46.45 \\ +C2f-DualDW & 96.2 & 96.5 & 98.5 & 2.21 & 6.1 & 58.8 \\ +GSConvSE & 95.9 & 96.2 & 98.6 & 1.79 & 5.2 & 59.14 \\ +BiNeck & 96.3 & 95.2 & 98.5 & 1.21 & 4.9 & 61.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Precision (\%)** & **Recall (\%)** & **mAP@50 (\%)** & **Params (M)** & **GFLOPs** \\ \hline YOLOv8n & 96.06 \(\pm\) 0.30 & 94.60 \(\pm\) 0.34 & 98.60 \(\pm\) 0.30 & 3.01 & 8.1 \\ YOLOv8-PigLite & 96.38 \(\pm\) 0.19 & 94.38 \(\pm\) 0.33 & 98.66 \(\pm\) 0.13 & 1.21 & 4.9 \\ \(p\) & 0.0054 & 0.0402 & 0.6974 & - & - \\ \hline \hline \end{tabular} \({}^{a}\)-\({}^{a}\) indicates “no data”.  
\end{table}
Table 3: \(t\)-test analysis of YOLOv8n vs. YOLOv8-PigLite performance on key metrics.  
This paper's approach, YOLOv8-PigLite, demonstrates superior performance metrics compared to the benchmark model, YOLOv8n, on the Comp dataset, even after undergoing lightweighting. Specifically, the precision (P) on the PBRD dataset exhibits a modest enhancement, although the recall (R) and mean average precision at 50 (mAP@50) remain rather stable; on the Comp dataset, the metrics for YOLOv8-PigLite are equivalent to those of YOLOv8n. The experimental findings conclusively demonstrate the superiority of this paper's algorithm in domestic pig behavior recognition, encompassing a broader range of key behaviors while maintaining or enhancing performance and ensuring a lightweight model. This offers advantageous support for achieving efficient and precise identification of domestic pig behavior in practical applications.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 3 Risk Analysis of Crowd Gathering on Slope in Urban Street Spaces
### Analysis of Factors Affecting Pedestrian Movement Speed
#### 3.3.3 Effect of Environmental Factors on the Speed of Pedestrian Movement  
Factors such as poor lighting conditions and unfavorable weather conditions (e.g., rain, fog) are contributing factors to poor visibility for pedestrians. Pedestrians' sensory sensitivity, psychological tension response, and alertness are heightened in dimly lit environments [27], which can have a significant impact on their speed of movement. Road conditions indirectly affect pedestrian speed by changing the difficulty of path travel, while obstacles indirectly affect pedestrian speed by changing path length or increasing avoidance frequency. Therefore, Equation (2) was further modified as follows:  
\[v^{\prime}=v_{\text{max}}\cdot v_{f(\rho)}\cdot v_{ft}\cdot\sigma_{s}\cdot \sigma_{p} \tag{5}\]  
In Equation (5), \(\sigma_{s}\) is the sensory response factor, which combines factors such as weather, light, and psychological tension, usually ranging from 0 to 1, depending on the strength of the effect of the environmental stimulus on the pedestrian's psychology. \(\sigma_{p}\) is the path efficiency factor, which takes into account the effects of obstacles and pavement conditions.  
\[\sigma_{p}=\frac{d_{ideal}}{d_{actual}} \tag{6}\]  
\(d_{\text{ideal}}\) is the distance of the ideal path, m; \(d_{\text{actual}}\) is the actual distance under the influence of road conditions and obstacles, m.  
In summary, the actual movement speed of pedestrians changes to different degrees under the influence of different crowd densities, different terrain features, and environmental factors, as shown in Table 1.
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 2 State of the Art
### Internet of Things
#### 2.1.3 IoT Communication Technologies  
Several communication technologies enable IoT objects interconnection. A specific communication technology is used in conformance with the IoT objects characteristics (limited energy consumption and CPU utilization) and applications requirements. IoT communication technologies correspond to an adaptation of an existing technology or a new specifically specified technology. We present in Figure 1 a non-exhaustive classification of IoT communication technologies into wireless cellular technologies (LTE, 4 G, NB-IoT, 5 G, etc.) [13] and wireless non-cellular technologies (IEEE802.15.4 [14], LoRaWAN [15], ZigBee [16], 6 LoWPAN [17], etc.).  
In the e-health application domain, different research works use the ZigBee communication technology to interconnect different IoT objects. In [18], a wireless healthcare monitoring system based on ZigBee is specified in order to provide real-time information concerning the health condition of patients. Furthermore, the authors in [19] developed a continuous patients monitoring system based on ZigBee protocol. In fact, ZigBee, 6 LoWPAN and other communication technologies use IEEE 802.15.4 as a basis for their lower layers (physical and medium access control layer). The expanding number of connected devices in the IoT makes the IPv6 usage a necessity. Consequently, 6 LoWPAN [17], based on IEEE 802.15.4 for the physical (PHY) and medium access control (MAC) layers, will be greatly used in the IoT environment for services requiring low data rate and low range coverage. Thus, several IoT real-time services can use IEEE 802.15.4 based communication technologies, like patient vital signal monitoring and emergency situations monitoring (i.e., fire alarms, intrusion detection alarms, etc.). In addition, other non-real-time services, such as the environment monitoring services (humidity monitoring, temperature monitoring, etc.), are also candidates for the usage of IEEE 802.15.4 based communication technologies. Therefore, we describe in Section 2.2 the most important characteristics of this standard that we adapt to design a QoS based access method called QBAIoT.  
Figure 1: IoT communication technologies.
## 3 The PSOTSC Algorithm
### Results of Algorithm Comparison  
Compared with DP, TRACLUS and TCSS typical segmentation and compression algorithms, the advantage of the PSOTSC algorithm is verified on real trajectory data set and maneuvering target simulation data set. The experimental results are shown in Figure 11. In the experiment of real trajectory data set, the number of initialization particles \(N_{P}\) is 15 and the maximum number of iterations \(N_{I}\) is 5. The DP algorithm needs to adjust the distance threshold to obtain the optimal segmentation, and the threshold sampling point in the experiment is \(d_{DP}=[8,6,4,3,2,1.5,1,0.7,0.5,0.3]\). The segmentation algorithm in TRACLUS will be affected by trajectory scaling, and different scaling ratios will affect the segmentation accuracy. The scaling ratio in the experiment is \(r_{TRACLUS}=[1,2,3,5,10,15,20,25,30]\). The segmentation accuracy of the TCSS algorithm is affected by the angle threshold setting, and the angle threshold setting in the experiment is \(a_{TCSS}=[80,70,60,50,40,30,20,15,10]\). In the experiment of maneuvering target simulation data set, the parameters \(d_{DP}\), \(r_{TRACLUS}\) and \(a_{TCSS}\) still need to be adjusted.  
As shown in Figure 11, the segmentation accuracy compression ratio curve obtained by the PSOTSC method is at the bottom of all comparison method curves, which can not only show that the PSOTSC method can achieve the lowest compression ratio under the same segmentation accuracy level, but can also show that the PSOTSC method can obtain the highest segmentation accuracy under the same compression ratio requirements. Therefore, this method has advantages over typical methods in segmentation accuracy and compression ratio.  
Figure 10: Segmentation effect of maneuvering target simulation.
# Development of Direct-Vibration Actuator for
## 2 Analysis
### Electromagnetic Analysis  
Using the 3D finite element method by commercial software, the flux density on the coil is obtained. The governing equation describing flux density B is expressed as  
\[\text{B}=\
abla\times\text{A} \tag{1}\]  
where B is the magnetic flux density, \(\
abla\times\) is the vector curl operation, and A is the magnetic vector potential.  
The magnitude of the Lorentz force in the coil can be expressed as  
\[\text{F}=\oint\text{Id}l\times\text{B} \tag{2}\]  
where I is the current in the coil, and \(l\) is the total coil length.  
The force factor is used to present the performance of the actuator. The force factor can be calculated by dividing the Lorentz force by the current or multiplying flux density of the coil by the total coil length. This is written as  
\[\text{Force factor}=\text{B}l\text{ [N/A]} \tag{3}\]  
Figure 4: Structure of direct-vibration actuator.  
Given the same input current, a higher force factor indicates a larger force generated in the coil. The force factor is related to the total force, acceleration, and SPL. When the force factor increases, the SPL increases.  
The material properties needed in electromagnetic analysis are listed in Table 1. The material of the top plate and yoke is Steel Plate Cold Commercial (SPCC), which has a nonlinear relationship between B and H. The relationship between B and H can be expressed as  
\[\text{B}=\text{\mu(H)H} \tag{4}\]  
where H is the magnetic field intensity. B and H correlate to the nonlinear value, which is called the permeability. On the B-H curve, the permeability can be obtained by calculating the slope, which varies. The B-H curve of SPCC is shown in Figure 5. The type of mesh, element size, and element number for FEM analysis are detailed in Table 2. The tetrahedral and hexahedral mesh is used depending on the shape of each structure. A finer mesh is used in the coil, and the air region is created around the actuator.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Part** & **Material** & **Property** & **Value** & **Unit** \\ \hline Air & - & Permeability & \(4\pi\times 10^{-7}\) & H/m \\ Magnet(N48H) & NdFeB & Remanence & 1.4 & T \\ Top Plate & SPCC & Permeability & B-H curve & H/m \\ Yoke & SPCC & Permeability & B-H curve & H/m \\ Coil & Copper & Permeability & \(4\pi\times 10^{-7}\) & H/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties for electromagnetic analysis.  
Figure 5: B-H curve of SPCC.