esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 V. Test Framework
### Experimental Results
#### 5.2.2 Case (2) DDS Publish-Subscribe over the Network Layer  
To measure the DDS delay, we had to measure the total end-to-end delay (Tt) by the proposed test framework, as shown in Figure 6. As mentioned before, this communication occurred by two GOOSE publish-subscribe actions plus a DDS data exchange. Therefore, by measuring the total end-to-end delay, the DDS delay time (TD) is calculated by (1).  
\[T_{D}=T_{t}-(2\times T_{G}) \tag{1}\]  
The average value of the total end-to-end delay (Tt) was obtained by recording a large number of samples (around 1k) as we did in Case 1. The latency of this case is changing between 1000 and 2700 \(\upmu\)s. The average and standard deviation are 2100 and 160 \(\upmu\)s, respectively. As a result, and as shown by (2), the DDS delay time is 1100 \(\upmu\)s. Table 1 summarizes the results for these two study cases.  
\[T_{D}=2100-(2\times 500)=1100\ \mu\text{s} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Study Case**} & \multicolumn{4}{c}{**Latency (\(\upmu\)s)**} \\ \cline{2-5}  & **Min.** & **Max.** & **Ave.** & **S.D.** \\ \hline
1 & 300 & 1300 & 500 & 50 \\ \hline
2 & 1000 & 2700 & 2100 & 160 \\ \hline \end{tabular}
\end{table}
Table 1: Results from Case 1 and Case 2.  
Figure 5: Results from Case 1.  
The test framework does not consider the latency of the DDS routing service and network traffic. However, experimental results report an average of around 70 \(\upmu\)s for the DDS routing service action (TR). Hence, the average latency due to network traffic (TN) should not be more than 7830 \(\upmu\)s, as calculated by (3).  
\[T_{N}=T_{Max}-(2\times T_{G}+T_{D}+T_{R}) \tag{3}\]  
In this equation, T_Max is the maximum allowed time delay for R-GOOSE messages, which is 10 ms, based on IEC 61850 standard. The average time delay for each section of proposed communication framework is reported in Table 2. These calculations mean that the proposed framework is fast enough and only needs around 2170 \(\upmu\)s to encapsulate a GOOSE message and transfer it by the DDS and give us 7830 \(\upmu\)s for communication over the WAN, which is feasible by defining a tunnel between two communicating networks along with a proper message priority policy. Generally, the network latency is highly dependent on the size of the message, network bandwidth, and performance (unicast/multicast features). As reported in [31], for a 32 bytes message size with the rate of 1 k message per second, the best effort of the DDS QoS profile resulted in 269 \(\upmu\)s latency for the communication system. It means that using a proper QoS profile and tunneling technique, the R-GOOSE communication would be feasible over the maximum time span (7830 \(\upmu\)s) available when the proposed framework is utilized.  
Figure 6: Results from Case 2.
## 5 Discussion
### Applications of EFSDAF to other Remote Sensing Products and Sensors  
The EFSDAF in this study was tested using surface reflectance values of MODIS and Landsat images, but the purpose of EFSDAF is to study various complex surface dynamics. Therefore, EFSDAF can be applied to vegetation monitoring (e.g., NDVI), crop growth, and other sudden events such as urbanization, forest fires, and landslides. In addition, with the frequent launch of satellites for the Earth Observation System in recent years, existing satellite sensors such as Sentinel 2 MSI (10, 20, 60 m) images can replace Landsat images as fine resolution images, and like Sentinel 3 OLCI (300 m), NPP-VIIRS images (375/750 m) can replace MODIS images as coarse resolution images. We are looking forward to testing EFSDAF for other applications as well as satellite sensors.
## 4 Examples
### Static and Dynamic Response of a Beam with Elastic Ends  
The schematic configuration of a beam with elastic ends is displayed in Figure 7. The properties of the beam are listed as follow: cross section \(A\) = 25.4 mm \(\times\) 3.175 mm, length \(L\) = 508 mm, and elastic modulus \(E\) = 207 GPa. A gradually increasing concentrated load \(P\) is applied at the mid-span point. [PERSON] and [PERSON] [31] as well as [PERSON] and [PERSON] [32] analyzed the static and dynamic response of the beam with fixed ends. The static and dynamic response of the beam with semi-rigid ends was investigated by [PERSON] and [PERSON] [13] and [PERSON] et al. [20] using the conventional FEM and the VFIFE, respectively. The rotation-resistant stiffness \(k\) of the semi-rigid ends is \(E\)/\(L\).  
In this study, the beam was represented by 51 particles (i.e., 50 contact elements). The ends were modeled as rigid and semi-rigid in the rotational direction, respectively. The semi-rigid behavior was simulated by the contact elements at the ends with the stiffness in the rotational direction of 49\(E\)/\(L\) which was obtained according to Table 1. Figure 7 shows that the load-displacement curves of the modified MDEM are consistent with the published results ([PERSON] and [PERSON] [29]; [PERSON]  
Figure 6: Load-displacement curves at the free end of the column.  
and [PERSON] [13]; [PERSON] et al. [21]); the stiffness of the ends has a significant effect on structural stiffness, and the stiffness of the beam with semi-rigid ends is obviously reduced. In addition, the stiffness of the beam increases with an increase of the load, the reason is that the tensile reaction at the supports induces the axial tension arising in the beam.  
The dynamic response analysis of the same beam was further performed. A sudden load was applied at the mid-span point, that is, the load \(p\) was linearly increased to \(p_{\mathrm{max}}\) (640 lb) within a very short time (10 \(\upmu\)s), and then the \(p_{\mathrm{max}}\) is maintained until the calculation ended (\(t=5\) s), as shown in Figure 8. During the analysis, the material was assumed to be perfectly elastic, and the viscous damping was ignored. It can be found from Figure 8 that the analysis results of the modified MDEM is identical with published results, and the minor error is due to the inevitable numerical method itself. Moreover, the stiffness of the ends affects significantly on the period and amplitude of displacement time history curves. The structural stiffness decreases with semi-rigid supports, whereas both the period and amplitude increase accordingly.  
Figure 8: Displacement time history curves at the mid-span point of the beam under sudden loading.  
Figure 7: Displacement at the mid-span point of the beam under static loading.
## 5 Conclusions  
The identification of some dependencies, in analytical forms, between the sizes of interest and the frequency and level of the voltage harmonics, has the role of allowing us to estimate the harmonics consequences in the case of other applications, in which the harmonic composition of the stator voltage wave is known. The similarity of the voltage harmonics consequences, regardless of the type of their sequence, is one of the important observations based on the data obtained.  
From an electric power point of view, the general conclusion is that the harmonics are felt both at the level of the IM, through the variations of the PF and the efficiency; as well as at the level of the power supply system, through the current harmonics introduced to it and by the decrease of the PF. In addition, the electromagnetic torque harmonics can affect the behavior, reliability, and service life of some parts of the IM mechanical system, as well as of the ones belonging to the driven technological equipment.  
Figure 8: Variations of efficiency versus the harmonics order and their level, \(\eta\)(k,\(\gamma_{\rm Vk}\)).  
The proposed methodology for analyzing the effects of the harmonics on IM could be extended by the following:  
* Deformation of the voltage waves applied to the stator, by overlaying a single harmonic, with different orders and percentage levels, so that the total RMS value of the voltage is less than or equal to the rated value.
* Introduction of two or even more harmonics into the harmonic content of the voltage wave simultaneously.
* The study of the IM behavior in harmonics at different percentage loads such as 25%, 50%, and 75%.
* Adaptation of the current physical model of the IM to track the effects of saturation on the current harmonics developed in the motor. The present study is a reference to assess the consequences of saturation.  
Conceptualization, H.G.B, V.M., and I.B.; methodology, H.G.B., V.M., and S.G.P.; software, H.G.B., C.S.P., and P.C.D.; validation, H.G.B., C.S.P., and P.C.D.; formal analysis, H.G.B., S.G.P., and I.B.; writing--original draft preparation, H.G.B. and V.M; writing--review and editing, H.G.B. and S.G.P.; visualization, H.G.B. and S.G.P.; supervision, V.M. and I.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The results presented in this paper were obtained in the framework of the GNaC 2018 ARUT grant \"Consequences of Power Quality Issues for Electromechanical Receivers\", research Contract no. 3092/05.02.2019, with the financial support of the Technical University of Cluj-Napoca.  
The authors declare no conflict of interest.
## 3 Results
### Association of PDC with Skeletal Anomalies  
From an occlusal standpoint, no significant associations were found between PDC and posterior crossbite (69.1% did not present this condition) or with dental midline deviation (60.3% showed no deviation). Similarly, no association was observed with skeletal class, where the distribution was similar across the three groups (Class I--26.47%; Class II--36.76%; Class III--36.76%).  
However, a significant relationship was identified between PDC and normodivergent facial growth patterns, with a lower proportion of hyperdivergent patterns (chi-square = 6.74, \(p<0.05\)). Normodivergent growth patterns were the most common (30 cases, 44.1%), followed by hypodivergent patterns (25 cases, 36.8%) and hyperdivergent patterns (13 cases, 19.1%).
# Hybrid Model-Based Analysis of Underground Articulated Vehicles Steering Characteristics
###### Abstract  
Owing to the harsh environment of underground mines, autonomous underground articulated vehicles (UAVs) with precise control and positioning system are particularly important. However, the ambiguity of steering characteristics hinders the development of UAVs. This study presents a model-based method to uncover the steering characteristics of a UAV. Firstly, a hybrid model of UAV was established, which included a dynamic model of articulated frames and a model of the hydraulic power steering system. Secondly, a field test of a typical UAV, a load-haul-dump (LHD) with 4 m\({}^{3}\) capacity, was carried out. In order to verify the correctness of the established model and the accuracy of the involved parameters, the field test results were used to verify the dynamic model in time and frequency domains. Then, the steering characteristics of the UAV were uncovered based on the verified hybrid model, and the results showed that the increased load would increase 'oversteering' under the same articulation angle and that the error of trajectory exceeded 0.3 m. In addition, the deviations of trajectories between the two frames were revealed during the transient steering process, and the maximum deviation reached 0.21 m when the velocity was 2 m/s and the articulation angle was 15\({}^{\circ}\). The comprehensive results indicate that the steering characteristics of UAVs cannot be ignored in regard to precise autonomous control and positioning.  
underground articulated; hydraulic steering system; steering characteristics; dynamic model +
Footnote †: journal: Applied Sciences
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Dataset Description
#### 3.1.4 Houston 2018  
The 2018 IEEE GRSS Data Fusion Contest (DFC) triggered public dissemination of this rich dataset, which was included in our tests to increase their statistical significance. The image of the Houston campus and its surrounding area was captured by the IRTES CASI-1500 sensor at a GSD of 1 m over Houston, Texas, USA. It has 601 \(\times\) 2384 pixels and 50 spectral bands with wavelengths ranging from 380 to 1050 nm sampled at 10 nm intervals. The scene contains 20 urban landcover classes. The false-color composite image, ground truth image, and class names for the Houston 2018 dataset are provided in Figure 8.
# _M\({}^{2}\)Tames_: Interaction and Semantic Context Enhanced Pedestrian Trajectory Prediction
## 3 Proposed Approach
### Loss Function  
This study designs a loss function inspired by Y-Net [30]. Specifically, \(M^{2}\)_Tames_ is designed as an end-to-end joint training method, which comprehensively considers two core tasks: predicting the distributions of agents' trajectories and their goals. By introducing weight factors, a weighted combination strategy of binary cross-entropy loss can balance the importance of different tasks. The design of this loss function enables \(M^{2}\)_Tames_ to effectively balance the contributions of multiple tasks during the learning process and to more comprehensively understand the complexities of pedestrian trajectory prediction. By integrating considerations for both the predictive goal and trajectory distribution, \(M^{2}\)_Tames_ can flexibly adapt to various task requirements, providing robust support for enhancing the accuracy of future pedestrian trajectory predictions. The end-to-end joint training and multi-task learning strategy offer significant advantages for \(M^{2}\)_[PERSON]_'s performance in long-term pedestrian trajectory prediction tasks.  
The goal loss, denoted as \(\mathcal{L}_{goal}\), is measured using Binary Cross-Entropy (\(BCE\)) to quantify the difference between the predicted goal point distribution \(\hat{\mathcal{P}}(\mathcal{Y}_{i}^{Tpred^{\prime}})\) and the actual target point distribution \(\mathcal{P}(\mathcal{Y}_{i}^{Tpred})\). The trajectory loss \(\mathcal{L}_{traj}\) calculates the binary cross-entropy loss between the predicted distribution and the actual distribution for all points from \(t=T_{obs}+1\) to \(t=T_{pred}\). The total loss \(\mathcal{L}\) is the weighted sum of these two losses. The importance of trajectory loss within the total loss is adjusted by the weight parameter \(\lambda\). This paper utilizes the Adam optimization method to dynamically optimize the value of \(\lambda\), achieving a more precise control of loss weights. The specific formulas are defined as follows:  
\[\mathcal{L}_{goal}=\frac{1}{N}\sum_{i=1}^{N}BCE(\mathcal{P}(\mathcal{Y}_{i}^{T pred}),\hat{\mathcal{P}}(\mathcal{Y}_{i}^{Tpred^{\prime}})) \tag{20}\]  
\[\mathcal{L}_{traj}=\frac{\sum_{i=1}^{N}\sum_{t=T_{obs}+1}^{Tpred}BCE(\mathcal{ P}(\mathcal{Y}_{i}^{t}),\hat{\mathcal{P}}(\mathcal{Y}_{i}^{t^{\prime}}))}{N \times(T_{pred}-T_{obs})} \tag{21}\]  
\[\mathcal{L}=\mathcal{L}_{goal}+\lambda\mathcal{L}_{traj} \tag{22}\]  
where \(N\) represents the number of agents. \(\mathcal{P}\) represents the true distribution. \(\hat{\mathcal{P}}\) represents the distribution predicted by the model. \(Y_{i}^{Tpred}\) represents the true target points. \({Y_{i}^{Tpred}}^{\prime}\) represents the predicted target points.
## 4 Results and Discussion
### Power Consumption and Peak Reduction  
The MC-LSTM model was integrated into the real-time monitoring system at Daekyung Engineering Headquarters to evaluate its effectiveness in reducing reliance on centralized power supplies and mitigating peak loads. The system was actively tested from 1 June 2024 to 30 September 2024, with real-time validation of its performance.  
The peak shaving strategy was designed with a user-defined threshold of 2.5 kWh. If the predicted power consumption exceeded this threshold for the upcoming 15 min interval, the external energy sources, such as R-ESSs and V2G, were automatically discharged to reduce peak demand.  
To evaluate its performance, validation focused on five consecutive working days, from 8 July to 12 July 2024, during which an external verification agency (Korea Institute of Green Climate Technology) supervised the process. The results, presented in Figure 7, show the actual and optimized power consumption during this period. The red dashed line represents the peak demand threshold, the yellow solid line shows the total power consumption before peak shaving, and the blue solid line illustrates the optimized power consumption after peak shaving. The green-shaded area indicates the power discharged from the external energy sources to reduce peak loads. A total of 246 kW was reduced from an initial 1155 kW, resulting in an energy saving of approximately 21.3%.  
The impact of the MC-LSTM-based peak shaving system on electricity cost reduction was also assessed by comparing monthly electricity costs before and after its implementation. The results showed that the system effectively reduced electricity costs, achieving an 11% reduction in June, 17% in July, 19% in August, and 8% in September 2024. These reductions demonstrate the effectiveness of the proposed system in enhancing energy efficiency and lowering operational costs by mitigating peak demand.  
Overall, the experimental results demonstrate that the MC-LSTM-based power consumption prediction system successfully identified real-time consumption patterns and effectively integrated external energy sources such as the R-ESS and V2G charger. The system achieved significant reductions in peak consumption and total power usage, highlighting its capability to maintain a reliable power supply while enhancing grid stability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Metric**} & \multicolumn{1}{c}{**LSTM**} & \multicolumn{1}{c}{**MC-LSTM**} \\ \hline \multirow{3}{*}{Accuracy} & Average & 91.20\% & 97.93\% \\ \cline{2-4}  & Maximum & 92.42\% & 99.24\% \\ \cline{2-4}  & Minimum & 82.03\% & 84.51\% \\ \hline \multicolumn{1}{c}{Computational Complexity} & 10.56 kFLOPs & 10.56 kFLOPs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of prediction accuracy and computational complexity between the single LSTM and MC-LSTM.
## 3 Operating Principle and Performance of DCM-CRM Cuk PFC Converter Utilizing VI Control
### Input Current and Peak Current of Input Inductor Analysis  
According to (7) and (24), the input current for the proposed converter can be expressed as  
\[i_{in\_VI}(t)=\frac{V_{\text{M}}t_{\text{on}}{}^{2}|\text{sin}(\omega t)|}{2T_{ \text{s}}L_{initial}} \tag{25}\]  
By substituting Equations (13) and (24) into Equation (5), the peak value of the input inductor current for the proposed converter can be derived as  
\[i_{LV\_\text{pk}}(t)=\frac{2I_{\text{O}}L_{2}V_{\text{M}}\Big{[}1-\frac{V_{ \text{M}}}{V_{\text{C1}}}|\text{sin}(\omega t)|\Big{]}\Big{|}|\text{sin}(\omega t )|}{L_{initial}(V_{\text{C1}}-V_{o})} \tag{26}\]  
According to (7) and (25), the theoretical waveforms of input current \(i_{in\_VI}(t)\) with variable inductor control and input current \(i_{in}(t)\) with conventional control are shown in Figure 9 for 110 V and 220 V input voltages. Figure 9 shows that the input current under conventional control is severely distorted, and the input current of the proposed converter with variable inductor control is a standard sinusoidal waveform. In theory, the proposed DCM-CRM Cuk PFC converter with variable inductor control can achieve unit PF.  
From Equations (5) and (26), the envelope curves of the peak input inductor current \(i_{LV\_pk}(t)\) with variable inductor control and peak input inductor current \(i_{LL\_\text{pk}}(t)\) with conventional control are shown in Figure 10 for a half-line cycle at 110 V and 220 V input voltages. As shown in Figure 10, the peak input inductor current of the proposed converter is significantly reduced, which is conducive to reducing the current stress on the switch and thus improving the efficiency of the converter.  
Figure 10: Envelope curves of input inductor peak currents \(i_{LL\_\text{pk}}(t)\) and \(i_{LV\_\text{pk}}(t)\) at 110 V and 220 V input voltages.  
Figure 9: Theoretical waveform of input current at 110 V and 220 V input voltage for both conventional and proposed converters.
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 4 Numerical Results  
### Exclusion Zone  
#### 4.1.1 The Exclusion Zone When Using Omni BS Antenna  
Herein, we investigate the exclusion zone with an omnidirectional BS antenna. In the RMa scenario, the x and y axes are the BS location coordinates, and the z axis is the I/N value, for which we consider the interference from a single BS. No BS can be deployed in the exclusion zone. The randomness of the LOS/NLOS path losses results in random behavior of the I/N value. For instance, the diagrams below show the case for RMa, where the BS location points at which I/N \(>-12.2\) dB are marked in red in Figure 10. The exclusion zone, in this case, is the area within the outline. Since the main lobe of the ES antenna faces true north, a higher gain results in higher interference from the BS. Hence, the exclusion zone has a larger coverage area on the north side of the ES. Owing to the randomness of the LOS/NLOS path losses and the I/N value in the UMa scenario also demonstrates random behavior; therefore, the points with \(-10\) dB I/N are no longer located on a perfect circle. The exclusion zone is the area where no \(-10\) dB points (marked in red) exist, as shown in Figure 11. For the UMi scenario, the exclusion zone is smaller than that of the UMa; this is because the antenna height difference between the BS and ES is larger. The larger antenna height difference, the longer the propagation distance, which causes higher path loss. This results in lower interference and a smaller exclusion zone, as shown in Figure 12.  
Figure 10: RMa scenario with ES antenna elevation angle of 5\({}^{\circ}\).