esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Result Analysis
### Crosswind Test
Similarly, the adjustment of the vehicle with CDC-ODDF is faster and more sensitive than that with CDC-LTR. In the second crosswind section (from the 3.5 th second to the 4.2 th second), the compensation of the left damping force with CDC-ODDF is also evident (especially on the left side of the rear axle). This effect is more prominent at the first instability period. At approximately the 5.28 th second, the right damping forces of the vehicle with CDC-ODDF has a compensation effect on the left side. This compensation effect eventually leads to the axle load transfer without exceeding the rollover threshold. Although the vehicle with CDC-ODDF still loses stability in the end, the smaller axle load transfer brings a better contact effect between the tires and the road surface. This result can also be seen from the comparison of vertical forces. Under the extreme instability condition at approximately the 3.35 th second, the CDC-ODDF control can ensure that the two tires of the vehicle touch the ground, while CDC-LTR, which only has the wheel on the right side of the rear axle, has vertical force. Although the effect of lateral force on the roll control is not evident (caused by sideslip), the CDC suspension still has great advantages compared with passive suspension. The strategy of CDC-ODDF is better than CDC-LTR. The optimal control strategy is adopted is not only to control the rollover, but also to change the distribution of damping forces and even the vertical forces of the vehicle. In addition, the redistribution of vertical forces can be combined with vehicle ESC control to achieve the goal of assisting ESC lateral stability control. Meanwhile, CDC plays an important role in driving stability with auxiliary effects. Figure 9: Roll angle rate, LTR, and lateral acceleration of crosswind test. The result of passive suspension is expressed by the black dotted line. The result of CDC-LTR is expressed by the blue chained line. The result of CDC-ODDF is expressed with the red solid line. (**a**) is the comparison result of the roll angle rate. (**b**) is the comparison result of LTR. (**c**) is the comparison result of lateral acceleration. To observe the comparison of the two CDC control strategies more conveniently, a local view, in which the passive suspension is removed, is added. Figure 10: Vertical and damping forces of each wheel in crosswind test with CDC-LTR and CDC-ODDF. The details can be seen in the legend. (**a**) is the comparison result of the vertical forces of wheels on the left. (**b**) is the comparison result of the vertical forces of wheels on the right. (**c**) is the comparison result of the damping forces of wheels on the left. (**d**) is the comparison result of the damping forces of wheels on the right. L is short for left, and R is short for right. 1 represents the front axle, and 2 denotes the rear axle.
## 8 Conclusions  
This paper presents the design, implementation, measurement, integration, and space qualification of an S-band antenna as part of the ground communication system of one of the three nanosatellites forming the ANSER constellation (INTA). The antenna is ultra-lightweight (30 g) and compact (6.7 mm in height), featuring dual circular polarization while achieving wideband AR, setting it apart from the existing literature. The antenna functions by being fed from a single point for both polarizations, thus eliminating the necessity for 0-90\({}^{\circ}\) phase-shifting networks. It achieves an 8% bandwidth in the S-band, encompassing both uplink and downlink frequencies. The technological challenges posed by the antenna's size, its wideband axial ratio, and the maintenance of stable radiation characteristics with minimal back radiation have been successfully overcome. The antenna has been qualified and integrated into Leader-S, which was launched aboard SpaceX's Falcon rocket in January 2025.  
Conceptualization, S.S.-S. and D.P.; methodology, S.S.-S. and D.P.; software, S.S.-S. and V.M.A.; validation, S.S.-S. and V.M.A., D.P. and A.S., formal analysis, S.S.-S. and D.P.; investigation, S.S.-S; resources, D.P.; data curation, S.S.-S.; writing--original draft preparation, S.S.-S.; writing--review and editing, J.L.M.-C., D.P. and J.A.R.; visualization, S.S.-S., J.L.M.-C. and D.P.; supervision, J.L.M.-C. and D.P.; project administration, D.P.; funding acquisition, J.L.M.-C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Spanish Government under grant TED2021-130650B-C21 (ANT4 CLIM) funded by MCIN/ AEI/10.13039/501100011033 (Agencia Estatal de Investigacion) and by UE (European Union) \"NextGenerationEU\"/PRTR.  
Not applicable.  
Data available statement:Data are contained within the article.
###### Abstract  
In this paper, we propose a high-altitude platform (HAP) and unmanned aerial vehicles (UAVs) collaboration framework in non-orthogonal multiple access (NOMA)-enabled Internet of Things (IoT) networks with the presence of an eavesdropping UAV. For the considered framework, we investigate the uplink secure transmission by optimizing channel allocation from UAVs to HAP, users' power, and UAVs' three-dimensional (3D) position. To solve this non-convex problem, we adopt the K-means cluster pair algorithm to divide paired users into different groups and each cluster can be served by a corresponding UAV. Then, the formulated optimization problem is decoupled into three subproblems and tackled iteratively based on the block coordinate descent (BCD) algorithm. Finally, simulation results verify that the proposed network architecture can achieve a higher secure rate, faster convergence evolution, and lower complexity in comparison with the current works.  
high-altitude platform (HAP); non-orthogonal multiple access (NOMA); Internet of Things (IoT); secure rate; unmanned aerial vehicles (UAVs) +
Footnote †: journal: Computer Science
# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
## 4 Experiment
### Results of the Data Synthesis Experiment  
Herein, data synthesis was conducted based on the previously proposed S2 GAN model. Further, 2000 training iterations with a batch size of eight were employed, and the Adam optimization function was used for the generator and discriminator models.  
Figure 12 illustrates the use of S2 GAN to synthesize skeleton data for typical static and dynamic behaviors, 'Standing on All Fours' and 'Sniffing', compared against real skeleton data. As shown in the resulting images, changes in behavior over time are distinctly represented for each specific action. Although the forms of the skeletons are not fully depicted, which might suggest difficulty in representing subtle movements, the joint coordinates reveal that while static behaviors show no change in position over time, dynamic behaviors exhibit some variations.  
Figure 11: Comparison of original data vs. preprocessed data distribution. (**a**) Represents the original data, and (**b**) represents the preprocessed data.  
Figure 12: Comparison between the synthesized results and the actual skeleton data for the behaviors ‘Standing on All Fours’ and ‘Sniffing’.  
Figure 10: Some examples of the skeleton extracted through YOLOv8x model.  
These results suggest that, although not perfect, the sensor data has the potential to replicate skeleton features. The arrangement of points at each time step indicates that the model is somewhat effective in recognizing the posture characteristics associated with each behavior. The potential for future improvements through more refined skeleton datasets, enhanced model architecture, and adjusted learning parameters is evident.
hyperlocal real-time data collection across cities, UAVs can precisely capture heat differentials, thermal leakage, and atmospheric changes [5]. UAVs also allow for the regular inspection of building insulation and the detection of weak links in building envelopes that exacerbate energy consumption [6; 7]. Additionally, the aerial mobility of drones facilitates cost-effective air-quality sampling at different altitudes and locations across metro areas [8]. Such granular quantitative data are pivotal for cities to diagnose climate vulnerabilities and layer risk profiles. UAVs have become especially valuable for climate change assessment and planning in dense urban areas, where their ability to collect nuanced data amidst complex built environments provides advantages over conventional UAVs and also allows cities to regularly monitor the efficiency of green infrastructure projects designed to mitigate climate change impacts through a detailed inspection and thermal imaging [9]. Moreover, the combination of rich UAV data-sets with artificial intelligence and machine learning algorithms offers additional capabilities for predictive climate change modeling and scenario planning in urban contexts [10]. By processing drone-captured imagery and readings using neural networks, the researchers can identify climate change-related patterns, generate simulations, and assess the efficacy of potential adaptation strategies [11]. As UAVs become more popular in environmental research, they are poised to provide the forensic-level insights cities need to respond to intensifying climate change impacts.  
This paper provides a comprehensive overview of drone technologies being leveraged in cities globally to assess and address climate change challenges. The paper details the types of sensing payloads and data analytics combined with drones to reveal the insights while examining the existing research where UAVs inform urban climate change strategies. Additionally, the regulatory and ethical considerations surrounding broader UAV deployments for environmental research are discussed. By highlighting UAVs and artificial intelligence (AI) as an emerging lynchpin in cities' climate change mitigation and adaptation efforts, this paper aims to spur further the innovations in using UAVs to create more sustainable and resilient urban futures.
## 4 Experiment  
### Overview  
As an evaluation of the developed system, we evaluated the users' impressions of it related to emotional expression and response delay. The purpose of this experiment is to clarify how the presence or absence of emotional expression and the length of response delay in the ECAs developed here affect users' impressions of the ECAs (e.g., satisfaction, responsiveness, interactivity, and information accuracy). As noted in Section 2, it is not clear how the response delay, which inevitably occurs in ECAs, interacts with the emotional expression of ECAs to alter various user impressions of ECAs. Therefore, in this experiment, we modified the emotional expression and response delay of ECAs to determine how they affect user impressions.  
Based on existing research that highlights the response timing in human-human interaction, this research seeks to extend the research to ECAs [20, 49, 50]. In the experiment proposed by [PERSON] et al. for automotive user interfaces [37], Satisfaction, Usability, and Context Awareness were evaluated depending on the response time of the user interface. This research aims to determine whether the agent response delay affects the user's evaluation of the agent. Their work showed that the user's evaluation declined with response delays exceeding 2 s or preemptive responses under 0 ms. Additionally, we aim to answer the research question of whether displaying emotions on the ECA's face changes the user's perception of the response delay.
## 3 Methodological Approach
### Evaluation Metrics
#### 3.2.1 Precision, Recall, and F1 Scores  
Precision and recall scores are the building blocks of several popular evaluation metrics, as they help to describe the classifier's performance in a broad perspective in terms of exactness and sensitivity, respectively. The F1 score, on the other hand, is a widely adopted evaluation metric for classification tasks, as it provides a descriptive identification of the classifier by combining both the precision and recall scores by computing their harmonic mean; that is to say, the F1 score expresses harmony, whereas unbalanced precision and recall scores are penalized. All scores described here take a value between 0 and 1, with 0 being the lowest and 1 being the highest. The precision, recall, F1 score, and accuracy are calculated as follows:  
\[Precision=\frac{True\ Positive\ (TP)}{True\ Positive+False\ Positive\ (FP)} \tag{2}\]  
\[Recall=\frac{True\ Positive}{True\ Positive+False\ Negative\ (FN)} \tag{3}\]  
\[F_{1}=2\ \times\ \frac{Precision\ x\ Recall}{Precision+Recall} \tag{4}\]  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP} \tag{5}\]
# An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology
## 4 Process Scheme and Measurement
### Experimental Validation  
Due to uncontrollable factors of the preparation platform and the preparation cycle, only the prototype without the switch is presented in this paper, as shown in Figure 12a. Figure 12b shows the diagram of the field test. The main instruments used were a vector network analyzer (the frequency range of this vector network analyzer is DC-67 GHz) and an RF line. Figure 13 shows the comparison of parameters between the simulation and measurement of the no-switch condition. As shown in Figure 13a, the test result of S11 is shifted 1.63 GHz to the left of the simulation result, and the \(-10\) dB operating bandwidth is 1.5 GHz. There are also additional resonance points in the 40 to 70 GHz range, including 44.9 GHz, 54 GHz, and 61.6 GHz. Figure 13b shows that the deviation of the VSWR is large in the range of 40 GHz to 50 GHz. The main reasons for the above deviations are errors in the preparation process, interference in the test conditions, and systematic errors. First, since the size of the antenna is in the order of millimeters, the solder joints at the feed ports will have some effect on the structure and flatness of the antenna radiating unit [24], as clearly shown in Figure 12a. This is the likely cause of the multiple resonance points and the leftward shift in frequency. The second reason is the limitation of experimental conditions; the measurements were not completed in the microwave darkroom, so the electromagnetic environment affected the antenna radiation [25]. In addition, bending and vibration of the test cable and torsion of the SMA connector increase the uncertainty of the test, resulting in systematic errors that affect measurement accuracy [26]. Overall, however, the errors in the key frequency range of 65-67 GHz are acceptable.  
Figure 11: Process program for L-slot frequency reconfigurable antenna.
## 5 Conclusions  
The main results and conclusions obtained in this study are summarized as follows:  
1. A 3D multi-physics coupled flow model is established for constructing multi-step horizontal salt caverns to simultaneously quantify the dissolution, cavity expansion, heat transfer, brine convection, turbulence flow, and diffusion effects. The results are consistent with field data of Vologgrad, verifying the accuracy of the proposed model.
2. The influence of heat transfer and turbulence flow is considered. The heat transfer accelerates the transportation and diffusion of brine. Temperature has a great influence on the dissolution rate of salt rock. The higher the temperature at the wall is, the faster  
Figure 34: Cavity’s final shapes in vertical section (X = 0 m) under different injection intervals.  
Figure 33: First stage cavern shapes in vertical section (X = 0 m) under different injection intervals.  
the dissolution rate is. The direction of forced thermal convection is perpendicular to the direction of fluid flow and consistent with the direction of the brine diffusion in the cavity.
3. The expansion of the new cavity is affected by both erosion and dissolution, and the expansion of old formed cavity is mainly affected by dissolution, and the expansion rate depends on the strength of the vortex. Forced convection has a significant effect on brine concentration distribution.
4. The previously formed cavity will continue to expand at a decreasing rate gradually as dissolution proceeds in the next stage. In the first stage, cavity expansion outward measures approximately 12 m, 7 m, 4 m, 2 m, and 0.5 m, respectively. The reflux cavities at the inlet section have the same shape. The solution cavity has the same height at 9 m at the last four stage.
5. Increasing the injection flow rate results in a larger cavern volume and efficiency. Increasing the injection concentration results in a smaller cavern volume and efficiency. The step distance hardly affects the cavern volume, but it does affect the cavern shape. A larger injection flow rate, a lower injection concentration, and a middle step distance are preferable in the field.  
Conceptualization, Z.W.; methodology, [PERSON] (jinchao Wang). and Q.Z.; software, [PERSON] (jinchao Wang); validation, [PERSON] (jinchao Wang); formal analysis, [PERSON] (jinchao Wang); investigation, [PERSON] (jinchao Wang), [PERSON] (jun Wang) and B.L.; resources, Z.W.; data curation, [PERSON] (jinchao Wang), [PERSON] (jun Wang) and B.L.; writing--original draft preparation, [PERSON] (jinchao Wang); writing--review and editing, Q.Z.; visualization, [PERSON] (jinchao Wang); supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. 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 due to privacy.  
The authors wish to acknowledge the financial support from the PetroChina Research Institute of Petroleum Exploration and Expansion (PIPED.CN-2f-2019-JS-359) and China Scholarship Council (CSC202306440111).
## 3 Materials and Experimental Methods
### CIP Simulation  
The CIP process simulation was conducted using ABAQUS 6.14-4 software (SIMULIA, Providence, RI, USA). The computations were executed on North-German Supercomputing Alliance hardware (HLRN, Zuse Institute, Berlin, Germany). The material was modeled utilizing the modified Drucker-Prager-Cap model. In the simulation, symmetry was considered, and due to geometric symmetry, only one-eighth of a cylindrical sample with a diameter and height of 12 mm and 3 mm was modeled, respectively. Moreover, three  
Figure 4: (**a**) Compressibility-relative density [%] over 100, 300, 500, and 700 [MPa] after CIP for M10S\(\mu\) and M10S\(n\) from Exp. and FEM outcomes. (**b**) The FEM predicted value of element volume EVOL [mm\({}^{3}\)] in the pressure certain domain for M10S\(\mu\) and M10S\(n\). (**c**) Volumetric plastic strain-Pressure (Hardening law) for M10S\(n\) and M10S\(\mu\) (**d**) FEM and Exp magnitude of e reduction in volume for M10S\(\mu\) and M10S\(n\) under 700 MPa CIP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Parameter**} \\ \cline{2-5}  & \(d_{t}\) & \(\beta\), & \(R\) & \\  & **(Cohesion of Material, MPa)** & **(Friction Angle, Degree)** & **(Cap Shape Parameter)** & **Hardening Law** \\ \hline
**M10S\(\mu\)** & 0.375 & 76.67 & 1.77 & \(p=30.071\)\(\epsilon\)\(14.508\)\(\epsilon_{\text{p}}^{p}\) \\
**M10S\(n\)** & 0.36 & 76.6 & 0.53 & \(p=33.434\)\(\epsilon\)\(14.508\)\(\epsilon_{\text{p}}^{p}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modified Drucker-Prager Cap model obtained parameters for M10S\(\mu\) and M10S\(n\).  
symmetry boundary conditions were applied along the X, Y, and Z planes. Furthermore, 5 \(\times\) 10\({}^{4}\) linear brick mesh elements, each consisting of 8 nodes, were utilized for Finite Element Method. A pressure of 700 MPa was applied to the free surface of 1/8 th of the FEM model.  
To compute the relative density after the CIP process from the simulated data, the hardening law equations were modified in the form of both the initial relative density (\(\rho_{0}\)) and the pressure at each step (\(p\)) and then incorporated into the output field of the ABAQUS software. The following formulas were entered for M10S\(\upmu\) and M10 Sn, respectively: \(\rho\) = \(\rho_{0}\) (0.33 \(P\))\({}^{0.069}\) and \(\rho\) = \(\rho_{0}\) (0.028 \(P\))\({}^{0.072}\). The initial relative density of the samples (\(\rho_{0}\)) in the formulas was equal to the after hand press relative density, which was 74.62% and 71.8%, respectively, for M10S\(\upmu\) and M10 Sn.  
To assess the homogeneity of density distribution and calculate the element volume (EVol), a Python script was employed. This script analyzed the samples after simulating the CIP process using ABAQUS CAE software. Furthermore, the volume reduction was determined by comparing the volume of samples before and after CIP, considering both experimental and computational results.
## 4 Analysis of Experimental Results
### Implementation of Rail Wear Calculation  
For the combination of algorithms proposed in this paper, the 4 PCS algorithm based on ISS feature points is used in the coarse alignment stage and the KD-ICP algorithm based on kd-tree accelerated search is used in the fine alignment stage. The combination of ISS-4 PCS + KD-ICP algorithms is used for the point cloud alignment of the 60 model rails and compared and analyzed with the original 4 PCS + ICP algorithms. The data obtained are shown in Tables 5 and 6, and the effect comparison is shown in Figure 11.  
As can be seen from Tables 5 and 6 and Figure 11, the ISS-4 PCS algorithm is significantly better than the original 4 PCS algorithm both in terms of running time and alignment accuracy, and the KD-ICP algorithm has a great improvement over the original ICP algorithm in terms of time. The total alignment time of the 4 PCS + ICP algorithm is 10.689 s and the alignment accuracy reaches 1.388 mm, while the total alignment time of the ISS-4 PCS + KD-ICP algorithm is 6.592 s and the alignment accuracy reaches 1.103 mm. Overall, the ISS-4 PCS + KD-ICP algorithm has an improvement of 38.33% in alignment time and 20.53% in alignment accuracy compared with the 4 PCS + ICP algorithm.  
In the calculation of wear on rails after fine alignment, as wear generally occurs in the inner working surface, rail section wear is mainly divided into horizontal wear \(\Delta\)M and vertical wear \(\Delta\)H, as shown in Figure 12, where the horizontal wear \(\Delta\)M measurement position is located on the working surface of the rail head from the standard tread surface down 16 mm a position, and the vertical wear \(\Delta\)H measurement position is located on the  
Figure 11: Comparison of rail alignment algorithms. **(a)** 4 PCS + ICP algorithm, where the white arrow represents the direction of the alignment, and in this direction follow the initial position of the source and target point clouds before alignment, the 4 PCS coarse alignment results, and the ICP fine alignment results; **(b)** ISS-4 PCS + KD-ICP algorithm, where the white arrow represents the direction of the alignment, and in this direction follow the initial position of the source and target point clouds before alignment, the ISS-4 PCS coarse alignment results, and the KD-ICP fine alignment results.  
working surface of the rail head from the width of the standard tread surface b position. The total wear W is half of the vertical wear \(\Delta\)H plus the horizontal wear \(\Delta\)M, i.e.:  
\[W=\Delta H+\frac{1}{2}\Delta M \tag{8}\]  
Finally, the deviation value between the source point cloud and the target point cloud after fine alignment can be combined with the wear calculation formula to derive the rail wear value.