esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Analysis
### Residual Deformation  
In the represented benchmark bridge package, there is no independent parameter indicating the residual deformations of different points of the bridge. However, this value can be extracted from the displacement history curve of each point. As can be seen in Figure 10a,b, for the SRB controlled bridge, the residual mid-span displacements in the X direction do not exist after the North Palm Springs earthquake, and the residual displacements in the Y direction decrease by 80%. The mid-span residual deformation under far-field and near-field earthquakes before and after applying the SRB in two directions, i.e., X and Y, are presented in Table 9 and Figure 11. As can be seen, the bridge with optimized SRBs is capable of reducing the residual deformations significantly.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Far-Field**} & \multicolumn{3}{c}{**Near-Field**} \\ \hline  & **NPalmspr** & **El Centro** & **ChiChi** & **Northridge** & **Turkey** & **Kobe** \\ \hline Res Def un.controlled X *(mm) & 42 & 110 & 160 & 110 & 290 & 130 \\ Res Def SRB controlled X ** (mm) & 0 & 0 & 106 & 50 & 65 & 60 \\ X Reduction & 100\% & 100\% & 33.7\% & 54\% & 77.6\% & 54\% \\ \hline Res Def un.controlled Y * (mm) & 55 & 125 & 110 & 50 & 100 & 130 \\ Res Def SRB controlled Y ** (mm) & 11 & 14 & 43 & 20 & 32 & 30 \\ Y Reduction & 80\% & 88.8\% & 60.9\% & 60\% & 68\% & 77\% \\ \hline \hline \end{tabular}  
* Residual mid-span deformation of uncontrolled benchmark bridge in the X/Y direction.  
\end{table}
Table 9: The mid-span residual deformation values before and after applying the SRB, in two directions, X and Y.  
Figure 10: Time history curve of the mid-span displacement under far-field North Palm Springs in the (**a**) X and (**b**) Y directions.
## 4 Real-Time Elbow and T-Junction Detection and Estimation (RT-ETDE) Framework
### Elbow and T-junction Detection Based on Outlier Extraction  
This section focuses on detecting elbows and T-junctions by analyzing outlier points that deviate from the straight pipe model within the transformed 3D point cloud in SCS. The elbow and T-junction introduce distinct patterns of outlier points, as shown in Figure 6. The elbow wall creates points in the inner and outer regions relative to the straight pipe model, which are called inner outlier points and outer outlier points, respectively. On the other hand, the branch of a T-junction only creates outer outlier points. These outlier patterns form the basis for detecting elbows and T-junctions.  
To detect elbows and T-junctions, we propose a solution that relies on a simple point cloud feature. Due to multipath effects and depth errors, some outlier points may be missing, while others may not correspond to elbows or T-junctions. To address these challenges, we analyze the point coverage of inner outlier points for elbow detection and outer outlier points for T-junction detection.  
We divide the point cloud into the concentric annuli in \(Y_{S}Z_{S}\)-plane with \(\delta r\) difference in radius, as shown in Figure 7. By checking the number of points in each annulus exceeding a threshold or not, we can decide if the annulus contains either the elbow or T-junction. Since the inner outlier points only exist in the elbow, we first check the inner annuli to detect the elbow. If the elbow does not exist, we check the outer annuli for T-junction detection. To determine the existence of an elbow or T-junction, the number of annuli containing them should be greater than a threshold. Once detected, the parameter estimation is triggered for the elbow and T-junction accordingly.
# MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph
###### Abstract  
With the rapid development of autonomous driving technology, multi-agent trajectory prediction has become the core foundation of autonomous driving algorithms. Efficiently and accurately predicting the future trajectories of multiple agents is key to evaluating the reliability and safety of autonomous driving vehicles. Recently, numerous studies have focused on capturing agent interactions in complex traffic scenarios. While most methods adopt agent-centric scene construction, they often rely on fixed scene sizes and incur significant computational overhead. Based on this, we propose the multimodal transformer graph convolution neural network (MTGNet) framework. The MTGNet framework can not only construct a panoramic, fully connected dynamic traffic map for agents but also dynamically adjust the size of traffic scenes. Additionally, it enables accurate and efficient multi-modal multi-agent trajectory prediction. In addition, we utilize the graph convolutional neural network (GCN) to process graph-structured data. This approach not only captures global relationships but also enhances the focus on local features within the scene, thereby improving the model's sensitivity to local information. Our framework was tested on the Argoverse 2.0 dataset and compared with nine state-of-the-art vehicle trajectory prediction methods, achieving the best performance across all three selected metrics.  
multimodal; multi-agent trajectory prediction; graph neural network; transformer
## 4 Case Study
### Calculation of Dry Beach Length  
Taking the satellite image of December 2023 as an example, the recognition result of dry beach area is shown in Figure 15a. Edge detection was performed on Figure 15a and the extracted contour is shown in Figure 15b. The beach crest line was extracted and fitted. The linear equation for the left beach crest line was Y1 = 1.06\(X-222.19\), and for the right beach crest line, it was Y2 = 0.24X + 242.83. Both lines are displayed in Figure 16a. The dry beach length at monitoring points A, B, and C was calculated. Using line Y1 as the \(X\)-axis, the pixel point distances from points A and B on the line Y1 to the boundary of the dry beach were calculated by Equation (11), as shown in Figure 16b. Similarly, the pixel distance from point C to the boundary of the dry beach was calculated by Equation (11), as shown in Figure 16c. Finally, the pixel distance was converted to the actual distance according to the scale.  
Figure 14: Identification results of dry beach area.
## 4 Discussion  
In various studies [6; 7; 8; 9; 10; 11], the importance of detecting the presence of the pest insects present in mirasol chili crops is detailed and described. They are virus vectors and cause direct damage to the plant by sucking the sap; they also feed on the phloem of the plants, leaves and tender shoots. They are very common throughout the world and damage a great variety of crops of fruit plants, vegetables and ornamental plants. They are extremely dangerous for agriculture because they can cause losses of 10 to 100% of crop production. They cause defoliation, yellowing or reduction of vigor. During the winter, they lodge in weeds, under leaves or grass near the cultivated fields, and in spring, they feed on the new crops that are available; this is why weed clearing before and during the start of a new crop cycle and crop monitoring with various resources such as yellow sticky traps must be performed to prevent the presence of these insects.  
This document presents a digital image processing method that involves two stages. In the first stage, the SIFT algorithm is applied to images of sticky yellow traps to extract the invariant characteristics of each insect contained in the trap, and to identify and differentiate the insect genera. For this stage, we showed that with the SIFT algorithm, different genera of insects can be identified with up to 94% detection of the specimens contained in the images.  
In the second stage, image correction is applied to transform the original image to grayscale, then to the CIELAB color space, and finally to extract the invariant characteristics of each insect, as shown in Table 4.  
Table 3 shows the mathematical representations of each feature according to the studied genus. For example, the solidity values of _Thrips tabacci_ and _Myzus persica_ are similar (8.84 \(\times\) 10\({}^{-1}\) and 8.82 \(\times\) 10\({}^{-1}\), respectively). However, without this, these two genera of insects can be differentiated by the other invariant features presented (eccentricity, the area in pixels, centroid, perimeter, size in square millimeters).  
To analyze the efficiency of the proposed method, the correlations by type of pest detected are shown in Figure 3. In the case of _Anthonomus eugenii_, a very low correlation of \(R^{2}=0.6908\) (Figure 3E) was observed in comparison with the other types of pests (Figure 3A-D,F), which may be due to the fact that the insect was found in smaller numbers during the experimentation period.  
These data indicate that the digital image processing method presented in this document can be applied to specify the invariant characteristics of different common insects in mirasol pepper crops.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 4 Discussion  
To verify the feasibility of the proposed synchronous rectification scheme for the CLLC resonant converter, an 800 W experimental prototype was constructed. The experimental setup is illustrated in Figure 13, with the topology identical to that shown in Figure 1. The parameters of the experimental prototype are detailed in Table 2.  
The switching signals for the proposed SR method were generated using a digital signal processor (DSP, TMS320F28335), which dynamically calculates the optimal turn  
Figure 11: Closed-loop control block diagram. Comparison of the calculated and simulated results of the delayed duty cycle of the SR gate drive signal in the above-resonance region for the proposed synchronous rectification scheme is plotted.  
Figure 12: Comparison of calculated and simulated SR gate drive signal duty cycle in the below-resonant region for the proposed synchronous rectification scheme.  
on/off timing based on real-time measurements of the input voltage, output voltage, and current.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 5 Proposed Method for Privacy Preservation in Healthcare Data
### Experimental Results on the COVID-19 Home Nursing Dataset
#### 5.5.2 Time Complexity Analysis with the Nursing Home COVID-19 Dataset  
In differential privacy, the time complexity primarily relates to the computational cost of executing queries on potentially large datasets while ensuring privacy guarantees. The first query involves filtering the dataset based on a single condition, while the second and third queries involve complex filtering conditions including logical AND and OR operations across multiple columns. It can be noticed that increasing the number of conditions in queries also increases the execution time for queries, as shown in Figure 25. The time increases from 0.06563 s to 0.35566 s, then to 7.34279 s due to the increase in conditions. A slight rise in execution time is typically incremental with each additional condition. However, the actual increase can also vary depending on the specific dataset characteristics (size, distribution, etc.) and the efficiency of the data processing system.  
For the previous \"Breast Cancer Prediction\", with around 30,000 records and 13 attributes, it can be noticed that even with more complex queries, the execution times remain relatively low compared to larger datasets. For another dataset, the \"Nursing Home COVID-19 Dataset\", with around 510,000 records and 39 attributes, execution times are slightly higher due to the sheer volume of data being processed.  
Figure 24: Analysis of the privacy parameter using the Nursing Home COVID-19 Dataset.  
Figure 25: Queries with time comparison using the Nursing Home COVID-19 Dataset.
## 3 Proposed Handover Scheme
### Overview of the Proposed Handover Scheme  
The proposed handover scheme comprises the following steps:  
* **Step 1** (Position tracking): The _position tracking_ (see Section 3.2) is initiated when the RSSI from the current AP falls below a certain threshold.
* **Step 2** (Position prediction): When the BSN user crosses the AP boundary, _position prediction_ (see Section 3.3) is made based on the movement trend extracted from position tracking.
* **Step 3** (Handover decision): The coordinator of the BSN executes the _handover decision_ (see Section 3.4) to determine the handover initiation time.
* **Step 4** (Handover initiation): Handover is performed at the handover initiation time when the actual position of the BSN is within an acceptable deviation range from the predicted position. Otherwise, handover is performed immediately.  
The overall flow chart of our proposed approach is shown as Figure 2.  
Figure 2: Flow chart of the proposed handover scheme.
## 2 Modeling of Hybrid-Propellant Launch Vehicle  
In this section, the subsystem models used in the optimization process are defined. The launch vehicle subsystems are coupled in a 6 DOF simulation. Additionally, the current study attempts to fill the gap present in the literature for optimization that incorporates 6 DOF integration and a model of subsystem couplings, including updates of subsystem parameters in each iteration during the optimization process. The proposed design and optimization study were applied to the Minotaur-I launch vehicle, and the results were obtained and evaluated. The Minotaur-I launch vehicle was chosen due to all its stages using a solid propellant. The Minotaur-I launch vehicle was also chosen due to the ease of modeling solid-propellant systems in simulation environments and the availability of reference mission data in the literature to compare with the optimization results. Minotaur-I, a member of the Minotaur rocket family produced by Orbital Sciences Corporation (now Northrop Grumman), is an American expendable launch system derived from the Minuteman II missile [36]. It is specifically designed for launching small satellites for the US Government. Minotaur-I stands at a height of 19.21 m and has a width of 1.67 m. Initially, launches of Minotaur-I were conducted from Space Launch Complex 8 at Vandenberg Air Force Base. The first successful flight carried The Joint Air Force-Weber State University Satellite (JAWSAT), an American military mini-satellite, on 27 January 2000, from Vandenberg Air Force Base. There has been a total of twelve successful launches of Minotaur-I. The launch vehicle consists of the M55A1 first stage and SR19 second stage, repurposed from a decommissioned Minuteman missile. Additionally, the third and fourth stages utilize components from the Pegasus rocket, specifically the Orion 50 XL and Orion 38. Minotaur-I has proven its reliability, with the most recent launch being the NROL111 national security space payload for the National Reconnaissance Office, which took place on 15 June 2021, from NASA's Wallops Flight Facility.
## 2 Materials and Methods
### Data Collection and Procedures
#### 2.2.1 Anthropometric Measurements and Indices  
Body height (H), and body weight (BW), waist (WC), hip (HC), mid-upper arm circumference (MUAC), and skinkoffs were measured using professional equipment and standardized procedures [22; 23]. Height was measured with a portable stadiometer with the head in the horizontal Frankfurt plane and recorded with a precision of 0.1 cm (SECA 220, Hamburg, Germany). Weight was measured using an electronic digital scale to the nearest 0.1 kg (SECA 799, Hamburg, Germany). Waist circumference (WC) was measured with a stretch-resistant tape that provides constant 100 g tension (SECA 201, Hamburg, Germany), at the midway point between the iliac crest and the costal margin (lower rib) on the anterior axillary line in a resting expiratory position [23; 24]. Hip circumference was measured around the widest part of the buttocks, with the tape parallel to the floor [23].  
On the non-dominant side of the body mid-upper arm circumference (MUAC) and skinkoffs were measured. MUAC was taken at the mid-point between the bony protrusion on the shoulder (acromion) and the point of the elbow (olecaron process), with the elbow to be flexed 90 degrees with palm facing upwards using a flexible tape, read to the nearest 0.1 cm [25].  
Participants' biceps, triceps, subscapular, and suprailiac skinkoffs were measured using skinkold calipers (Harpenden Skinfeld Caliper F0120, Baty International, Burgess Hill, UK) [26].  
All measurements were performed under strictly standardized conditions (room temperature 22 \({}^{\circ}\)C, air humidity 45%) by well-trained researchers, using the same device, in order to avoid inter-observer and inter-device variability. Measurements were taken twice, and if a difference existed between the first two measures of <5% for skinkoffs and <1% for all other measures, a third measure was taken, and the averages were calculated. The intra-tester technical errors of measurement (TEM) for skinkoffs was 3.4% and for all other measures was 0.5%.  
Based on anthropometric measures several anthropometric indices, most commonly found in screening studies were calculated:  
1. used for obesity diagnosis: * ABSI = WC (in m)/[BMI (in kg/m\({}^{2}\))\({}^{2/3}\)\(\times\) H (in m)\({}^{1/2}\)]; m\({}^{11/6}\) kg\({}^{-2/3}\) * AVI = [2 \(\times\) WC (in cm)\({}^{2}\) + 0.7 \(\times\) (WC (in cm) \(-\) HC (in cm)\({}^{2}\)]/1000; cm\({}^{2}\) * BAI = [HC (in cm)/H (in m)\({}^{1.5}\)] - 18; % * BMI = BW (in kg)/H (in m)\({}^{2}\); kg/m\({}^{2}\) * BRI = 364.2 \(-\) 365.5 [1 \(-\)\(\pi^{-2}\) WC (in m)\({}^{2}\) \(\times\) Height (in m)\({}^{-2}\)]\({}^{1/2}\); no units * RFM = 64 \(-\) [20 \(\times\) (H (in m)/WC (in m)) + 12 \(\times\) sex (0 for men, 1 for women); no units * WHR = WC (in cm)/HC (in cm); no units.
2. used as a marker of muscle mass [27; 28]: * Muscle Arm Circumference, MAC = (MUAC (in cm) \(-\)\(\pi\)\(\times\) triceps skinkoffs (in cm)); cm * Arm Muscle Area, AMA = [MUAC (in cm) \(-\) (\(\pi\)\(\times\) triceps skinkoffs (in cm)\({}^{2}\))/4 \(\pi\)]; cm * MUAC/H = MUAC (in cm)/H (in cm); no units.
## 3 State Estimation and Prediction
### Trajectory Prediction and Risk Assessment  
The future trajectory of the remote target in the vehicle surroundings is estimated with a CTRV model. The state space at time step \(k\) is defined as  
\[x_{k}=[X_{k}\ \gamma_{k}\ v_{k}\ \psi_{k}\ \omega_{k}]^{T} \tag{10}\]  
where \(X_{k}\) is the relative distance in the longitudinal direction, \(Y_{k}\) is the relative distance in the lateral direction, \(v_{k}\) is the target speed, \(\psi_{k}\) is the relative course heading, and \(\omega_{k}\) is the yaw rate. The state transition equation for the prediction of the state at time step \(k+1\) is given by  
\[x_{k+1}=x_{k}+\left[\begin{array}{c}\frac{v_{k}}{\omega_{k}}(\sin(\psi_{k}+ \omega_{k}\Delta t)-\sin(\psi_{k}))\\ \frac{v_{k}}{\omega_{k}}(-\cos(\psi_{k}+\omega_{k}\Delta t)+\cos(\psi_{k}))\\ 0\\ \omega_{k}\Delta t\\ 0\end{array}\right]. \tag{11}\]  
For the purpose of risk assessment, the circle model described in [23] is employed and a possible collision event is predicted based on the future trajectories of the remote vehicle and the host vehicle as shown in Figure 5. The host vehicle radius \(R_{HV}\) and the remote vehicle radius \(R_{RV}\) are given by  
\[R_{HV} =\frac{\sqrt{W_{HV}{}^{2}+L_{HV}{}^{2}}}{2} \tag{12}\] \[R_{RV} =\frac{\sqrt{W_{RV}{}^{2}+L_{RV}{}^{2}}}{2} \tag{13}\]  
Figure 4: High-level fusion system architecture.  
where \(W_{HV}\) and \(L_{HV}\) denote the width and the length of the host vehicle, respectively, and \(W_{RV}\) and \(L_{RV}\) denote the width and the length of the remote vehicle, respectively. A possible collision is determined if the following inequality is true:  
\[\sqrt{\left(X_{HV}-X_{RV}\right)^{2}+\left(Y_{HV}-Y_{RV}\right)^{2}}\leq R_{HV}+ R_{RV}\;. \tag{14}\]  
The detection of a possible collision leads to the generation of a collision warning to the host vehicle. Based on the time-to-collision (TTC) estimate, four different collision warning messages are provided. Following the collision warning stages discussed in [69], the warning messages provided by the proposed system include \"no threat\" for no possible collision, \"threat detected\" for \(\text{TTC}>2.6\) s, \"inform driver\" for \(1.6\) s \(<\text{TTC}\leq 2.6\) s, and \"warn driver\" for \(\text{TTC}\leq 1.6\) s. The description for each warning message is described in Table 5. The conditions for the collision warning messages defined here are similar to those of Daimler PRE-SAFE [70] and Mobileye FCW [71]. The PRE-SAFE and Mobileye systems warn the driver approximately \(2.6\) s and \(2.7\) s before the expected collision, respectively. In the case of the PRE-SAFE system, an additional warning is provided at approximately \(1.6\) s before the expected collision.
## 4 Results
### Qualitative Data Analysis
#### 4.3.2 Importance of Precision in Implemented Interactions  
Participants at the first level generally consider precision in reaching 3D objects to be important, with most participants giving a score of 5, as shown in Figure 7a.  
The analysis of the survey results on the second level in Figure 7b shows that the participants rate the importance of precision in the resizing of 3D object magnification differently. At the sublevel without distractors, grades 1 and 2 were given by 28.30% of participants, while 20.75% of participants gave the highest grade of 5. Grades 3 and 4 were given by 20.75% and 30.19% of participants, respectively, showing that participants had a wide range of opinions on the importance of precision in this type of interaction. At the sublevel with distractors, a significantly larger number of participants, 52.83%, gave the highest rating of 5, emphasizing the high importance of precision at this sublevel. In contrast, the lowest ratings of 1 and 2 were only given by 5.66% of participants, indicating that participants generally see a greater need for precision at this sublevel. These results suggest that the task where distractors were introduced may have been more demanding. It is also possible that the experience on the sublevel without distractors may have given participants a deeper understanding of the task and the skills required to accurately magnify the 3D object.  
Figure 7: Participants’ ratings of the importance of precision in (**a**) 3D object retrieval; (**b**) resizing a 3D object; (**c**) rotating a 3D object; (**d**) pressing 3D button with a virtual finger.  
At the third level (Figure 7c), a similar number of participants (58.49% and 54.72%) gave the highest rating in both sublevels, indicating that most participants considered precision to be very important for successfully solving the task.  
Similarly, most participants in all sublevels at the fourth level (Figure 7d) confirmed the importance of precision when interacting with 3D buttons with the virtual hand and gave it the highest rating.