esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognizing and Recovering Ball Motion Based on Low-Frame-Rate Monocular Camera
## 4 Experimental Results
### Discussion  
Our method was evaluated through several experiments. The result shows that the F1-score of distinguishing streaks was 0.97, and the average integrity of the extracted streaks was 87.7%. Furthermore, our trajectory reconstruction method could provide better results than existing approaches.  
As most monocular-vision-based approaches do, our proposed method also faced the problem of outputting inaccurate results on the moving direction prediction. To solve this problem, it is necessary to increase the number of cameras in different locations, but this will increase the cost.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Speed (m/s)**} & \multicolumn{3}{c}{**Velocity Prediction Error**} \\ \cline{2-4}  & **Method in [11]** & **Method in [12]** & **The Proposed Method** \\ \hline
3-11 & 3.24 & 3.68 & **1.99** \\
11–17 & 2.26 & 0.95 & **0.72** \\
17–22 & 1.35 & 0.96 & **0.64** \\
22–28 & 1.06 & 0.59 & **0.22** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The average errors of the predicted velocities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Angle (\({}^{\circ}\))**} & \multicolumn{3}{c}{**Direction Prediction Error (\({}^{\circ}\))**} \\ \cline{2-4}  & **Method in [11]** & **Method in [12]** & **The Proposed Method** \\ \hline
0–10 & 57.79 & 8.041 & **5.17** \\
10–20 & 57.31 & 17.10 & **11.71** \\
20–30 & 52.74 & 26.52 & **25.63** \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average errors of the predicted directions.
# High Precision Indoor Positioning System for Mobile Asset Management and Safety +
## 3 Conclusions  
Dynamic object tracking in indoor settings has posed a crucial scientific problem. It includes numerous applications where it can be implemented in various locations for moving objects' indoor localization. The study concluded that our developed indoor positioning system's results are better compared with other technology, and the comparison is provided in Table 1. UWB radio-based embedded systems technology provides a much better accuracy than WiFi for basements and closed environments. In addition, the associated hardware used in this study were wireless anchors and wireless tags, onboard controller program files to make the hardware work as wireless anchors or wireless tags, and offline executable files to track the monitored devices locally. The position of the movable tags was calculated by using triangulation and the TDOA approach with meter-level accuracy.  
Conceptualization, M.K., Methodology, L.U.Z. and H.S.F., Software Development, H.S.F., Validation, L.U.Z., Formal Analysis, L.U.Z. and M.K., Hardware Gadget, L.U.Z., Original Draft Preparation, H.S.F., Supervision, M.K., Review, H.Z. and Editing, H.S.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Experiments and Validation
### Environment Setup  
The model's precision is assessed using two key performance indicators: mean absolute error (MAE) and root-mean-square error (RMSE). Their calculation formulas are given as  
\[MAE=\frac{1}{M}\underset{i=1}{\overset{M}{\underset{i=1}{\sum}}}|p_{i}-p_{i}^{* }| \tag{22}\]  
\[RMSE=\sqrt{\frac{1}{M}\underset{i=1}{\overset{M}{\underset{i=1}{\sum}}}\left( p_{i}-p_{i}^{*}\right)^{2}} \tag{23}\]  
where \(p_{i}\) represents the actual values and \(p_{i}^{*}\) denotes the predicted values.
## 5 Conclusions  
In this paper, we develop a useful solution to the HSV problem with unexpected centroid shifts, actuator faults and external perturbations. Resorting to a designed sixth-order dynamic compensator, the proposed adaptive FTC scheme can achieve the attitude tracking objective without the exact knowledge of the inertia matrix \(J\), despite the presence of variations in the system inertia matrix, eccentricity and actuator faults. Subsequently, based on the properties of eccentric moments, systematic uncertainties and external perturbations, the performance of the proposed FTC controller can be improved by adapting the corresponding adaptive estimator to support the designed FTC controller using adaptive techniques.Then, the stability of this designed FTC strategy is guaranteed by the Lyapunov stability theorem. Finally, the simulation results show that the proposed FTC scheme is effective for the reconfigurable HSV flight control design under the condition of the inertia matrix and eccentricity.  
In future work, we will focus on the FTC of HSV with structural failures caused by variable centroid displacement (the structure and size of the elements of the inertial matrix \(J\) are uncertain). Furthermore, the problem of deflection saturation of the control surface should also be taken into consideration [24].  
Writing--original draft preparation, H.Y.; writing--review and editing, Y.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61773012), the National natural sciences fund youth fund project (61807017), the research start-up fund of Jiangsu University of science and technology (1052932101).  
Figure 11: Control surface deflection angles of \(\delta_{t}\).  
Figure 9: Control surface deflection angles of \(\delta_{c}\).  
**Institutional Review Board Statement:** This study did not involve humans or animals.  
**Informed Consent Statement:** This study did not involve humans or animals.  
**Data Availability Statement:** Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The database is identified by References [8,10].  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 1 Introduction
### A Toolkit for Protoring  
The aim of this paper is to propose an open-source initiative to make proctoring-related algorithms accessible, portable, scalable and customizable, as a toolkit. Instead of creating a complete proctoring solution, the authors aim to promote customizablility, transparency, and alleviate concerns about proctoring tools, as discussed in [20]. Additionally, the proposed algorithms are designed to run directly on consumer devices, which can be called edge, in real-time, eliminating the need to send video streams or recorded videos to a remote server. This helps protect the privacy of end users. For organizations and facilitators, it can reduce the need for dedicated hardware and infrastructure, as the algorithms can extract features at the edge. This addresses scalability issues.  
As of the writing, the proposed toolkit is able to detect face orientation and eye blink. Those features are important for a variety of purposes, including proctoring, as they can provide valuable insights into the behavior and attentiveness of individuals. For example, detecting face orientation can help to determine whether someone is looking directly at the screen or camera, which can be useful for detecting cheating during online exams or for ensuring that employees are paying attention during online meetings. Eye blink detection can also be useful for assessing attentiveness, as prolonged periods of inactivity or low blink rate can be indicators of fatigue or lack of engagement.  
Similar to the proposed toolkit, the OpenFace Toolkit [29] is an open-source project that offers libraries and tools for facial behavior analysis. It is written in C++ and Python and is designed to be cross-platform, utilizing the CMake build system to run on Windows, Linux, and macOS. However, certain features based on dlib and TBB may not be compatible with mobile devices such as iOS and Android, and some prior knowledge of CMake is required to build for these platforms. In contrast, the authors' toolkit uses the out-of-the-box build configuration provided by the MediaPipe Framework to easily support a wider range of devices.
# Prediction of Wind Power with Machine Learning Models
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.2.3 Recurrent Neural Networks Structure  
RNNs have been utilised to assimilate the short-term temporal dependency on wind power. RNNs can consider antecedent information and formulate a prediction [39]. RNNs learn their predictions by adjusting ML parameters through backpropagation and gradient descent. RNNs are designed to process input data and model dependencies in sequential data. As a result, RNNs typically consist of multiple neuron layers, with each layer improving by utilising the previous layer's outputs.  
RNN structure parameters are shown in Table 3. The proposed architecture comprised 32 neurons with a ReLU activation function in the first layer. Following this, a flattened layer was implemented, after which, a dense layer comprising 64 neurons and utilising a ReLU activation function was employed. This was then succeeded by another thick layer containing 32 neurons and a ReLU activation function. Finally, the model was completed with a dense layer with a single output and a linear activation function.
## 2 Methodology
### Evaluation Parameters  
Evaluation parameters used in this research are defined with the confusion matrix in Table 1.  
The rate of positive test result is sensitivity,  
\[Sensitivity=\frac{TP}{TP+FN}*100\% \tag{9}\]  
The ratio of a negative test result is specificity,  
\[Specificity=\frac{TN}{TN+FP}*100\% \tag{10}\]  
The overall measure is accuracy,  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}*100\% \tag{11}\]
## 3 Mathematical Model for Optimal Trajectory Planning
### Objective Function Design
#### 3.2.1 Energy Consumption (EC)  
Due to the physical limitations of small UAV, like battery power management, we need to design the path for UAV carefully so that it can surveille the path within its limited battery power. Therefore, it is always preferable to a path with less fuel consumption. Assuming the UAV having constant velocity during the operation time, then we formulated energy consumption, EC as,  
\[F_{EC}=\frac{\sum_{i=1}^{N_{w}-1}EC_{i}}{maxEC} \tag{5}\]  
where,  
\[EC_{i}=\ P_{w}*t_{i,i+1} \tag{6}\]  
\[t_{i+1}{=}\frac{d_{i,i+1}}{v} \tag{7}\]  
\[d_{i,i+1}=\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left(y_{i+1}-y_{i}\right)^{2}+ \left(z_{i+1}-z_{i}\right)^{2}} \tag{8}\]  
where \(EC_{i}\) is the energy consumption from the \(i\)th waypoint to (\(i+1\))th waypoint. \(P_{w}\) the energy consumption of the UAV at velocity \(v\) for unit time, while \(t_{i,i+1}\) is the duration of flight time taken by UAV from \(i\)th waypoint to (\(i+1\))th waypoint. \(d_{i,i+1}\) is the Cartesian distance of a flight from \(i\)th waypoint to (\(i+1\))th waypoint. We normalized the energy consumption and formulated it as, \(max\)\(EC\),  
\[maxEC=(N_{w}-1)*P_{w}*\frac{d_{max}}{v} \tag{9}\]  
where,  
\[d_{max}=\sqrt{X^{2}+Y^{2}+Z^{2}} \tag{10}\]  
where \(X\), \(Y\), and \(Z\) are the boundary condition of operation space in the 3-D environment.
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 5 Experimental Results
### Impact of Prediction on HO Costs  
Figure 7 represents the results of the vertical and horizontal costs regarding the non-predictive case and the predictive case using different mobility models. We can observe the predictor accuracy and the corresponding HO costs for each model. Table 4 summarizes the delays regarding each type of link between two nodes in the process of HO completion. As shown, we obtained the highest transmission cost at 138.5 for the vertical handover when there is no prediction, and it gradually decreased as the prediction accuracy increased. It has the lowest value for the VAR-GRU at 99.7 (i.e., 28.01% reduction). For horizontal HO, the transmission cost for the non-predictive case is 23. This value slightly increased when we used the RNN and LSTM models as the mobility model at, respectively, 23.3 and 23.2. However, for GRU and the proposed VAR-GRU models, this cost was reduced to 22 and 21.67.  
Figure 6: Impact of different mobility behaviours on the performance of the mobility models.  
In Table 5, the handover processing costs for non-predictive (i.e., conventional) and predictive (proposed method) scenarios are presented. As mentioned earlier, this cost is defined based on the number of handover messages required for HO procedure completion. According to Table 5, for horizontal handover (see Figure 3), the proposed method effectively reduces the number of messages from 12 to 7 (i.e., 41.66% improvement). However, if the target gNB was predicted incorrectly, 14 messages are needed to complete the HO. Additionally, for a vertical handover (see Figure 4), the proposed method improves the processing cost by 57.14% (from 28 to 12) and it needs 34 messages for the case with a wrong prediction. Therefore, it is vital to consider the impact of wrong predictions in our evaluations as well. The obtained processing costs suggest that not having any prediction is more efficient than making decisions based on wrong predictions. Hence, predictor accuracy is of primary importance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Link Type** & **Delay** \\ \hline UE to NG-RAN & 1 ms \\ NG-RAN to AMF & 7.5 ms \\ AMF to SeMMu (PGW-C + SMF) & 1 ms \\ SeMMu to S-GW & 7.5 ms \\ SeMMu to PGW-U+UPF & 7.5 ms \\ SeMMu to PCRF+PCF & 7.5 ms \\ AMF to AMF & 15 ms \\ SeMMu to PGW & 7.5 ms \\ SeMMu to E-UTRAN & 7.5 ms \\ E-UTRAN to UE & 1 ms \\ PGW to PCRF & 7.5 ms \\ S-GW to PGW & 7.5 ms \\ SeMMu to SGSN & 1 ms \\ SGSN to RNC & 6 ms \\ SGSN to S-GW & 7.5 ms \\ SeMMu to SeMMu & 15 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: The delay regarding each link type in rgw HO procedure [10,25].  
Figure 7: Handover transmission cost.
## 4 The SMA-LRBs  
Adding the SMA wires to base isolators as a supplementary element can mitigate the seismic response of the structures in terms of displacement and internal forces, and offers some benefits such as re-centering capability, stability, high fatigue resistance, high energy dissipation capacity, etc. For these reasons, many researchers have used SMA wires in conjunction with LRBs, which some of them are mentioned in the introduction.  
In this study, the newly discovered iron-based SMA alloy, i.e., FeNiCoAlTaB with 13.5% super-elastic strain is used, and the finite element approach is applied to investigate the effects of different wire configurations on the seismic performance of a benchmark bridge. To do so, some milestones should be reached, including the development of an appropriate constitutive model for the SMA-LRBs. Due to the complexity of the shear behavior of the SMA-LRBs, [PERSON] and [PERSON] [24] suggested using the idea of superposition to divide the smart isolation into two separate systems, namely, SMA wire and LRB to simplify the model. Then, to obtain the shear hysteresis model for the SMA-LRB, first, the model is found for each device; second, the models are added to each other to form the hysteresis behavior of the isolation system. It is worth mentioning that [PERSON] and [PERSON] verified the superposition method through the finite element model validated by experimental results [24].  
Figure 5: (a) Arias intensity of the real record from Table 1 for the station “El Centro Array #12” and the Arias intensities of five corresponding synthetic acceleration time histories shown in Figure 1a, for both horizontal ground motion components. (b) Arias intensity of the real record from Table 1 for the station “Westside Elementary School” and the Arias intensities of five corresponding synthetic acceleration time histories shown in Figure 1b, for both horizontal ground motion components.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Heuristic Slice Provisioning  
The proposed two-stage slice provisioning algorithm is described in detail below.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 2 Mathematical Models  
The time-domain model, ACF, and PSD expressions of ChPN will be explored in this section.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 3 Technologies and Methods for Rockburst Research
### Numerical Simulation Techniques
#### 3.4.2 Hybrid Approaches  
In general, the hybrid approach model describes continuous deformation and discontinuous fracture using finite elements and contact surfaces, respectively. The failure process of rockburst is characterized by a transition from a continuous to a discontinuous state as well as a transition from static to dynamic damage. As a result, the simulation should use an appropriate constitutive model to describe the material separation process as well as a suitable mechanism to calculate the interaction forces between elements that control particle motion. Explicitly modeling the continuous to discrete transformation yields a physically realistic representation using a number of testable parameters. The process and principle of GDEM are depicted schematically in Figure 6, where the block composed of one or more finite element elements is used to characterize the elastic, plastic, damage, and other continuous properties of materials, and the interface between them, using spring connectors, is used to characterize the fracture, slip, collision, and other discontinuous properties. The process of bending-bulging rockburst can be well captured by GDEM. In general, hybrid approaches can better describe the transition from continuous deformation to discontinuous fracture than continuum and discrete approaches, demonstrating their advantage in simulating rockbursts. The computational costs of hybrid approaches, on the other hand, are extremely high, thus limiting their applications.  
Figure 5: History of numerical methods employed in modeling rockbursts.
# A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network
## 1 Introduction
Therefore, the NLOS tracer method is proposed in this paper to record the occurrence of an incorrect position estimation. When we use the NLOS tracer method to find that there are two or more consecutive occurrences of incorrect position estimation, the EKF is used for updating to reduce the adverse influence of this situation on positioning and improve the robustness of the algorithm. (2) The improved probabilistic data association algorithm has a high positioning accuracy and robustness to NLOS errors, therefore it is used to reduce the NLOS errors. (3) The simulation and experimental results show that the proposed algorithm in this paper can mitigate the influence of NLOS errors when the NLOS error obeys different distributions. The paper is structured as follows. Section 2 introduces the signal model and provides an overview of existing techniques from the literature. The proposed algorithm is explained in Section 3, and Section 4 illustrates the simulation results. Conclusions are drawn in Section 5.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 2 Methodology
### Gap Evaluation  
This subsection covers the methods used in Part A of the algorithm, which are used to evaluate the available gaps for lane change. For each lane, we only consider the single gap in which the current longitudinal position of \(V_{H}\) resides. This means we only need to examine two or three gaps, which is in accordance to the fact that ordinary sensors on autonomous vehicles can only guarantee detection of the nearest vehicles in current and adjacent lanes, as Figure 2 shows. Besides, the solution is simplified and calculated load can be significantly reduced.  
Figure 1: The control diagram of the lane change algorithm with three parts: identification of available vehicle gaps, trajectory planning and trajectory correction.
## 4 Semantic Representation: EUREKA Schema Proposal
### Parsing Workflow  
Statements from multilingual speech/text are processed by a transformer-based semantic parser that identifies relevant tokens, aligns them to schema slots using constrained decoding, and integrates structured patient data to fill or refine entries. Temporal reasoning is supported through lightweight rule-based modules and clinical heuristics. Figure 3 illustrates the workflow of the EUREKA scheme.  
Here is an example of the parsing. Given the input: \"My mother can't speak properly and her right arm is numb. It just started.\", EUREKA parses this into the following:  
```
symptoms = [speech.slurred, limb.weakness.right], onset_time = <5 min>, risk = high, action = activate_stroke_protocol
```  
EUREKA enables fine-grained semantic abstraction while preserving alignment with clinical realities. It ensures that NLP outputs from TriagE-NLU are both machine-actionable and human-verifiable, essential for deployment in safety-critical settings like telemedicine triage.
# An Active Path-Associated Cache Scheme for Mobile Scenes
## 3 The Proposed Approach
### Neighbor Selection Mechanism  
The distribution of APs is usually arranged according to the signal coverage requirements (RSSI), which does not take into account the actual user's mobile behavior. However, the movement behaviors generated by a large number of users will show different transition probabilities from the source AP to the neighboring AP. Figure 2 shows the above process. It can be seen that the transition probability of the source AP to the neighboring AP presents a large difference. In traditional IP networks, there is also research on caching based on neighbors (such as PNC [29] and SNC [15; 30]), but the main research object is the contextual information of mobile devices, and the focus is on data buffering. This is essentially different from this paper's focus on the caching of mobile ICN network content. We first introduce the acquisition of transition probability.
# A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
## 5 Analysis and Discussions
### Effect of Topology and Human Mobility Information  
To demonstrate the effectiveness of multiview information learning, we adjust hyperparameter \(\eta\) in Equation (9) to control the different proportions of topology and human mobility information in the representation learning process. The ETA errors (MAPEs) resulting from the different representations under the various \(\eta\) settings are illustrated in Figure 9. On the Xi'an dataset, there is a clear trend whereby the ETA error gradually decreases from only topology aware (\(\eta=1\)) or only mobility aware (\(\eta=0\)) to topology and mobility aware (\(\eta\) near 0.5). On the Shenzhen dataset, the minimum error occurs when the topology and mobility information are equally embedded (\(\eta=0.5\)). This indicates the effectiveness of multiview information learning.  
Figure 8: Relationships of the different distances in various spaces, including: (**a**) the distance in latent space and the corresponding topological distance; (**b**) the distance in latent space and the corresponding transition probability.