esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The conventional isolation structure with rubber bearings exhibits large deformation characteristics when subjected to infrequent earthquakes, which may lead to failure of the isolation layer. Although passive dampers can be used to reduce the layer displacement, the layer deformation and superstructure acceleration responses will increase in cases of fortification earthquakes or frequently occurring earthquakes. In addition to secondary damages and loss of life, such excessive displacement results in damages to the facilities in the structure. In order to overcome these shortcomings, this paper presents a structural vibration control system where the base isolation system is composed of rubber bearings with magnetorheological (MR) damper and are regulated using the innovative control strategy. The high-order single-step algorithm with continuity and switch control strategies are applied to the control system. Shaking table test results under various earthquake conditions indicate that the proposed isolation method, compared with passive isolation technique, can effectively suppress earthquake responses for acceleration of superstructure and deformation within the isolation layer. As a result, this structural control method exhibits excellent performance, such as fast computation, generic real-time control, acceleration reduction and high seismic energy dissipation etc. The relative merits of the continuity and switch control strategies are also compared and discussed.  
structural vibration control; base isolation control system; magnetorheological (MR) damper; shaking table test; high order single step integration algorithm +
Footnote †: journal: Applied Sciences
# Technology Behavior Model--Beyond Your Sight with Extended Reality in Surgery
## 2 Literature Review
### Theoretical Foundation  
#### 2.2.1 Technology Acceptance Model  
The technology acceptance model is [PERSON]'s rational behavior in studying the acceptance of information or technology systems by users. [PERSON] et al. [28] argued that TAM is one of extensive recognized and commonly used models to study the willingness to use  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Past** & **Now** & **Future** \\ \hline Environment Awareness & \multicolumn{2}{c}{–} & \multicolumn{2}{c}{High-definition camera} \\ \cline{2-4} Technology & Fixed label & Computer graphics & Deep learning \\ \hline Motion tracking & \begin{tabular}{c} Magnetic markers, visual \\ markers \\ \end{tabular} & \begin{tabular}{c} GPS, inertial navigation \\ system \\ \end{tabular} & \begin{tabular}{c} An optical system, depth of \\ field camera \\ \end{tabular} \\ \hline Display technology & Handheld projector & Head-mounted display & Virtual retina display \\ \hline Interactive technologies & Flat user interface & \begin{tabular}{c} A 3D user interface, gesture \\ and pose capture, speech \\ recognition \\ \end{tabular} & \begin{tabular}{c} Touch, eye tracking, and \\ man-machine symbiosis \\ \end{tabular} \\ \hline Medical image modeling & Solution of the plane model & \
\begin{tabular}{c} Surface shading technique, \\ volume roaming technique, \\ and surface reconstruction \\ technique \\ \end{tabular} &
\begin{tabular}{c} CT model reconstruction + \\ real-time deep learning \\ calibration \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: XRSG technology in the future.  
new technologies [24]. The use of this model in this study can help medical experts to perceive the usefulness and ease of use of XRSG by using XRSG in clinical operations. Medical experts actively use XRSG in clinical operations to change user behavior. Through subjective perception, good impression, and active choice, people's acceptance of using extended reality smart eye is developing. According to the current understanding of relevant information, SG can play an important role in clinical surgery, facilitate doctors to operate, and improve the success rate of surgery, thereby changing people's behavior and approving the use of SG in surgery.
# InfNRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control
###### Abstract  
Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducibility and interpretability of the results. For example, a manual inspection is still necessary to assess the quality and subsequent retention of collected fNIRS signals for analysis. Machine Learning (ML) approaches are well-positioned to provide a unique contribution to fNIRS data processing by automating and standardizing methodological approaches for quality control, where ML models can produce objective and reproducible results. However, any successful ML application is grounded in a high-quality dataset of labeled training data, and unfortunately, no such dataset is currently available for fNIRS signals. In this work, we introduce fNIRS-QC, a platform designed for the crowd-sourced creation of a quality control fNIRS dataset. In particular, we (a) composed a dataset of 4385 fNIRS signals; (b) created a web interface to allow multiple users to manually label the signal quality of 510 10 s fNIRS segments. Finally, (c) a subset of the labeled dataset is used to develop a proof-of-concept ML model to automatically assess the quality of fNIRS signals. The developed ML models can serve as a more objective and efficient quality control check that minimizes error from manual inspection and the need for expertise with signal quality control.  
fNIRS; machine learning; quality control +
Footnote †: journal: Applied Sciences  
1
## 2 Materials and Methods
### Evaluation of Pancreas Segmentation  
For each validation case of the Pancreas-CT dataset, three-dimensional CT images were processed slice-by-slice using the trained deep learning models, and the segmentation results were stacked. Except for the stacking, no complex postprocessing was utilized. Quantitative and qualitative evaluations were performed for the automatic segmentation results.  
The metrics of quantitative evaluation were calculated using the three-dimensional segmentation results and annotated labels. Four types of metrics were used for the quantitative evaluation of the segmentation results: dice similarity coefficient (DSC), Jaccard index (JI), sensitivity (SE), and specificity (SP). These metrics are defined by the following equations:\[DSC=\frac{2|P\cap L|}{|P|+|L|} \tag{3}\]  
\[JI=\frac{|P\cap L|}{|P|+|L|-|P\cap L|} \tag{4}\]  
\[SE=\frac{|P\cap L|}{|L|} \tag{5}\]  
\[SP=1-\frac{|P|-|P\cap L|}{|I|-|L|} \tag{6}\]  
where \(|P|\), \(|L|\), and \(|I|\) denote the number of voxels for pancreas segmentation results, annotated label of pancreas segmentation, and three-dimensional CT images, respectively. \(|P\cap L|\) represents the number of voxels where the deep learning models can accurately segment pancreas (true positive). Before calculating the four metrics, a threshold of 0.5 was used for obtaining pancreas segmentation mask from the output of the U-net [24]. The threshold of 0.5 was fixed for all the 82 cases. A Wilcoxon signed rank test was used to test statistical significance among the DSC results of 10 combinations of deep learning models and data augmentation methods. Bonferroni correction was used for controlling family wise error rate. \(p\)-values less than 0.05/45 = 0.00111 was considered as statistical significance.  
For the qualitative evaluation, two radiologists with 14 and 6 years of experience visually evaluated both the manually annotated labels and automatic segmentation results using a 5-point scale: 1, unacceptable; 2, slightly unacceptable; 3, acceptable; 4, good; 5, perfect. Inter-observer variability between the two radiologists were evaluated using weighted kappa with squared weight.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 4 Localization Methods  
In the second phase, through the measured parameters of the signal and the known coordinates of the reference nodes, the coordinates of the unknown nodes can be determined. There are several positioning techniques; among the most important are trilateration, triangulation, multilateration, and fingerprinting.
# Simulation Tool for Winter Navigation Decision Support in the Baltic Sea
## 8 Verification and Validation of the Simulation Model
### Validation
#### 8.2.3 Historical Data Validation  
The historical data include arrival and departure times at ports along with the time spent at port. As mentioned earlier, only the port departure times are included as inputs in the model. The arrival of a vessel at its destination port depends on the ice conditions, dirways, the availability of icebreakers, and the logic of how icebreakers prioritize assistance requests from multiple vessels. One of the ways to validate the model is by comparing the arrival times of vessels at different ports in its schedule with the historical arrival times. A total of 249 trips were analyzed. For ease of visibility, a set of ships were chosen to be presented. Figure 8a,b show the simulated and historical arrival times plotted for two sets of ships. It is observed that most trips are close to their expected arrival times. There are instances, such as Vessel 4 in Figure 8b, where significant delays are experienced. Study of such cases will help to improve the model in future work.
## 2 Antenna Geometry and Data Generation  
This section introduces the overall structure and design parameters of a bent wire monopole antenna and then explains the acquisition of the training data using the Numerical Electromagnetic Code (NEC) simulation [1].
## 4 Validation
### Validation Results
#### 4.3.4 Confidence Intervals  
The statistical agreement between the maritime data and the simulation outcome is tested in terms of confidence intervals. Specifically, for each of the simulated port-specific number of instances of IB assistance and mean IB waiting times, we consider an approximate 95 confidence interval determined by multiplying the standard deviation (\(\sigma_{sim}\)) of the mean values (\(\mu_{sim}\)) of 35 independent simulation runs by 2. As per Tables 4 and 5, for each of the simulated performance metrics, the maritime data fall within the obtained 95% confidence interval.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Port** & **Maritime Data** & \(\mu_{sim}\) & \(\sigma_{sim}\) & \(\mu_{sim}-2\sigma_{sim}\) & \(\mu_{sim}\) + \(2\sigma_{sim}\) \\ \hline Kokkola & 18 & 28.7 & 6.5 & 15.8 & 41.7 \\ Raahe & 48 & 38.7 & 5.5 & 27.6 & 49.7 \\ Oulu & 50 & 52.5 & 6.8 & 38.8 & 66.1 \\ Kemi & 28 & 38.4 & 5.2 & 28.0 & 48.9 \\ Tornio & 48 & 54.5 & 7.1 & 40.3 & 68.6 \\ \hline \end{tabular}
\end{table}
Table 4: Approximated 95% confidence interval for the simulated port-specific number of instances of IB assistance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Port** & **Maritime Data** & \(\mu_{sim}\) & \(\sigma_{sim}\) & \(\mu_{sim}-2\sigma_{sim}\) & \(\mu_{sim}\) + \(2\sigma_{sim}\) \\ \hline Kokkola & 1.7 & 3.6 & 1.4 & 0.7 & 6.4 \\ Raahe & 3.5 & 3.5 & 1.2 & 1.0 & 6.0 \\ Oulu & 3.8 & 3.0 & 1.6 & \(-\)0.2 & 6.1 \\ Kemi & 2.3 & 3.1 & 1.0 & 1.1 & 5.1 \\ Tornio & 3.8 & 2.9 & 0.9 & 1.2 & 4.7 \\ \hline \end{tabular}
\end{table}
Table 5: Approximated 95% confidence interval for the simulated port-specific mean IB waiting times (hours).
## 6 Conclusions and Future Work  
This article focuses on the crucial challenge of frame degradation in multi-person pose tracking, with the goal of improving accuracy while minimizing computational resource consumption. To address this challenge, we propose a spatiotemporal sampling framework that effectively leverages information from neighboring video frames to extract valuable features. What sets our approach apart from conventional pose estimation and tracking frameworks is the inclusion of a spatiotemporal sampling module specifically within the person detection stage, an aspect often overlooked in previous research. This module, which is fully differentiable, offers several advantages over existing articulated pose tracking frameworks by reducing reliance on extensive flow data and intricate propagation strategies. Moreover, our techniques seamlessly integrate into high-performance detection architectures, consistently leading to significant improvements in the tracking stage. It is worth noting that we have not explored additional acceleration of the model through the incorporation of contemporary pruning and quantification methods. In future endeavors, we intend to delve into more intricate designs for the feature sampling and aggregation modules. Additionally, our objective is to explore the potential of integrating pose estimation with articulated tracking in an end-to-end manner, rather than treating them as separate stages.  
Conceptualization, S.L. and W.H.; methodology, S.L. and W.H.; software, S.L.; validation, S.L. and W.H.; formal analysis, S.L.; investigation, S.L.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L. and W.H.; visualization, S.L.; supervision, W.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data and related information for this study are provided in accordance with our data availability policy. We adhere to relevant laws and regulations to ensure data accessibility and transparency. The dataset utilized in this study was derived from publicly accessible resources, specifically PoseTrack obtained from [[https://posetrack.net/](https://posetrack.net/)]([https://posetrack.net/](https://posetrack.net/)), PoseTrack21 sourced from [[https://github.com/andoer/PoseTrack21](https://github.com/andoer/PoseTrack21)]([https://github.com/andoer/PoseTrack21](https://github.com/andoer/PoseTrack21)), and COCO available at [[https://cocodataset.org/](https://cocodataset.org/)]([https://cocodataset.org/](https://cocodataset.org/)), with reference numbers [2; 5; 38].  
The authors declare no conflicts of interest.  
Figure 4: Sample results. The detections are color-coded based on track ID. It is worth noting that our framework demonstrates successful tracking of individuals throughout the sequence.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Cable Body Risk Parameters
#### 2.1.3 Overloaded Operation  
When the load on a cable exceeds its design capacity, the temperature of the cable rises, which can easily lead to overheating or fires.
## 1 Introduction
These algorithms are capable of filtering noise effectively, identifying individual movement patterns, and extracting meaningful performance metrics from dense sensor data. In canoeing, algorithmic processing has enabled automatic identification of stroke phases, assessment of technique consistency, and identification of fatigue-related degradation of technique [19; 20]. Advanced signal processing techniques and machine learning algorithms have enhanced our ability to analyze movement patterns in real time, providing immediate feedback to athletes and coaches [21, 22]. This technological advancement has particularly benefited the analysis of cyclic movements in sports, allowing for more precise quantification of performance parameters and technique variations [23]. Previous algorithmic approaches for paddling analysis have primarily focused on stroke detection rather than comprehensive phase identification, with [PERSON] et al. [17, 19] using peak detection algorithms and [PERSON] et al. [9] employing threshold-based approaches that lack sensitivity to broader phase transitions and are susceptible to fatigue effects. Elite C1 coaching methodologies emphasize phase-specific technique requirements--powerful asymmetrical strokes during initial acceleration, balanced rhythmical patterns in steady-state cruising, and increased stroke rate with maintained efficiency during sprints--yet current training relies heavily on subjective observation rather than quantitative metrics for phase transitions, creating an opportunity for technology-enabled coaching innovations that provide real-time feedback on phase-specific performance [3, 20, 23]. Despite these technological advances, there remains a significant gap in our understanding of how to effectively monitor and analyze different training phases in real time during canoe training sessions. While existing research has examined isolated aspects of canoeing performance, the specific challenge of automatically identifying transition points between critical training phases in C1 canoeing remains inadequately addressed, with previous studies by [PERSON] et al. [16] and [PERSON] et al. [15] quantifying overall metrics but failing to develop real-time phase segmentation methods; our polynomial fitting approach addresses this gap by modeling acceleration curves and identifying inflection points that mark genuine phase transitions rather than relying on arbitrary thresholds. Therefore, this study aims to address this gap by developing and implementing an algorithm-based system for real-time analysis of three critical training phases in canoeing: the initial acceleration phase, the steady-state cruising phase, and the final sprint phase. We hypothesize that our custom algorithm will effectively identify and analyze these distinct phases, providing valuable insights into technique efficiency and performance variations. Furthermore, we propose that this real-time analysis system will enable more precise and timely adjustments to training protocols, ultimately contributing to enhanced performance optimization in competitive canoeing.
# Multi-Agent-Supported Tracking Based on Hidden Markov Model with Weighted Entropy
## 1 Introduction
* Reconstructing Kalman filters within a unified HMM framework: This architecture explicitly models state transitions and observation processes corresponding to the dual-phase model, enabling probabilistic inference and better prediction of pedestrian trajectories. * The proposal of a weighted entropy mechanism for adaptive multi-cue fusion, dynamically balancing appearance and motion cues based on real-time uncertainty quantification: By leveraging entropy as a measure of feature reliability, our mechanism adjusts fusion weights to prioritize stable features during tracking, enhancing robustness to occlusions and appearance changes. * The introduction of a multi-agent mechanism to manage the tracks separately. By assigning each track to an independent agent, the HMM-based Kalman filter and multi-cue fusion with weighted entropy can be effectively conducted and updated according to the obtained rewards.
## 4 Results
### Phase and Separation Variation
**(a)** The average autocorrelation is shown in red, and the black dotted line shows the autocorrelation of a pulse with an average separation; **(b)** Average spectrum of the double pulse train; **(c)** Polar plot of the pulse separation as a function of relative phase; **(d)** Average SPIDER measurement of the whole pulse train; **(e)** Average FROG measurement of the whole pulse train; **(f)** Retrieved FROG trace with G error 0.0124; **(g)** Temporal intensity and phase of the pulse retrieved by SPIDER; **(h)** Spectral intensity and phase of the pulse retrieved by SPIDER; **(i)** Temporal intensity and phase of the pulse retrieved by FROG; **(j)** Spectral intensity and phase of the pulse retrieved by FROG. The general structure is quite consistent, however. The retrieved pulse separations are always equal to the correct average separation of the double pulses measured.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Land, Wetland, and High-Earth Regions Mapping  
The wetland is a core element in the economic resources management of the world. It performs many beneficial activities for the global ecosystem. It helps us to reduce natural hurricanes, floods, and storms. Typically, optical and thermal remote sensors are applied for the land observation and for the identification of the major disturbing events such as severe storms and fires [49]. Among those are Geo eye [50], Rapid eye, Quick birds [51], Worldview satellites [52], and airborne remote sensors. Radar backscatter allows us to assess the dielectric properties of soil (dielectric constant) and to infer the surface layout (smooth vs rough surface). The dielectric constant is, in general, a complex quantity that can be expressed as a combination of two real quantities, the real permittivity and the conductivity [53]. Both are highly dependent on the media and provide a measurement of the electrical properties of a specific medium. In dry conditions, most naturally occurring media have a relative permittivity which assumes values in the range between 3 and 8 [54]. When water is added to dry soil, its permittivity increases and, as a consequence, the radar reflectivity also increases [55; 56; 57; 58; 59]. Radar Imaging can also be used to provide optical data of both wetland and upper region of the Earth [60]. Synthetic Aperture Radar (SAR) with its significant ability to penetrate the clouds is also used for day-night monitoring and measurement of wetland [61]. Ground-Penetrating Radar (GPR) is a particular radar technology used to investigate the \"underground world\". Depending on the application, the radar can use either air-coupling or ground-coupling antennas. GPR is employed in a variety of fields ranging from geophysical to civil engineering applications: for instance, it may be used for the analysis of wetland, rocks, pavements and structures analysis [62; 63; 64], underground water detection [65], or the study of ice sheets and the thickness of glaciers [66].
## 3 System Model and Problem Formulation
### Network Model  
This paper considers the UAV-assisted VANET, which consists of \(N\) network nodes (vehicles or UAVs). The network nodes adopt the shortest path map based movement (SPMBM) mobility model to plan the trajectory [29]. The nodes periodically interact with each other by the Hello message, which is used to discover available neighbor nodes.  
As shown in Figure 1, when the SN \(N_{a}\) has the message to send, \(N_{a}\) can select the neighbor node \(N_{j}\) as the RN to deliver its message by the SCF method. As such, the end-to-end delay \(T_{a,b}\) from the SN \(N_{a}\) to the DN \(N_{b}\) can be expressed as  
\[T_{a,b}=\sum_{i=1}^{n}\left(T_{S_{i}}+T_{P_{i}}+T^{\prime}{}_{P_{i}}+T_{Q_{i}} \right)+\sum_{i=1}^{n-1}T_{C_{i}}, \tag{1}\]  
where \(i\) represents the \(i\)-th hop, \(n\) represents the total number of hops, \(T_{S_{i}}\), \(T_{P_{i}}\), \(T_{P_{i}}\), \(T_{Q_{i}}\), and \(T_{C_{i}}\) represent the \(i\)-th hop sending delay, propagation delay, processing delay, queuing delay, and the carrying delay, respectively.