esa-sceva/satcom-chunk-collection huggingface dataset:
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
Based on the above work, three challenges remain in the task of trajectory recovery from signaling data. (1) Existing research on signaling data mainly focuses on using signaling data for coarse-grained urban sensing tasks. How to design a fine-grained trajectory recovery framework is still a blank. (2) Existing methods usually regard trajectory recovery as a sequential problem, and they only use the coordinates and time information of the trajectories to tackle this problem, which makes it difficult to capture the spatial dependence between base stations. (3) Signaling data have complicated spatiotemporal features that can somehow reflect the moving patterns of the users by recording the locations of the cell towers, the time that users are within the detection range of the cell towers, and unique IDs of the cell towers. How to fully utilize the rich information of them and fuse the multiple features well is still a huge challenge. To tackle these challenges, we propose a novel graph-enhanced trajectory recovery framework, i.e., GenTrajRec, in this paper, which is dedicated to recovering precise trajectories from raw signaling data. To obtain the fine-grained trajectory, we designed a new real position restoration problem that maps a coarse trajectory to an accurate location in this work. To capture deep transition patterns, GenTrajRec first develops a trajectory transition graph module, which reconstructs the relations between base stations. To capture both the spatial-temporal features and the contextual information of trajectory, GenTrajRec then utilizes a time encoder and a position encoder, which can learn rich spatial and temporal patterns. To make full use of these features, we introduce a feature fusion module that can aggregate spatial-temporal embeddings and transition graph embeddings. Finally, we adopt a transformer-based ST-layer to recover precise trajectory. Overall, our contributions can be summarized as follows. Figure 1: Signaling data. (**a**) shows that the base stations cannot represent the real position of the users. These base stations still have a distance from the real trajectory. (**b**) shows that there could be redundant segments in the trajectories. * We propose a novel framework for recovering precise trajectories from raw signaling data, which has great prospects in constructing large-scale trajectory datasets and solving the problem of data sparsity in most downstream traffic prediction tasks. * We introduce a trajectory transition graph module to capture deep transition patterns. Combining spatiotemporal features and transition pattern features, we design a transformer-based sequential model for trajectory reconstruction. * We conduct substantial experiments on two real-life datasets to evaluate the effectiveness of our proposed GenTrajRec. Experimental results demonstrate that GenTrajRec outperforms existing methods.
# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 2 Related Works  
### Visual SLAM  
The research field of visual SLAM systems has made significant progress. In its early stages, the MonoSLAM system, employing an Extended Kalman Filter (EKF) and Shi-Tomasi feature points [13], pioneered image tracking for monocular SLAM [14]. Despite its simplicity, this method had low efficiency and posed challenges in accuracy improvement. The PTAM system, utilizing a different strategy, optimized only keyframes with Bundle Adjustment (BA) and separated tracking and mapping tasks into different threads, achieving higher precision and laying the foundation for BA-based SLAM research [15]. Given the high dependency of pure visual SLAM on image quality and feature points, researchers have explored combining visual SLAM with other sensors, like the IMU. VIN-Mono, by tightly coupling visual and IMU information, achieved a significant improvement in accuracy and robustness [2]. ORB-SLAM3 integrated various research ideas, introduced multiple data association patterns, and implemented the first complete multi-map system [16]. This enhancement improved the performance of loop detection and relocation. The ORB-based approach demonstrated efficiency and robustness [17]. However, challenges remain, including the reduced matching performance of manually extracted feature points in sparsely textured or dynamically changing scenes, and the limitations of bag-ofwords methods in handling complex environments [4]. Additionally, the impact of feature points from dynamic objects on tracking performance remains a concern. In response to these challenges, research into visual SLAM and dynamic SLAM systems leveraging deep features has attracted significant attention.
## 4 Experiments
### Performance of Evaluation Metrics  
The evaluation metrics used in this study played a critical role in assessing and optimizing system performance. Among these, the most impactful was the **weighted consistency metric**, which served as an indicator of agreement among model outputs.  
Figure 2: Composite accuracy across 20 evaluation batches. The steady increase highlights the effectiveness of dynamic weighting.  
Higher consistency values directly correlated with improvements in composite accuracy, as they reflected better alignment among models in their predictions.  
Figure 3 illustrates the dynamic adjustment of model weights across 20 batches. Model 3 consistently maintained the highest weight due to its superior historical performance, while Model 1 exhibited significant fluctuations in weight. This dynamic adjustment mechanism ensured that the system prioritized models with higher reliability, thereby enhancing overall accuracy. The logarithmic weight update formula amplified this effect, providing a balance between responsiveness to performance changes and stability in model contributions.  
Another important observation was the interplay between weight distribution and consistency. When weights were too heavily skewed toward a single model, consistency values dropped, leading to a temporary decline in system accuracy. This underscores the need for balanced contributions from multiple models to achieve optimal collaboration. Adjusting the weight decay rate and thresholds for updates could further improve the system's ability to maintain high consistency without compromising individual model contributions.
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 4 IMM-TPMB Filter Driven by Model and Data Collaboration
### Update
According to Equation (\ref{eq:P1}), calculate the partial derivatives of the function \(\Pi\) with respect to \(Q_{k}\) and \(R_{k}\), where the extremum points are as follows:  
\[\left\{ \begin{array}{l}Q_{k}^{\prime}=\frac{1}{k}\sum\limits_{m=1}^{k}\left\{ (\hat{x}_{m}-f(\hat{x}_{m-1}))(\hat{x}_{m}-f(\hat{x}_{m-1}))^{\text{T}}\right\} \\ \text{R}_{k}^{\prime}=\frac{1}{k}\sum\limits_{m=1}^{k}\left\{(z_{m}-h(\hat{x}_ {m}))(z_{m}-h(\hat{x}_{m}))^{\text{T}}\right\}\end{array} \right. \tag{74}\]  
According to the calculation process of the ASCKF, \(f(\hat{x}_{m-1})\) and \(h(\hat{x}_{m})\) can be represented as follows:  
\[\left\{ \begin{array}{l}Q_{k}^{\prime}=\frac{1}{k}\sum\limits_{m=1}^{k}\left\{ \left(\hat{x}_{m}-\frac{1}{2n}\sum\limits_{i=1}^{2n}f\left(\hat{x}_{i,m-1|m-1} \right)\right)\left(\hat{x}_{m}-\frac{1}{2n}\sum\limits_{i=1}^{2n}f\left(\hat{x }_{i,m-1|m-1}\right)\right)^{\text{T}}\right\}\\ \text{R}_{k}^{\prime}=\frac{1}{k}\sum\limits_{m=1}^{k}\left\{\left(z_{m}-\frac{1 }{2n}\sum\limits_{i=1}^{2n}h\left(\hat{x}_{i,m|m-1}\right)\right)\left(z_{m}- \frac{1}{2n}\sum\limits_{i=1}^{2n}h\left(\hat{x}_{i,m|m-1}\right)\right)^{ \text{T}}\right\}\end{array} \right. \tag{75}\]  
By solving the first-order partial derivatives of the function \(\Pi\) with respect to \(Q_{k}^{\prime}\) and \(R_{k}^{\prime}\), a suboptimal estimate of the unknown covariance matrices of process and measurement noise can be obtained, which is then incorporated into the nonlinear filter ASCKF for recursive computation. Table 1 gives the algorithm process. Combined with the algorithm proposed in Section 3, this method does not depend on noise statistics and exhibits good robustness. Usually, the detection probability and clutter rate are two important factors that affect tracking performance during the process of target estimation. Therefore, we analyze measurement noise and process noise individually, and then obtain the modification of \(Q^{\prime}_{k}\) and \(R^{\prime}_{k}\) in the ASCKF. Both the over-estimation and the under-estimation derived from the factors can be corrected. On the other hand, the IMM filtering framework ensures the motion switching between two continuing dynamics, which can distinguish the close targets. Especially, these targets can be further distinguished by using the proposed filter based on the novel filtering mechanism. This algorithm replaces the covariance matrix recursion of the CKF with the square root form of the covariance matrix, ensuring the efficiency of the filtering process while avoiding numerical instabilities caused by rounding errors, thus enhancing the precision and stability of the filtering algorithm. Moreover, ensuring the positive definiteness of the covariance matrix enhances its numerical stability. The performance of the proposed method, compared to CKF and UKF, features smaller estimation errors and faster convergence rates, and it effectively tracks the motion state of maneuvering targets, enhancing the system's accuracy.
# Adaptive Attention-Enhanced Yolo for Wall Crack Detection
## 5 Conclusions  
The appearance of wall cracks may not only lead to the degradation of the building structure, but also shorten the service life of the building and pose a threat to the safety of the residents. Therefore, the detection and repair of wall cracks is essential to ensure the safety of buildings, not only to avoid potentially major accidents, but also to promote the continued development of the construction industry. With the rapid development of science and technology, DL has been widely used in many fields. In this context, the research of this paper relies on deep learning technology, namely the AAEY method, to detect wall cracks, aiming to extract crack features and identify locations through this technology, so as to achieve an efficient wall crack detection system.  
In this study, the Yolo model underwent a series of enhancements and improvements. First, by incorporating Swin Transformer's MSA mechanism into the bottleneck part of the network backbone, the model is able to better capture remote association information in the image. Second, the ARF module is introduced to the Neck stage to enable more comprehensive feature extraction, from detail to the global scope. At the same time, the MHA structure of the prediction stage is introduced to flexibly adjust the focus between different features. These improvements resulted in a new AAEY model with significantly enhanced feature extraction capabilities and improved accuracy in crack identification. The experimental results suggest that these changes enable the model to accurately capture crack characteristics under various conditions, improve the performance of YOLOv5 in crack detection, and provide an effective solution in the field of wall crack detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1-Score** \\ \hline LightGBM & 68.01\% & 75.78\% & 69.50\% \\ CrackTree & 73.22\% & 76.45\% & 70.80\% \\ CSN & 83.40\% & 68.55\% & 75.24\% \\ FCN & 81.73\% & 78.97\% & 79.95\% \\ Ours AAEY & 88.69\% & 80.18\% & 84.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with existing methods.  
**Author Contributions:** Conceptualization, J.L. and Y.C.; methodology, J.L and Y.C.; software, Y.C.; validation, W.W.; formal analysis, Y.C. and W.W.; investigation, J.L. and Y.C.; resources, J.L.; data curation, Y.C.; writing--original draft preparation, Y.C. and W.W.; writing--review and editing, J.L. and Y.C.; visualization, Y.C. and W.W.; supervision, Y.C.; project administration, J.L. 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. The data are not publicly available due to privacy concerns.  
The authors declare no conflicts of interest.
# Understanding the Influence of the Head Coach on Soccer Training Drills--An 8 Season Analysis
###### Abstract  
Soccer players perform a variety of training drills to develop the physical, technical and tactical qualities required for match-play. The role of coaches in prescribing training suggests that players may not always meet physical targets set by conditioning staff. To quantify the physical outputs elicited by different training drill types, 183 professional soccer players were monitored over 8 seasons using Microelectromechanical Systems during normal training, yielding 65,825 drill observations [362 \(\pm\) 341 observations-player\({}^{-1}\)]. Linear mixed models assessed the influence of drill type, head coach and playing position on physical output. Drills lasted \(\sim\)14 min, eliciting total distances and high speed running of \(\sim\)1000 m and 40 m, respectively. Conditioning drills elicited substantially greater relative high-speed running [18.8 \(\pm\) 27.2 m.min\({}^{-1}\)] and Sprint [3.5 \(\pm\) 9.4 m.min\({}^{-1}\)] distances than all other drill types. The proportion of training drill types used and external outputs elicited per drill were affected by the head coach. Midfielders recorded the highest total distance [77.3 \(\pm\) 36.1 m.min] and PlayerLoad\({}^{\text{\tiny{nt}}}\) [8.29 \(\pm\) 3.54] of any playing position, whilst the lowest outputs were recorded by goalkeepers. This study provides reference data for practitioners when seeking to manipulate training prescription to achieve physical output targets whilst also meeting the team's technical and tactical objectives.  
football; monitoring; training load; coaching staff; drill categories +
Footnote †: journal: Applied Sciences
### Related Work
The IRS-assisted D2D communication provides the advantage of improved Signal-to-Interference Noise Ratio (SINR) and extends the coverage area compared to traditional D2D communication [27]. Most conventional studies implement IRS reflection coefficients with continuous phase shifters, which is expensive and difficult to realize due to hardware limitations. It is more convenient to use IRS phase shifts with the finite resolution, which may reduce signaling overheads and energy consumption when acquiring instantaneous channels [28]. An analysis of the trade-off between spectral efficiency and energy efficiency in a multiuser multiple input multiple output (MIMO) UL system supported by IRS with discrete phase shifters is presented in [29]. To maximize resource efficiency, the precoding matrix at users and the beamforming matrix at IRS are optimized jointly. The discrete IRS phases are constructed employing an iterative mean square error minimization method [30]. The authors of [31] demonstrated that the difference in sum rate performance between a continuous phase shifter and a 1-bit discrete phase shifter is negligible. In [32], an IRS prototype at 28 GHz is realized using a two-bit discrete phase shifter. The analysis of the impact of discrete phase shifters on BER and outage performance would be more beneficial for design and practicing engineers in the field of 6G wireless communications [7]. Motivated by the above, in this paper, an IRS-assisted bidirectional FD-MTC system is proposed. In comparison with related works, this paper is the first to analyze the performance of the IRS-assisted bidirectional FD-MTC system. The major outcomes of this paper are highlighted below:
* Analytical expression is derived for the outage probability and BER of the bidirectional FD-MTC system assisted by an ideal continuous phase shifter at IRS. The analytical derivations are confirmed by an MC simulation. The performance is also compared with the IRS-assisted bidirectional HD-MTC system. * A detailed investigation on the outage and BER of the bidirectional FD-MTC system is carried out for non-ideal cases of the discrete phase shifter (1-bit, 2-bits, and 3-bits) at IRS. * Further, the analysis is carried out for the achievable rate in various locations of the IRS in a bidirectional FD-MTC system to determine the optimal location of the IRS. The paper is organized as follows: the system model of the proposed IRS-assisted bidirectional FD-MTC system is described in Section 2. The performance analysis of the proposed system in terms of outage probability, BER, and achievable rate is investigated in Section 3. In Section 4, the analytical results are verified using MATLAB simulation and we discuss the inference of the proposed system. Finally, Section 5 concludes this paper.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.2 Environmental Data  
The environmental data used in this study mainly comprised climatic data and marine hydrographic data, including maximum air temperature (Tmax), mean air temperature (Tem) [65], sea surface temperature (SST) [66], vapor pressure deficit (VPD) [67], sea water salinity (SAL) [68], and photosynthetically active radiation (PAR). Tmax and Tem were sourced from the ERA5 daily air temperature products developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). The daytime air temperature (Tair) was the average of the two. The SST data were also sourced from ECMWF. PAR was obtained from the Global Land Surface Satellite (GLASS). VPD was derived from the TerraClimate dataset, which was created using climatically aided interpolation, combining normal high-spatial-resolution climatological data from historical datasets. We used the Google Earth Engine (GEE) to obtained Hybrid Coordinate Ocean Model (HYCOM) SAL data. HYCOM is a data-assimilative numerical ocean simulation model. This study only utilized the sea surface water salinity data. To match the spatial resolution of the fAPAR from Sentinel-2 images, all the environmental factors were interpolated using the ordinary kriging interpolation. The datasets used in this study are shown in Table 1.
# Analysis of the Transonic Buffet Characteristics of Stationary and Pitching OAT15A Airfoil
## 4 Conclusions  
Numerical simulations were conducted to compute a transonic unsteady flow field around an OAT15A airfoil that was in stationary and harmonically pitching conditions in a transonic buffeting flow, using the URANS method with the sst-k\(\omega\) model. Velocity divergence snapshots and the DMD method were employed to investigate the unsteady aerodynamics and the feature of the flows. The main conclusions are as follows:  
1. When the airfoil is in pitching motion, the oscillation frequency has a significant effect on the flow characteristics. If the pitching frequency is close to the buffeting frequency of the flow, the aerodynamics may manifest as a harmonic vibration with only the pitching frequency of the structure as the fundamental frequency, and the frequency of the flow field disappears.
2. Velocity divergence snapshots show that for a given pitching motion, compared with the corresponding buffet characteristics of the stationary airfoil, the interaction of two shocks exists in the former, which leads to an increased range of shock motion. The wake shows obvious disordered motions compared with that of the latter, which results in a more complex unsteady shock behavior of the flow under elastic oscillation.
3. The DMD modes extracted by ordering the modes at different frequencies according to the modal energy criterion can obtain the principal modes, which reflectthe periodicity of the transonic shock oscillations, and facilitate the study of the dominant mode characteristics in the periodic flow.  
Conceptualization, X.N. and G.Z.; methodology, X.N. and L.W.; software, X.N. and C.H.; validation, G.Z., G.Y. and Z.J.; formal analysis, X.N. and Z.J.; investigation, G.Y.; resources, X.N.; data curation, X.N. and L.W.; writing--original draft preparation, X.N.; writing--review and editing, X.N. and C.H.; supervision, G.Z.; project administration, G.Y.; funding acquisition, C.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation (No. 12302230).  
Not applicable.  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
## 2 Methodology
### Dataset
#### 2.1.5 Digital Elevation Model  
We used topographic data from the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). GMTED2010 is a global continental-wide elevation dataset produced by the U.S. Geological Survey (USGS) and the U.S. National Geospatial-Intelligence Agency (NGA). It features improved vertical accuracy compared to the GTOPO30 dataset. It has several resolution levels: 30-arc-second (1 km), 15-arc-second (450 m), and 7.5-arc-second (225 m). The data can be accessed via the Earth Explorer site: [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 1 January 2021). To match the spatial resolution of GOSIF data, the GMTED2010 1 km data were resampled to a 0.05\({}^{\circ}\) spatial resolution.
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 4 Experimental Results and Discussions
### FFT Analysis Processing
Comparing Figure 14a with Figure 14b, it can be concluded that the computing time of FFT operation based on the CPU is much longer than that based on the GPU when the point number of FFT analysis and the length of optical fiber is invariable. When the length of optical fiber is 2020 m and the point number of FFT is 8192, the consumption time is 1.17 ms with FFT analysis performed on the GPU. Under this experimental condition, it takes about 119.5 ms with FFT analysis performed on the CPU, so the speed of using GPU is approximately 140 times faster than that of traditional series computation based on a CPU in FFT analysis processing. It can be noted that a parallel computation method based on the GPU can shorten substantially the time of FFT analysis processing and optimize the real-time performance of the \(\Phi\)-OTDR sensing system.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 4 Modeling the Performance of Bluetooth Low Energy for Opportunistic Sensor Data Collection
### Connection-Based Approach
#### 4.2.2 Maximum Amount of Collected Data per Contact Interval  
We next model the maximum amount of data, denoted \(L_{\mathit{{data\_com}}}\), that the mobile entity may collect from the sensor node within a contact interval in the connection-based approach.  
Let \(L_{\mathit{{data\_payload}}}\) be the maximum payload size of a data channel packet. Taking into account that only an integer number of round trip exchanges may be performed in each period of _comInterval_ size, and considering also that a number of round trip exchanges may be carried out as well in the last connection event (which may have a shorter duration than _comInterval_), \(L_{\mathit{{data\_com}}}\) can be computed as follows in absence of bit errors:  
\[L_{\mathit{{data\_com}}}=\bigg{(}\bigg{\lfloor}\frac{\mathit{{connInterval} }}{T_{RT}}\bigg{\rfloor}\cdot\lfloor N_{CE}\rfloor+\bigg{\lfloor}\frac{(N_{CE }-\lfloor N_{CE}\rfloor)\cdot\mathit{{connInterval}}}{T_{RT}}\bigg{\rfloor} \bigg{)}\cdot L_{\mathit{{data\_payload}}} \tag{23}\]  
In a more general scenario where bit errors can take place, and assuming that bit errors are uncorrelated, the previous expression can be rewritten as follows:  
\[L_{\mathit{{data\_com}}}=\big{(}\mathit{{op\cdot}}\lfloor N_{CE}\rfloor+ \lfloor\mathit{{op\cdot}}(N_{CE}-\lfloor N_{CE}\rfloor)\big{\rfloor}\big{)} \cdot L_{\mathit{{data\_payload}}} \tag{24}\]