esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study introduced significant improvements to the Multi-Object Tracking (MOT) algorithm, particularly within panoramic scenes, which have historically posed unique challenges due to the extensive spatial coverage and the possibility of targets crossing scene boundaries. Building upon the robust foundation of YOLOX_s for object detection, and incorporating advanced tracking strategies, the optimized algorithm has notably improved performance metrics such as MOTA, IDF1, and IDSWs.  
The comparison of the proposed algorithm against conventional methods, as illustrated in Figures 7-10, underscores its enhanced ability to maintain continuous tracking of targets even as they pass through challenging scene transitions. This represents a substantial advancement over previous studies, where tracking failures at scene boundaries were a common limitation, leading to loss in target tracking or incorrect identity assignments. Our findings, supported by quantitative improvements in key performance indicators, align with the working hypothesis that targeted enhancements in detection and tracking mechanisms can significantly mitigate the issues associated with panoramic Multi-Object Tracking.  
From a broader perspective, this study contributes to the ongoing evolution of MOT technologies by addressing a critical gap in panoramic scene tracking. It enriches the toolkit available to researchers and practitioners in the field and opens new avenues for real-world applications where seamless object tracking across wide spatial extents is required, such as in urban surveillance, wildlife monitoring, and autonomous navigation systems.  
Looking forward, several directions for future research emerge from this study. Firstly, exploring the integration of semantic segmentation with tracking algorithms could offer deeper insights into scene context, potentially enhancing tracking accuracy in densely populated or highly dynamic environments. Secondly, applying deep learning techniques for adaptive model tuning in real-time tracking scenarios presents a promising area for improving algorithmic efficiency and responsiveness. Lastly, extending this methodology to include aerial or drone-based panoramic footage could broaden the scope of MOT applications, addressing an even wider range of surveillance and monitoring challenges.  
Conceptualization, J.S.; methodology, J.S. and H.Y.; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON] and H.Y.; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, H.Y.; visualization, [PERSON]; supervision, H.Y.; project administration, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Survey of Structure of Atmospheric Turbulence in Atmosphere and Related Turbulent Effects
## 2 Structure of Atmospheric Turbulence in Earth's Atmosphere
### Experimental Studies on Atmospheric Turbulence
showed that non-Kolmogorov turbulence can be considered as coherent structures, and Kolmogorov turbulence could be considered as a mixture of different coherent structures. Coherent non-Kolmogorov turbulence differed from incoherent Kolmogorov turbulence in a more rapid decrease in the one-dimensional spectrum of temperature fluctuations (with exponential value \(-8/3\) instead of \(-5/3\)) in the inertial interval. The results of atmospheric measurements show that the structure of atmospheric turbulence is composed of Kolmogorov turbulence at lower levels and non-Kolmogorov turbulence at higher levels. Thus far, there are two models for the structure of atmospheric turbulence in Earth's atmosphere: (a) A two-layer model, namely, the turbulence in the Earth's atmosphere is composed of Kolmogorov turbulence in the troposphere and \(-5\) non-Kolmogorov turbulence in the stratosphere, constructed by [PERSON] based on observations of early Earth's atmosphere [26]. Later, [PERSON] et al. revised the two-layer model based on the consistency between the refractive index structure function and the power spectral density [27]; (b) a three-layer model, namely, that the turbulence in the Earth's atmosphere is composed of Kolmogorov turbulence in the boundary layer, \(-10/3\) non-Kolmogorov turbulence in the free atmosphere, and \(-5\) non-Kolmogorov turbulence in the stratosphere, which all have the following form:  
\[\Phi_{nB}(x,z)=0.033\ C_{n}^{2}(z)\ \kappa^{-11/3}, \tag{3}\]  
\[\Phi_{nF}(x,z)=0.015\widetilde{C}_{nF}^{2}(z)\ \kappa^{-10/3}, \tag{4}\]  
\[\Phi_{nS}(x,z)=0.0024\widetilde{C}_{nS}^{2}(z)\ \kappa^{-5}, \tag{5}\]  
where \(z\) is a propagation distance that varies between \(z=0\) and \(z=L\), and \(C_{n}^{2}(z)\) is the conventional Kolmogorov turbulent index-of-refraction structure parameter in the boundary layer that is dependent on the altitude and has units of \(m^{-2/3}\), \(\widetilde{C}_{nF}^{2}(z)\) is the non-Kolmogorov index-of-refraction structure parameter in the free troposphere that depends on the altitude and has units of \(m^{-1/3}\), and \(\widetilde{C}_{nS}^{2}(z)\) is a non-Kolmogorov index-of-refraction structure parameter in the stratosphere that is dependent on the altitude and has units of \(m^{-2}\). Here, it is noteworthy that the structure of atmospheric turbulence in the Earth's atmosphere varies with different regions and atmospheric conditions.
## 5 Results and Discussion
Figure 9 displays the groups of the binder model (Figure 3) and compression level. The minimum and maximum values of the LB simulations within the particular groups of data are shown as boxes. Each group of data consists of 25 image series, with numbers of unlabeled data in the groups as follows: three in group A, 40%; five in group A, 50%; eleven in group B, 50%; seven in group C, 50%; one in group D, 20%; one in group D, 30%; eighteen in group D, 40%; and thirteen in group D, 50%. In most of the groups, the predicted permeability of unlabeled micro-structures was within the range of permeabilities calculated earlier by the LB simulation, except for groups D, with 40% and 50%. The latter group suffers from having only being subject to a few successful simulations by [PERSON] et al. [9]. The wider range of predicted permeabilities results from the small amount of labeled data in these two groups. This indicates that the CNN is able to predict consistent permeability values for micro-structures that are not eligible for LB simulations as soon as the material is within the feature range of the training data. In a similar manner, [PERSON] et al. [25] filled missing positions in their membrane database with ML-predicted values. The results show that a CNN is able to accurately predict the permeability of GDLs, with a limited number of geometries used for training. The training data were created by means of a stochastic geometry model that had been validated in previous investigations [9]. The CNN was trained with labeled permeabilities. Additional features--compression level and the binder model--are inherent in the training data but were not labeled in the training phase. With increasing compression, the permeability predicted by the CNN systematically decreases. Moreover, the binder model exhibits a systematic influence on the permeability. Both effects were also reported in previous investigations using the LB method [9; 34]. The impact of these two parameters on the predicted permeability is larger than the deviation of the predicted permeability from that computed with the LB method.
# Intelligent Total Transportation Management System for Future Smart Cities
## 3 Results--Examples of Sub-Model Developments
### Intelligent Total Transportation Managing System in Smart Cities
#### 3.3.3 Contract-Based Traffic Management  
Contract-based traffic management is a supporting service that increases the efficient operation of both drivers and service providers. Accordingly, drivers are the first receiving the essential information. Service providers might also benefit from this service by gaining profit from this contract, allowing for efficient operation increasing supply competition.  
The contract-based traffic management may consist of a \"semi-priority\" system. In such cases, the drivers will have information from the transport management centers on the recommended shortestways, and possibly shorten the traveling time while seeing individual commanding signals on the road for a short time.
## 4 Stability Evaluation and Comparative Analysis of Goafs Based on Field Monitoring
### Pillar Stress Monitoring and Analysis  
Four monitoring points, 1#-1L, 1#-2L, 1#-3L, and 1#-4L, were arranged on the pillar of 1# chamber in the inclined mining area by using a KSE-II-1 borehole stress meter. From the installation of the stress meter to the completion of the monitoring, 11 measurements were made, and the time span was 10 months, which reflects the stress data of the pillar during mining. The sampling data of the stress meter are shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Goaf** & **Stability** & \begin{tabular}{c} **Numerical** \\ **Simulation** \\ **Analysis Level** \\ \end{tabular} & **Final Level** & \begin{tabular}{c} **Goaf** \\ **Number** \\ **Level** \\ \end{tabular} & \
\begin{tabular}{c} **Stability** \\ **Level** \\ \end{tabular} &
\begin{tabular}{c} **Numerical** \\ **Simulation** \\ **Analysis Level** \\ \end{tabular} & **Final Level** \\ \hline CSJ-2\# & III & III & III & XJ-24\# & II & II & II \\ CSJ-3\# & III & II & III & BFZ-2\# & II & I & II \\ CSJ-4\# & III & III & III & BFZ-3\# & I & I & I \\ CSJ-5\# & III & III & III & BFZ-6\# & III & II & III \\ CSJ-6\# & II & II & II & BFZ-8\# & II & II & II \\ CSJ-7\# & II & II & II & BFZ-9\# & III & II & III \\ CSJ-8\# & III & III & III & NCB-3\# & III & II & III \\ CSJ-11\# & III & III & III & NCB-17\# & III & III & III \\ CSJ-2\# & III & III & III & NCB-19\# & II & II & II \\ CSJ-X\# & III & III & III & NCB-8\# & I & I & I \\ XJ-1\# & II & II & II & NCB-10\# & I & I & I \\ XJ-2\# & II & II & II & DCJ-1\# & II & I & II \\ XJ-3\# & II & I & II & DCJ-3\# & III & II & III \\ XJ-4\# & III & III & III & DCJ-6\# & II & I & II \\ XJ-39\# & III & II & III & F18N-10\# & III & II & III \\ XJ-40\# & III & III & III & F18N-12\# & II & I & II \\ XJ-7\# & I & I & I & F18N-13\# & II & I & II \\ \hline \end{tabular}
\end{table}
Table 9: Comparison of the results of two evaluation methods.  
Figure 11 is the trend diagram of the data obtained from the four stress gauges. According to the analysis of stress monitoring data, the stress of horizontal surrounding rock is 1-2 MPa. With the increase of mining height, pillar stress begins to increase gradually and becomes stable near the end of mining. Generally, the surrounding rock of the roadway of Shiregou Iron Mine has satisfactory stability and a smooth stress curve.
## 3 Methods
### Refinement of the 3D Box  
When MultiFusion [36], PatchNet [12], etc., feed the generated pseudo-lidar to the CNN in the form of the XYZ map for the feature extraction, operations such as convolution and pooling will continuously reduce the resolution of the feature map, resulting in the loss of some key information, which seriously affects the precision of the algorithm. Considering the above problems, when we feed the data fused with pseudo-lidar and image information into the CNN for the keypoint-based initial 3D box estimation, there is another data processing method of the pseudo-lidar. We project the pseudo-lidar onto the bird's-eye view and use the bird's-eye view features to refine the initial 3D box on the XZ plane, which compensates for the information lost in the keypoint estimation branch to a certain extent.  
The method of generating a bird's-eye view refers to AVOD [11]. We clip the pseudo-lidar to the \([-40,40]\times[0,70]\) range of the XZ plane to include all the points in the camera's field of view and then divide the grid evenly in the XZ plane, finally filling each grid with the maximum height of the point clouds as the content of the grid, and a bird's-eye view containing direct spatial information is obtained. We take the center point of the initial 3D box as the reference point, enlarge its length, width, and height to double the original box to obtain a larger 3D box, and then project the larger 3D box onto the bird's-eye view to obtain the region bird's-eye view, finally feeding it into a deep neural network for the information's extraction, estimating the offsets \(x,y,z,l,w,h,\theta\) of the initial 3D box. At last, these offsets and the parameters of the initial 3D box are added together to obtain a more accurate 3D box.  
Compared with Decoupled-3D [42], MultiFusion [36], PatchNet [12], and other algorithms that also introduce extra depth information for detection, Decoupled-3D [42] uses the bird's-eye view generated by the pseudo-lidar for the refinement of the initial 3D box, lacking the direct use for the pseudo-lidar stereo information. MultiFusion [36] and PatchNet [12] do not utilize the bird's-eye view information to compensate for the lost information of the pseudo-lidar in the keypoint estimation branch. On the basis of a single image, for the first time, we gradually fuse the image, point cloud, and bird's-eye view, three different modal data to fully extract the rich information of the original image, which achieves the goal of using the different modal information of the original image to detect and refine the 3D box.  
Loss function of the refinement stage:  
\[L_{2}=\text{smooth}L1(L_{ang}+L_{\text{dim}}+L_{poz}) \tag{11}\]  
The loss function of the refinement stage uniformly adopts a smooth\(L1\) loss function to calculate the loss of each residual amount, where \(L_{ang}\) represents the angle offset estimation loss, \(L_{\text{dim}}\) represents the dimension offset estimation loss, and \(L_{poz}\) represents the position offset estimation loss.
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 3 A Network Traffic Prediction Framework Incorporating Prior Knowledge
### Prediction Framework  
The network traffic prediction model with fused prior knowledge, which we call FPK-Net, consists of three main components: a traffic characterization module, a traffic feature extraction module, and a fused prior knowledge module. Among them, the flow characterization module consists of an R/S analysis to calculate the Hurst exponent, which is used to measure the self-similarity of flow sequences with different step sizes. The flow feature extraction module consists of one-dimensional convolution to extract the features of the flow sequences, and the fused prior knowledge module takes the parameters derived in the flow characterization module and incorporates them into the long short-term memory gating mechanism. The LSTM extracts coarse-grained features from the fine-grained features extracted from the front-end, refines the processing of different dimensional features to a certain extent, and can avoid memory loss and gradient dispersion caused by an excessively long step length. When the CNN is combined with LSTM, the short-term features of the time-series are ignored; thus, an attention mechanism is added to the CNN-LSTM model to expand the perceptual field of the input and perceive information before and after the time-series comprehensively. The attention mechanism improves the influence of the temporal features while reducing the influence of unimportant features in the model. The CNN-LSTM model based on the attention mechanism is used to fuse coarse- and fine-grained features to comprehensively portray the temporal data. Figure 2 illustrates the general framework of the fused prior knowledge network traffic prediction model (FPK-Net) proposed in this paper.
## 2 Materials and Methods
### Machine Learning Algorithms
#### 2.3.1 Gaussian Mixture Model (GMM)  
The Gaussian Mixture Model (GMM) algorithm is a probabilistic algorithm used to represent normally distributed subpopulations within a general population. Typically, it is employed for unsupervised learning to automatically learn subpopulations and their assignment. It is also used for supervised learning or classification to learn the boundaries of subpopulations.  
The Gaussian Mixture Model starts with the means of each Gaussian being equal to the means of each actual class, as well as their standard deviations. Each potential class is represented by a Gaussian function. This representation is iteratively refined, or trained, by the Expectation Maximization algorithm. Instance labels are assigned according to the Gaussian that best represents each one. The Gaussian Mixture Model is a weighted sum of Gaussian densities with M components, as follows:  
\[\mathrm{p}(\mathrm{x}|\lambda)=\sum_{i=1}^{M}\omega_{i}\mathrm{g}\big{(} \mathrm{x}\,|\mu_{i},\sum i\big{)} \tag{1}\]  
where \(\mathrm{x}\) corresponds to a vector of continuous data with D dimensions, \(\omega_{i}\) (for i ranging from 1 to M) represents the mixture weights, and \(\mathrm{g}\) (\(\mathrm{x}\,|\,\mu i\), \(\Sigma i\)) with (i ranging from 1 to M) represents the Gaussian densities of the components. Each component density takes the form of a Gaussian function with D variables.  
\[\mathrm{g}\big{(}\mathrm{x}\,|\mu_{i},\sum i\big{)}=\frac{1}{(2\pi)^{\frac{D}{2 }}|\sum i|^{\frac{1}{2}}}\exp\,\{-\frac{1}{2}(\mathrm{x}-\mathrm{u}_{i})^{ \prime\sum_{i}^{-1}(\mathrm{x}-\mathrm{u}_{i})}\} \tag{2}\]  
where, the mean vector: \(\mu_{i}\), and the covariance matrix: \(\sum i\).  
The model assumes that all data points are obtained from a mixture of finite Gaussian distributions with unknown parameters [32].
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 3 Deep Learning-Based Decoder  
Deep learning-based decoders have recently presented in [13; 14; 16; 17; 18]. The key approaches focused on the frameworks of binary classification [13; 16; 17; 18] and denoiser [14]. The approach of binary classification utilizes the DNN to recognize 0 or 1 for each received bit, where the soft information (reliability) is applied. The second one adopts the NN to remove the noise from the received signal, thereby improving the accuracy of the error-correcting process. This framework is a cascade of a denoiser and the conventional HDD.
## 4 The Considered Nodes' Synchronization and Data Transmission Protocol
### Results and Comparisons
This is due to the fact that in order to minimize the average consumed battery power it might be convenient to increase the battery capacity depletion of a node, e.g., by increasing its average idle listening period. In order to better highlight the _lagging-behind_ effect in the non-optimized case, we report in Figure 10 the optimized and non-optimized \(R_{s}\) for \(N=50\). It can be observed that the optimal \(R_{s}\) increases more than linearly, and the difference between the optimal and non-optimal \(R_{s}\) increases with the node order. As further consideration, it is worth noting that the optimization scheme will inevitably increase the time needed for achieving synchronization, that is not however a critical parameter in the scenario at hand. As final results, we report in Figure 11a,b the total battery capacity depletion for \(p_{tx}=0.5\) and for \(T_{A}=T_{P}\). This choice for the timing advance has been verified to conservatively compensate for possible small clock offsets accumulated during the short sleeping period. As for \(\Delta_{S}\), this parameter is set following the procedure described in Section 4.4 by setting \(P_{S}=0.995\) which yields, for the scenario at hands, \(\Delta_{S}=30T_{P}\). The average battery capacity depletion \(Q_{tot,s}=Q_{synch,s}+Q_{data,s}\) evaluated as in (13) is compared with actual total average capacity depletions derived through simulations for both the optimized and non optimized SYNCH procedures. The cases \(N=10\) and \(N=20\) are shown in Figure 11a,b, respectively, showing an almost perfect matching between analytical and simulation results. A similar behavior is observed for different parameters setting. The actual probability \(P_{O}\) of missing a packet in the transmission chain due to TX/RX misalignment has been estimated through simulations as \(P_{O}\leq 0.05\) in all cases, in line with the imposed maximum \(P_{S}=0.995\). Figure 8: Per-node and average battery capacity depletion during SYNCH operations for the optimized and non-optimized schemes for \(N=5\) (**a**) and \(N=10\) (**b**). Figure 10: \(R_{s}\) values for the optimized and non-optimized schemes for \(N=50\). Figure 9: Per-node and average battery capacity depletion during SYNCH operations for the optimized and non-optimized schemes for \(N=20\) (**a**) and \(N=50\) (**b**).
# The Melt Enthalpy of Pu\({}_{6}\)Fe
## 2 Original Analysis  
The DSC heating cycle used in the STR analysis is reproduced in Figure 1. The solid line is the data, and dashed lines are \"baselines\" added for the purpose of integrating the area under peaks associated with phase transitions. The \(\alpha\to\beta\) and \(\gamma\to\delta\) transitions in pure Pu are readily apparent at approximately 350 and 595 K, as is the prominent melt feature at the eutectic temperature of \(T_{\rm eu}\approx 685\) K. Through comparison of theirmeasured \(\alpha\rightarrow\beta\) enthalpy change with a reference value of \(\Delta H_{\alpha\rightarrow\beta}=15.7\) J/g, STR inferred that 12 wt% of their \(m=29.7\) mg sample was pure Pu. This translates to a mass \(m_{\text{Pu}}=3.56\times 10^{-3}\)g, or \(n_{\text{Pu}}=1.49\times 10^{-5}\) mol. Similarly, \(m_{\text{Pu}_{6}\text{Fe}}=2.61\times 10^{-2}\) g or \(n_{\text{Pu}_{6}\text{Fe}}=1.75\times 10^{-5}\) mol. The atom percentage of Fe was then a/o Fe = 12.7. This is less than the ideal Pu\({}_{6}\)Fe composition of 14.3 a/o Fe, as anticipated based on the Pu transitions visible in Figure 1.  
STR's best estimate of raw heat absorption at melt was \(Q\sim 27.5\) J/g, from which they derived a Pu\({}_{6}\)Fe melt enthalpy of  
\[\Delta H_{\text{fus}}^{\text{Pu}_{6}\text{Fe}}=\frac{A_{\text{Pu}_{6}\text{ Fe}}}{y_{\text{Pu}_{6}\text{Fe}}}Q=46.6\text{ kJ/mol}, \tag{1}\]  
where \(A_{\text{Pu}_{6}\text{Fe}}=1490.56584\) (g/mol) is the molar mass of Pu\({}_{6}\)Fe and \(y_{\text{Pu}_{6}\text{Fe}}\) is the mass fraction of Pu\({}_{6}\)Fe in the mixture. This is equivalent to 6.67 kJ/(mol atoms), or 31.2 J/g. The logic of Equation (1) is simple and intuitive: \(\Delta H_{\text{fus}}^{\text{Pu}_{6}\text{Fe}}\) is the heat absorbed at \(T_{\text{eu}}\), after correction for the excess Pu on a per mass basis. The excess Pu itself is a thermal spectator, and the steadily accumulating liquid does not absorb additional heat.