esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Cross-Frame Association Module
#### 3.3.3 Memory Recall Mechanism  
To address feature degradation caused by short-term occlusions, we designed a dynamic memory pool and its update rules. For each tracked target, a memory pool \(\mathcal{M}_{j}^{t}\) containing the features of the last three frames is maintained as follows:  
\[\mathcal{M}_{j}^{t}=\begin{cases}\begin{bmatrix}f_{j}^{t-1},\,\mathcal{M}_{j}^ {t-1}(1),\,\mathcal{M}_{j}^{t-1}(2)\end{bmatrix},&\text{if target $j$ is successfully matched at frame $t-1$},\\ \begin{bmatrix}f_{j}^{t-1},\,\mathcal{M}_{j}^{t-1}(0),\,\mathcal{M}_{j}^{t-1}( 1)\end{bmatrix},&\text{if target $j$ fails to match at frame $t-1$}.\end{cases}\end{cases} \tag{14}\]  
Here, \(\mathcal{M}_{j}^{t}(k)\) denotes the feature vector at the \(k\)-th position in the memory pool, with \(k=0\) representing the most recent feature.  
When a target enters an occlusion state, a single frame's feature may be insufficient to provide reliable association information. In such cases, a weighted historical feature is used to calculate comprehensive similarity as follows:  
\[S_{\text{motion}}(i,j)_{\text{occl}}=\sum_{k=0}^{2}\gamma_{k}\cdot S_{\text{ cos}}(K_{j},\mathcal{M}_{j}^{t}(k)),\quad\gamma_{k}=0.85^{k} \tag{15}\]  
Here, \(S_{\text{cos}}\) denotes the cosine similarity function, and \(\gamma_{k}\) is the decay weighting coefficient, which exponentially decreases with increasing temporal distance. The choice of the weighting coefficient \(\gamma_{k}\) is based on experimental validation, with 0.85 as the base, demonstrating optimal performance across multiple datasets. This coefficient strikes a balance between emphasizing the most recent features and not overly diminishing the contribution of historical information.  
The proposed decay weighting design effectively balances the importance of recent and historical features, particularly mitigating challenges associated with short-term occlusions (typically spanning 1-3 frames). In contrast to methods employing simple averaging or exclusively relying on the most recent features, our memory recall mechanism substantially enhances target re-identification rates, thereby improving the system's robustness under occlusion conditions.  
Through the comprehensive design of the Cross-Frame Association Module, we have successfully achieved robust target tracking in complex visual environments, demonstrating exceptional performance in occluded and densely populated scenes. Experimental results validate that, compared to existing approaches, this module significantly improves tracking accuracy and continuity while maintaining computational efficiency.
## 5 Conclusions  
The article examines the issue of proving the existence of solutions to the problems of minimizing the energy consumption of a sensor network by adjusting the coverage radii and minimizing the cost of the network while maximizing the coverage area. These problems are formulated in the form of optimization problems, so proving the existence of a solution is a necessary condition for using the proposed approaches. The proof of the existence of solutions is based on the use of the Weierstrass and Kuhn-Tucker theorems. The case of data exchange between network elements is considered, the main condition of which is the intersection of sensor coverage zones. An analysis of the effectiveness of the proposed approach in the case of different values of the intersection level of the coverage zones was carried out. Based on the analysis of the energy consumption function, it was concluded that the energy consumption function forms a plane in the n-dimensional Euclidean space, where n is the number of sensors in the network. The values of the squares of the optimal coverage radii are located at the intersection of this plane with the plane equal to the optimal value of energy consumption. This statement is illustrated in the case of a network consisting of two sensors. With the use of the presented methods, the energy efficiency of a particular network increased by 7%, which confirms the effectiveness of the proposed methods.  
The paper presents a Big Data good practice related to designing sensor network with optimal coverage and energy consumption. The work was fulfilled within the framework of the project iBigWorld [20], which is devoted to gaining skills for developing innovative solutions based on Big Data in real-world applications.  
Conceptualization, O.B. and V.M.; methodology, V.P.; software, V.P.; validation, O.B. and V.M.; formal analysis, V.P.; investigation, O.B.; resources, V.P.; data curation, V.M.; writing--original draft preparation, V.P., O.B. and V.M.; writing--review and editing, V.P., O.B., and V.M.; visualization, V.P.; supervision, O.B.; project administration, V.P. and O.B; funding acquisition, V.M. All authors have read and agreed to the published version of the manuscript.  
The work was co-funded by the European Union's Erasmus + Programme for Education under KA2 grant (Project no. 2020-1-PL01-KA203-082197 \"Innovations for Big Data in a Real World\"). The APC was funded by the University of Bielsko-Biala.  
Not applicable.  
The authors thank reviewers for comments that allowed to improve the paper significantly.  
The authors declare no conflict of interest.
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Carbon Estimated in Burned** & **Carbon Estimated by GFED** & **Carbon Estimated by GFED** & **Correlation 1 (2)** \\  & **Areas in Vegetation** & **in All Classes** & **in Forest Area** & **Correlation 1 (2)** \\ \hline
2011 & 6.1 & 12.1 & 7.0 & 0.52 \\
2012 & 4.3 & 3.3 & 0.8 & 0.11 \\
2013 & 7.7 & 13.5 & 7.8 & 0.12 \\
2014 & 0.8 & 1.8 & 0.3 & 0.24 \\ \hline \hline \end{tabular}  
* Polynomial correlation. We considered all cells of 10 \(\times\) 10 km. GFED: Global Fire Emissions Database.  
\end{table} 
Table 7: Emissions of carbon (Tg). number of previous works [20; 21; 22]. The results of those three works are similar to each other (9.6 Tg, on average) but lower than those of this study. We found that the burned area parameter had a large influence. CONAFOR statistics recorded less than twice the burned area identified by MCD64A1 Collection 6.0, and this can be explained by the discrepancy in the definition of forest fires. Many fires involving vegetation are associated with the clearing of agricultural fields and with forestry operations; these are not considered uncontrolled fires, and hence are not registered as forest fires, but they are still recorded by MCD64. Hence, it is necessary to identify whether the emissions come from a forest fire or from a change in the land use [57]. Another parameter with an influence on the differences was the biomass. When only the biomass map was considered, without the annual update of NPP, the calculated mean of the emissions fell from 16.8 Tg to 12.1 Tg; even so, this is still a high value. The spatial and temporal distributions of emissions in the study area were associated with the distribution of precipitation, drought, and human activities. There were two very important elements in the emissions distribution: hydrological drought and biomass. The values of the estimated emissions were expected to be low because the estimates of the burning efficiency were small compared with those of other studies. However, the emissions were higher, owing to the inclusion of different inputs, such as a yearly update of the biomass and burned areas. Therefore, it is important to consider the changes over time in the variables and the contribution of satellite data, e.g., the observation of the phenomenon on the day of occurrence. In this work, we considered drought conditions by using the fuel moisture model and the annual update of the biomass.
## 4 Prediction of Water Inrush Risk Based on PMGIM
### Electrical Survey  
The network parallel electrical method is founded on analyzing and interpreting apparent resistivity data of rock mass, where areas of low resistivity typically denote water-rich anomalies or water-bearing structures. Figure 12 depicts the results of parallel electrical geophysical exploration on the 182602 working face. Along the strike, four anomalous areas (AA1, AA2, AA3, and AA4) were identified in the outer section of the working face, confirmed later by drilling to have significant water presence, closely matching the  
Figure 11: Regional division of PMGIM distribution types in working face.  
high-risk zones for severe water inrush identified by [PERSON]'s index analysis. Abnormal microseismic events persisted in area AA4 during mining operations, escalating the risk of water inrush. Subsequently, a drainage borehole was constructed, discharging water at a maximum rate of 130 m\({}^{3}\)/h to prevent water inrush. This underscores the primary PMGIM event as pivotal in delineating areas prone to water inrush.
# An Efficient AdaBoost Algorithm with the Multiple Thresholds Classification
## 1 Introduction
Furthermore, an analysis of the diversity of sub-classifiers generated by the AdaBoost algorithm found that in the previous iterations [29; 30], each new sub-classifier focused on samples that were difficult for the previous sub-classifier to classify correctly, so there was a high diversity. However, after several iterations, samples that are difficult to classify correctly are likely to always be misclassified, resulting in a sharp decline in diversity as the generated sub-classifiers pay more and more attention to the same batch of samples that cannot be classified correctly. Although many scholars have made significant contributions in improving the accuracy and generalization rates in previous work, the work has mostly focused on modifications to the algorithmic framework, while few scholars have focused on the tuning of the Weak Learn algorithm itself. In this paper, we solved the problem of low accuracy and weak generalization of the AdaBoost algorithm when using the threshold classification method as a weak classifier. Our learning model faces two challenges. First, how to mine more exploitation values from all features of training samples to improve the accuracy of threshold classification; second, how to assign weights to different weak classifiers during iteration to build integrated classifiers with strong generalization ability. To solve the above two challenges, we propose a novel AdaBoost algorithm with Multiple Threshold Classification as the weak learn algorithm, which can improve the classification accuracy and generalization ability. In all, the innovation of this paper is that the threshold classification method is improved by taking the weighted voting results of the three thresholds with the highest accuracy as the prediction results, called Multiple Threshold Classification method, and the method is used as a Weak Learning algorithm for the AdaBoost algorithm. Meanwhile, in order to make the Multiple Threshold Classification method more adaptable to the AdaBoost algorithm, when assigning weights to the weak classifiers, we choose the maximum error rate as the weight parameter in order to obtain more weak classifiers with smaller weights to fully exploit the value of all features of the sample. Finally, we can obtain a better predictive classifier with higher accuracy, generalization ability, and convergence in different types of classification problems in different domains. The rest of this paper is organized as follows. In Section 2, in order to facilitate the description, we introduce the AdaBoost algorithm and Threshold Classification algorithm as the Weak Learn algorithm of AdaBoost. In Section 3, we illustrate our AdaBoost algorithm with Multiple Thresholds Classification and a new weight distribution in detail. Our AdaBoost algorithm is tested for accuracy and generalization in Section 4 using 12 datasets from UCI. Conclusion and future work are given in Section 5.
## 2 Establishment of a Simulation Model of the Cable Terminal Sheath Structure
### Establishment of a Geometric Model of the Roof Cable Terminal  
With the gradual deepening and maturity of finite element ideas, a variety of physical field programs based on finite elements have appeared on the market. COMSOL 4.6.2 Multiphysics software stands out among the many software types with its powerful multiphysical field coupling simulation function and has become one of the most important physical field simulation and analysis programs now available. It was originally a toolkit in the MATLAB R2020a software and has developed into an independent software package integrating electromagnetic field, acoustics, heat transfer, structural mechanics, optics, and other simulation analysis. Figure 1 shows the basic steps of physical model simulation when COMSOL 4.6.2 software is used. Users do not need to establish partial differential equations by themselves. Just by the user selecting the appropriate physical field and setting the initial and boundary conditions, the background program can process the grid and analyze and solve by itself. The final results will be displayed graphically so that the physical parameters of the established model can be seen intuitively.  
Figure 1: COMSOL software simulation flow chart.  
This study takes the cable terminal as the object through the electric field simulation of the cable terminal with sheath, obtains its electric field distribution characteristics, finds out the electric field concentration point near the insulating sheath, and lays a theoretical foundation for analyzing the discharge mechanism of the insulating sheath of the cable terminal. The actual cable terminal can be considered as an approximately three-dimensional symmetrical structure. To facilitate the simulation calculations with COMSOL software and improve the simulation efficiency, the spatial dimension of the physical model in this study adopts the axisymmetric structure of the cable terminal.  
In this paper, the two-dimensional axisymmetric model of cable terminal insulation sheath structure has been established with AutoCAD_2016_64 bit software, and the built model structure is imported into the geometric module of the COMSOL software. Figure 2 shows the geometric model of the cable terminal insulation sheath structure.
# Study on the Multi-Mode Optimal Control of Four-Wheel Steering Vehicle
## 2 Vehicle Model  
### Two Degree of Freedom Vehicle Model  
At present, the research of four-wheel steering vehicles with improved control algorithms mostly uses the linear 2-DOF 'bike' model including yaw rate and sideslip angle.  
The following assumptions are made when modeling: ignore the influence of the steering system and take the front wheel angle as the input directly; without considering the acceleration and deceleration transition conditions, the vehicle speed is considered to remain unchanged when steering; and ignore the roll and pitch motion of the vehicle and consider that the vehicle only performs plane motion parallel to the ground, and the vehicle only has two degrees of freedom: lateral motion along with \(y\)-axis and yaw motion around the \(z\)-axis. A 2-DOF dynamic model of four-wheel steering vehicle is established, as shown in Figure 1.  
According to the above assumptions and regulations, the following vehicle motion differential formula can be obtained through dynamic analysis:  
\[\left\{\begin{array}{l}\left(k_{f}+k_{r}\right)\beta+\frac{1}{u}\Big{(}ak_{ f}-bk_{r}\Big{)}\omega_{r}-\Big{(}k_{f}\delta_{f}+k_{r}\hat{\delta}_{r} \Big{)}=mu\Big{(}\beta+\omega_{r}\Big{)}\\ \Big{(}ak_{f}+bk_{r}\Big{)}\beta+\frac{1}{u}\Big{(}a^{2}k_{f}+b^{2}k_{r}\Big{)} \omega_{r}-\Big{(}ak_{f}\delta_{f}-bk_{r}\delta_{r}\Big{)}=I_{Z}\dot{\omega}_{ r}\end{array}\right. \tag{1}\]  
where \(\beta\) and \(\omega_{r}\) are the sideslip angle and yaw rate of the vehicle, respectively. \(u\) is the longitudinal velocity, \(m\) is the vehicle mass, \(k_{f}\) and \(k_{r}\) are the positive cornering stiffness of the front and rear axles, respectively. \(a\) and \(b\) are the distance from the vehicle's center of gravity to the front and rear axles, respectively. \(\delta_{f}\) and \(\delta_{r}\) are the front and rear-wheel steering angles, respectively. \(I_{Z}\) is the yaw moment of inertia.  
Figure 1: Single-track vehicle model.  
In the form of a state equation: Take \(X=\begin{bmatrix}\beta&\omega_{r}\end{bmatrix}^{T}\) as system variables, \(U=\begin{bmatrix}\delta_{f}&\delta_{r}\end{bmatrix}^{T}\) as system input, \(Y=\begin{bmatrix}\beta&\omega_{r}&a_{y}\end{bmatrix}^{T}\) as the output of the system, \(a_{y}\) as lateral acceleration. Formula (1) can be transformed into a state-space matrix:  
\[\left\{\begin{array}{l}\dot{X}=AX+BU\\ Y=CX+DU\end{array}\right. \tag{2}\]  
where  
\[A=\begin{bmatrix}\frac{k_{f}+k_{r}}{mu}&\frac{ak_{f}-bk_{r}}{mu^{2}}-1\\ \frac{ak_{f}-bk_{r}}{I_{Z}}&\frac{ak_{f}+b^{2}k_{r}}{uI_{Z}}\end{bmatrix},B= \begin{bmatrix}-\frac{k_{f}}{mu}&-\frac{k_{r}}{mu}\\ -\frac{ak_{f}}{I_{Z}}&\frac{bk_{r}}{I_{Z}}\end{bmatrix}\]  
\[C=\begin{bmatrix}1&0\\ 0&1\\ \frac{k_{f}+k_{r}}{m}&\frac{ak_{f}-bk_{r}}{mu}\end{bmatrix},D=\begin{bmatrix} 0&0\\ 0&0\\ -\frac{k_{f}}{m}&-\frac{k_{r}}{m}\end{bmatrix}\]  
The key parameters of the model are shown in Table 1.
# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 2 Related Work  
### Traditional Infrared Small-Target Detection Methods  
Traditional infrared small-target detection methods typically assume that targets appear as sparse, high-intensity points on locally smooth backgrounds. These approaches are non-learning-based, operate on single-frame imagery, and rely heavily on handcrafted filters or statistical priors to enhance local contrast and suppress background clutter.  
Early techniques include morphological operators such as the Top-Hat transform [1], which isolates bright points by subtracting the morphologically opened image from the original. Variants like the Multiscale and Ring Top-Hat filters improve scale adaptability but remain sensitive to parameter selection and struggle with non-uniform clutter. The Max-Mean and Max-Median filters [2] estimate local background statistics and identify salient targets based on intensity contrast, yet they require fine-tuned window sizes and may fail in highly textured scenes. Local contrast-based detectors, such as the Absolute Directional Mean Difference (ADMD) and Adaptive Gaussian Difference (AGD), enhance target visibility by emphasizing directional contrast or Gaussian-filtered differences [3,4].  
Other spatial-domain methods include Gaussian Curvature Filtering (GCF) [5], which preserves blob-like structures while smoothing out background patterns, and Local Steering Kernel (LSK) reconstruction [6], which fits a locally adaptive model to suppress structured clutter. Frequency-domain approaches, like the Phase Spectrum of the Fourier Transform (PFT) [7], highlight spatial anomalies through phase-only information, achieving good background suppression but limited spatial precision. Matched filtering methods maximize the signal-to-noise ratio based on the assumed point-spread characteristics of the target [8], yet they are template-dependent and vulnerable to shape variation. Overall, while traditional methods offer low computational cost and require no training data, their performance degrades significantly in the presence of complex clutter, dim targets, or highly dynamic backgrounds. Moreover, they lack adaptability to varying environments and often produce high false alarm rates due to the limited discriminative capacity of handcrafted features.
## 3 System Model
### Spectrum Sharing Schemes  
In this paper, we assume that the CUs and the D2D users can transmit data in both licensed spectrum and unlicensed spectrum, and the BS will select the subchannel to support LTE, D2D, LTE-U and D2D-U users. The spectrum sharing of these users is based on the following spectrum access mechanisms.  
**A: Licensed spectrum**  
In the licensed spectrum, each D2D user is allowed to reuse one subchannel of the CUs such as prior works [18]. Furthermore, we mainly consider resource sharing on the uplink channel due to the interference can be easier to resolve on the uplink than that on the downlink. To avoid severe co-channel interference between the CUs and the D2D users, we assume that each subchannel can be occupied by at most \(M_{T}\) D2D users. Moreover, we assume that the number of CUs is larger than the number of the licensed subchannels, and the number of D2D user does not exceed twice the number of subchannels, i.e., \(M>L\), \(N>2M\)[26]. Therefore, the system is heavy-loaded, i.e., there is no unused channels in the licensed spectrum. The CUs and D2D users which failed to match with channels in the licensed spectrum will access to the unlicensed spectrum by using duty-cycle method while ensuring the quality of service (QoS) of Wi-Fi system [7].  
**B: Unlicensed spectrum**  
The Wi-Fi system adopts CSMA/CA protocol to operate in the unlicensed spectrum, and the Wi-Fi AP first listens to the target channel before transmission. If the channel is sensed idle for an amount of time, a backoff procedure begins to avoid collision; otherwise, the Wi-Fi AP will keep monitoring the intended channel until the channel is unused. Moreover, the Wi-Fi system operates in the whole unlicensed spectrum, where the user is only allowed to access to the unlicensed spectrum in a period of time.  
When LTE-U or D2D-U users access to the unlicensed spectrum, the Wi-Fi users will experience severe interference. To avoid the performance degradation of the Wi-Fi system and realize a fair and harmonious system, we assume that both D2D-U and LTE-U adopt duty-cycle mechanism as shown in Figure 2[27]. The LTE-U or D2D-U users transmit data in the LTE-U/D2D-U on-time, and Wi-Fi users are allowed to transmit data in the LTE-U/D2D-U off-time to avoid collision. In this mode, LTE-U/D2D-U and Wi-Fi time division multiplexing channels are employed to achieve the purpose of fair channel occupation. The duty-cycle mechanism can be used in the areas where there is no listen-before-talk (LBT) requirement. The structure of the existing LTE BS is slightly changed, but to achieve  
Figure 1: System model.  
better performance, Wi-Fi signal monitoring needs to be added to the BS. Moreover, we treat the CUs and D2D users as one type of the user on account of the CUs and D2D users have the same grade of service in unlicensed spectrum.
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 4 Simulation Results
### Comparison of Received Packets
For the lowest congestion case (packet size of 256 bytes and beacon interval of 0.1 s), this is almost the same for all approaches, but for the highest congestion case (packet size of 1024 bytes and beacon interval of 0.05 s), DRCC1 has the best performance, even though the total number of received packets is lower. The number of packets received from vehicles within a 100 m radius by DRCC1 is 2.5% higher than the next highest value for DR-DCC and about 7% higher compared to DRCC5. Therefore, it is important to choose the appropriate version of DRCC based on the application needs.
## 3 Materials and Methods
### Gravity Anomaly Separation  
The observed gravity anomaly, \(G_{obs}\), consists of high and low frequency components representing shallow and deep geological structures, respectively. These frequency components are known as residual (high), \(G_{res}\), anomaly and regional (low), \(G_{reg}\), anomaly:  
\[G_{obs}=G_{res}+G_{reg} \tag{1}\]  
Some mathematical methods are required to separate the map data into two components [24]. The separation of these components must be performed in the spatial frequency domain via fast Fourier transform (FFT) known as a low-pass filter and a high-pass filter based on the method given by [PERSON] and [PERSON] [25]. The regional gravity anomaly map is a product of a low-pass filter (long wavelength) that shows deep-seated structure while the residual gravity anomaly map is produced by the high-pass filter (short wavelength) that shows the shallow structure.