esa-sceva/satcom-chunk-collection huggingface dataset:
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 4 Performance Evaluation
### Overlapped Area  
We measure the overlapped area to evaluate how much overlapped reconnaissances of each model occurred during the simulation. When a unit region is scanned by two or more UAVs at the same time, we cumulatively add the unit region. Figure 10 shows the result of the total overlapped area with the existing models and the proposed models. As shown in Figure 10, the overlapped area is reduced from five times to twenty times less than the previous schemes by applying the proposed overlap avoidance method.  
Figure 9: The interval coverage rate results with/without Overlap Avoidance: (**a**) Random Waypoint; (**b**) Random Markov Process; (**c**) Enhanced Gauss–Markov; and (**d**) Distributed Pheromone Repel.  
Among the proposed models, the RW with overlap avoidance model shows the smallest overlapped area because it turns the directions to other ways deterministically. On the contrary, the other three models adjust their action probabilities. In the DPR model with overlap avoidance, the pheromone information still affects to the action probabilities, which results in more overlapped area than other overlap-avoidance schemes.  
While the proposed avoidance schemes guide UAVs not to reconnoiter the same area, it also prohibits collision of UAVs. During our simulations, the proposed overlap-avoidance models show no collision. However, the simulations of previous models sometimes show two UAVs meeting at a point which implies collision of the UAVs. That is, the proposed avoidance scheme prohibits collision of UAVs as well.
## 2 Fundamental Theories
### SVM Classifier Parameters Tuning  
#### 2.2.1 Support Vector Machine  
A SVM is a widely used machine learning method based on the minimization principle of structural risk [23]. It has many unique advantages in solving small sample, multi-class, non-linear and high-dimensional pattern recognition problems [5]. In this paper, the proposed method mainly uses a nonlinear multi-class SVM as the backend multi-class classifier to solve the classification problem [28]. Given a group of sample set \(S=\left\{(x_{i},y_{i})_{i=1}^{n}|x_{i}\in R^{N},y_{i}\in\{-1,1\}\right\}\), where \(x_{i}\) presents the \(N\) dimensional input feature of the \(i\)-sample, and \(y_{i}\) is the corresponding label for the input sample. If the hyperplane equation reaches the criterion of the optimal separating plane, the optimal separation plane can be solved by the following objective functions and constraints.  
\[\left\{\begin{array}{l}\min\frac{1}{2}\|w\|^{2}+c\sum\limits_{i=1}^{n}\xi_{ i}\ (\xi_{i}\geq 0,c\geq 0)\\ \mathrm{s.t.}y_{i}(wx_{i}+b)\geq 1-\xi_{i}\ (i=1,2,\ldots,n)\end{array}\right. \tag{5}\]  
where \(w\) denotes the weight vector and \(b\) depicts the bias vector, \(\xi_{i}\) is the relaxing factor and \(c\) is a constant named as the penalty factor and it can relieve contradiction between algorithm complexity and classification accuracy. Finally, the classification problem of SVM are transformed to the following dual optimization problem.  
\[\left\{\begin{array}{l}\max L=\sum\limits_{i=1}^{n}\mu_{i}-\frac{1}{2}\sum \limits_{i,j=1}^{n}\mu_{i}\mu_{j}y_{i}y_{j}K(x_{i},x_{j})\\ \mathrm{s.t.}\sum\limits_{i=1}^{n}\mu_{i}y_{i}=0\ (i=1,2,\ldots,n,\mu_{i}\geq 0) \end{array}\right. \tag{6}\]  
where \(\mu_{i}\) are the Lagrangian multipliers, and \(K(x_{i},x_{j})\) represents a kernel function that meets \(K(x_{i},x_{j})=<\phi^{\mathrm{T}}(x_{i})\cdot\phi(x_{j})>\), where the \(<\cdot>\) is the inner product operation. The final SVM classification function is:  
\[f(x)=\mathrm{sgn}\left(\sum\limits_{i=1}^{n}a_{i}y_{i}<K(x_{i},x_{j})>+b\right) \tag{7}\]
## 4 Results and Discussion
### Consensus on Challenges
#### 4.1.7 Set-Up Time  
Set-up time was reported to be a challenge related to the application as well as how much time is needed to set up the hardware, power it on, run the software, and use the application the way it is intended to be used. AR applications require previous preparation. For example, 3D models need to be previously drawn using other software in order to be incorporated during a visualization application. Therefore, this challenge is related to thedevelopment of the application. Users need guidelines and instructions to guide them on what to prepare and how to set up and use this application, so they become more familiar with the software and the hardware (head-mounted displays, cellphones, and tablets) and ease its implementation.
## Appendix B  
We take the calculation process of \(C_{11}^{sat}\) as an example. From the anisotropic Gassmann's Equation (10), \(C_{11}^{sat}\) can be expressed as  
\[C_{11}^{sat}=C_{11}^{dry}+\frac{\left(K_{o}-C_{1a}/3\right)^{2}}{\left(K_{o}/ K_{f}\right)\phi_{t}\left(K_{o}-K_{f}\right)+\left(K_{o}-C_{pq}/9\right)} \tag{13}\]  
Therefore, in order to obtain \(C_{11}^{sat}\) through Equation (13), \(C_{1a}\) and \(C_{pq}\) are indispensable. Based on their definitions and rewriting the results in terms of \(K_{dry}\), \(M_{dry}\) and \(\delta_{N}\), we can express them as  
\[C_{1a}=3K_{dry}(1-\delta_{N}) \tag{14}\]  
\[C_{pq}=9K_{dry}(1-\delta_{N}K_{dry}/M_{dry}) \tag{15}\]  
Substituting Equations (14) and (15) into Equation (16), we have  
\[C_{11}^{sat}=M_{dry}(1-\delta_{N})+\frac{\left(K_{o}-K_{dry}(1-\delta_{N}) \right)^{2}}{\left(K_{o}/K_{f}\right)\phi_{t}\left(K_{o}-K_{f}\right)+\left(K_{ o}-K_{dry}+\delta_{N}K_{dry}^{2}/M_{dry}\right)} \tag{16}\]  
We also can express and calculate \(C_{2a}\) and \(C_{3a}\) in the same way as \(C_{1a}\)  
\[C_{2a}=C_{3a}=3K_{dry}(1-r\delta_{N}) \tag{17}\]  
where \(r=\lambda_{dry}/(\lambda_{dry}+\mu_{dry})\).  
Then, we can derive other stiffness elements of the saturated rocks using a similar method, and the results are  
\[C_{33}^{sat}=M_{dry}\Big{(}1-r^{2}\delta_{N}\Big{)}+\frac{\left(K_{o}-K_{dry }(1-r\delta_{N})\right)^{2}}{\left(K_{o}/K_{f}\right)\phi_{t}\left(K_{o}-K_{f }\right)+\left(K_{o}-K_{dry}+\delta_{N}K_{dry}^{2}/M_{dry}\right)} \tag{18}\]\[C_{13}^{sat}=\lambda_{dry}(1-\delta_{N})+\frac{\left(K_{o}-K_{dry}(1-\delta_{N}) \right)(K_{o}-K_{dry}(1-r\delta_{N}))}{\left(K_{o}/K_{f}\right)\phi_{t}\left(K_{o }-K_{f}\right)+(K_{o}-K_{dry}+\delta_{N}K_{dry}^{2}/M_{dry})} \tag{14}\]  
\[C_{44}^{sat}=\mu_{dry} \tag{15}\]  
\[C_{55}^{sat}=\mu_{dry}(1-\delta_{T}) \tag{16}\]  
By using the above equations, we can perform anisotropic fluid substitution analysis for HTI media.
## 5 Conclusions  
A detailed analysis on the spatial variability characteristics of the NDVI and the synergistic effect between P and T for the NDVI in northern China was carried out, which enriches the research results in this field and provides a reference for ecological environment improvement. The main conclusions are as follows:  
1. The overall distribution pattern of the NDVI increased from northwest to southeast. On a temporal scale, the NDVI mainly showed a downward trend before 2012, which then changed to upward. In space, NDVI decreased in the west and increased in the east.
2. On the whole, NDVI, P, and Tmax had the strongest multivariate correlations, with approximately 43.4% of the total study area passing the 95% confidence level significance test. Mnpmax was mostly between 0.39-0.51, followed by Tave and Tmin.
3. The explanatory power of the synergistic effect between P and T for the NDVI was enhanced from northwest to southeast. Among them, the explanatory power of the synergistic effect between P and Tmax for the NDVI was the strongest, followed by Tave and Tmin. As for the annual change rate, the overall explanatory power between P and T for the NDVI was weakened in the central area and strengthened in the east and west.  
Conceptualization, Y.C. and L.M.; methodology, Y.C.; software, Y.C.; validation, L.M. and T.L.; formal analysis, X.H., writing--original draft preparation, Y.C.; writing--review and editing, Y.C. and L.M.; visualization, G.S.; supervision, G.S.; project administration, Inner Mongolia Agriculture University; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technology-Planning Project of Inner Mongolia (2020 GG0074) and the Inner Mongolia Science Foundation (2020 MS05054).  
**Institutional Review Board Statement:** Not applicable.
## 2 Materials and Methods
### Dataset and Scenery  
The dataset is a crucial part of the project. In order to achieve reliable results, a well-balanced dataset is essential. For this study, the \"WiSARD\" dataset was initially used for training purposes [43]. \"WiSARD\" contains visual and thermal images acquired by a drone across a variety of wildlife environments over an extended period, at varying angles and under different lighting conditions. The dataset consists of three main subsets: a set of 26,862 labeled RGB images called \"Visual Only\", a set of 29,989 2D infrared (thermal) images labeled \"Thermal Only\", and a combined set of 15,453 synchronized visual and thermal image pairs called \"Visual and Timed\".  
To improve dataset diversity and better simulate the project's specific conditions, the \"WiSARD\" dataset was later mixed with the authors' own filmed footage. This additional footage was captured using a DJI Mavic 2 Enterprise Dual, which is equipped with both thermal and RGB cameras. A lower resolution of 1920 \(\times\) 1080 for RGB and 640 \(\times\) 360 for thermal images was selected to reduce processing time and enable faster algorithm execution. The drone's ability to capture time-synchronized RGB and thermal images made it particularly suitable for this study, aligning well with the structure of the \"WiSARD\" dataset. Qualitative examples from the \"WiSARD\" dataset, as well as our custom-collected images, are presented in Figure 2.  
The footage was recorded during winter in a location where human subjects blended easily with the background and with other objects resembling the shape of a human when viewed from above. This scenario presented a challenging test case for the algorithm. During the testing phase, multiple false positives were observed due to the limited size of and variation in the winter dataset, particularly the lack of diverse negative samples. Most negative samples consisted of either snow-covered terrain or heavily snow-laden trees. To address this, a portion of the recorded video footage was removed from the test and evaluation set, segmented into frames, and manually labeled for use in training and validation.  
Ultimately, the final dataset was formed by merging the \"WiSARD\" dataset and the authors' footage in equal proportion, resulting in a balanced 1:1 ratio that improved model generalization and performance across varied environmental conditions.  
Figure 2: SARGAS dataset images (**left**), WiSARD dataset images (**right**).
## 1 Introduction
When the laser is focused on a metal target, particle behavior is determined by the interaction time. At the same energy, interactions occurring over nanoseconds to microseconds trigger photothermal reactions; at shorter reaction times (femtoseconds to picoseconds), most reactions are photochemical, leading to nanoparticle growth or fragmentation [37; 38; 39]. Smaller and more homogeneous nanoparticles are generated at shorter pulse widths. However, for the bonding or growth of previously generated nanoparticles, nanosecond pulse widths that produce thermal effects may be important. In addition, plasma induced by laser ablation creates liquid shock waves and cavitation bubbles, which affect nanoparticle fragmentation and growth. Other metal ions in the liquid affect the sizes and shapes of the nanoparticles. The various mechanisms should ideally be independently examined, but this is very difficult given the extremely short time scales. For bioengineering, living cells can be subjected to photothermal therapy using nanoparticles produced via LAL, LFL, and LML; heterosynthetic nanoparticles enhance  
Figure 1: Schematics of (**a**) Laser ablation in liquid (LAL), (**b**) Laser fragmentation in liquid (LFL), and (**c**) Laser melting in liquid (LML). therapeutic efficiency [16; 28; 29; 40]. Silver (Ag) nanoparticles exhibit high laser absorption in the infrared range [40]. It is relatively easy to control the size of gold (Au) nanoparticles by adjusting the laser pulse width [41]. Palladium (Pd), which belongs to the platinum group, exhibits excellent electrical and thermal conductivity and ductility, and catalyzes both hydrogenation and dehydrogenation [13; 42]. Heterogeneous nanoparticles may include combinations of these elements as alloys, intermetallic compounds, or core-shells, exhibiting new properties that complement those of single-element particles [3; 4; 5]. In a novel process, we synthesized heterogeneous (AgPd, AuPd) nanoparticles with the characteristics of both elements via pulsed laser beam irradiation of Ag or Au in a solution of Pd ions. Femtosecond and nanosecond pulses were delivered, and solution metal ions affected nanoparticle fragmentation and growth. The process conditions were optimized, and the morphological characteristics size distribution, stability, and optical properties of the heterogeneous nanoparticles were analyzed.
# Coupling Denoising to Detection for SAR Imagery
## 3 Results
### Ablation Study  
We conduct an ablation study for structurally verifying the proposed fusing region proposal strategy. We first compare the case without fusing itself after denoising on input noisy SAR image, which corresponds to the first experiment in Table 3. With the comparison to inputting only denoised SAR image as an input to detection network, we can identify whether the usage of real SAR image as another input of the detection network is important. This case shows the poorest detection performance and justifies the importance of fusing information from raw noisy SAR images. Secondly, for the choice of feature map after fusing, we perform experiments with feature map from denoised SAR image or feature map from real SAR image. As a result, keeping the feature map from the real SAR image as proposed is found to be much better.
# Collaborative Trajectories Optimization for Tram and Connected Vehicles at Signalized Intersection
## 4 Methodology
### Solved with the Adaptive Elitist Genetic Algorithm (A-EGA)  
In this paper, the A-EGA is designed to solve the problem of the traditional genetic algorithm being slow and falling easily into the local optimal solution.  
Improvements to the A-EGA's selection, crossover and mutation operators are as follows:  
1. Selection: After fitness calculation of the comprehensive objective function, selection begins. In the selection process, elite individuals are retained, and the roulette selection method is adopted. The survival probability of the individuals is determined by their fitness. The elite reservation strategy improves the convergence ability of the genetic algorithm.
2. Crossover and mutation: A random number is used to determine the position of the crossover. The mutation operation takes a fixed-value mutation. In crossover and mutation operations, the crossover probability \(P_{\text{c}}\) and mutation probability \(P_{\text{m}}\) directly affect the convergence speed of the algorithm. If \(P_{\text{c}}\) and \(P_{\text{m}}\) are too large, they will destroy the chromosome structure with good fitness, and, if \(P_{\text{c}}\) and \(P_{\text{m}}\) are too small, they may fall under the local optimal solution. A value that is too large or too small will lead to a decrease in evolutionary efficiency. Therefore, an adaptive crossover and mutation operation is constructed. The calculation formulas for the probability of an adaptive crossover and mutation are defined as \[P_{\text{c}}(q_{1},q_{2})=\begin{cases}P_{\text{cmax}}\frac{F_{\text{max}}-f^{ \prime}}{F_{\text{max}}-F_{\text{avg}}}&f^{\prime}\geqslant F_{\text{avg}}\\ P_{\text{cmax}}&f^{\prime}<F_{\text{avg}}\end{cases}\] (27) \[P_{\text{m}}(q)=\begin{cases}P_{\text{mmax}}\frac{F_{\text{max}}-f}{F_{ \text{max}}-F_{\text{avg}}}&f\geqslant F_{\text{avg}}\\ P_{\text{mmax}}&f<F_{\text{avg}}\end{cases}\] (28)  
where \(P_{\text{c max}}\) and \(P_{\text{m max}}\) are the maximum crossover rate and the maximum mutation rate, respectively; \(F_{\text{max}}\) and \(F_{\text{avg}}\) are the maximum fitness value and average fitness value for all chromosomes; \(f^{\prime}\) represents the higher fitness values in chromosomes \(q_{1}\) and \(q_{2}\) and \(f\) represents the fitness value of chromosome \(q\).  
When superior offspring are produced in the process of evolution, the probability of crossover and mutation is reduced. In other words, in order to extend the search range and maintain the diversity of the population, the algorithm is given a higher probability to generate new solutions in the early stage of evolution, and, in order to improve the convergence rate of the algorithm, the algorithm is given a smaller probability to destroy the optimal solution in the late stage of evolution. The values of \(P_{\text{c max}}\) and \(P_{\text{m max}}\) are obtained from reference [36] and set to 0.9 and 0.4, respectively. The number of iterations is set to 100. The optimal scheme is outputted after the required number of iterations is reached.
## 3 Proposed Algorithm
### Experimental Setup  
The proposed training and inference phases of the algorithm are actualized on NVIDIA RTX 3090 GPUs (NVIDIA, Santa Clara, CA, USA) using PyTorch 2.1.0 in Python 3.9. Our model is implemented based on the original CapsNet architecture proposed by [PERSON] et al. [40], which utilizes dynamic routing between capsules to encode spatial and part-whole relationships. On this basis, we modify the routing algorithm of the CapsNet and update the capsule weights using spectral information. Furthermore, we design modules such as HFBE, BT, and SIP to enhance spectral-spatial representation and improve adaptability in challenging tracking scenarios. The experimental training data parameters are as follows: \(Thr\) in HFBE is set to 0.28 for distinguishing the target from the background, and the spectral positive number \(\varepsilon\) in SIP is set to \(10^{-6}\), \(\lambda\) in the capsule loss function \(\mathcal{L}_{cap}\) is set to 0.5, and \(\theta\) in the total loss function \(\mathcal{L}_{total}\) is set to 0.5. The Adam optimization algorithm is used during training. We trained the network with a training rate of 0.0001 for offline practice. The learning rate was set to 0.0003 for the online refresh. The iterative routing experiments were set to 3 times.  
The dataset HOT2023 [49] is used in this study. The HOT2023 dataset is accessible at www.hsitracking.com (accessed on 7 April 2025). The dataset was acquired at 25 FPS using three XIMEA fast cameras (including VIS, NIR, and RedNIR). They cover 16 bands, 25 bands, and 15 bands respectively. These include 182 training videos and 89 validation videos. HOT2023 contains NIR data at 665-960 nm, RedNIR data at 600-850 nm and hyperspectral data (VIS) at 460-600 nm. The video scenes involve 19 challenging conditions.  
As shown in Figure 4, we selected four NIR data sequences (car11, car78, rider11, and rider17) and four hyperspectral data sequences (board, kangaroo, ball&mirror9, and rainystreet10) in the HOT2023 dataset.  
Table 1 displays more details about the variables of the eight test sequences, and Figure 4 shows the false-color images of the eight sequences. We selected specific sequences from the eight test sequences. In particular, we selected sequences with different challenges such as background clutter (BC), scale variation (SV), fast motion (FM), in-plane rotation (IPR), motion blur (MB), occlusion (OCC), and out-of-plane rotation (OPR) in order to fully evaluate the generalization ability of the tracker.  
In our experimental design, the images processed by the selected RGB tracker are not directly derived from any three bands of the original hyperspectral data. Instead, the pseudo-color image sequence optimized by the HOT2023 dataset is used. The generation of pseudo-color images ensures the best contrast, rich texture information, and distinct target recognition features for the target tracking task. These pseudo-color images can effectively capture the characteristics of the original NIR and VIS data, maintaining a high degree of consistency with the original inputs in the target tracking task.
## 7 Implementation and Evaluation
### Planetlab  
In addition to our testbed, we ran some experiments on PlanetLab [43] to have a more common distribution of services. We were able to easily deploy some PUMA services on several selected servers within the PlanetLab network and run our workflow experiments after establishing the required environment. Most of these servers are usual single or dual core machines with around 2-3 GHz and 1-4 GB memory like the ones provided by the Massachusetts Institute of Technology and the University of California at Berkeley.  
Due to the fact that the PlanetLab network is intensely used, we had to put up with a constant high degree of processing power utilization of around 60%-75% on every server. Despite the attempt to reserve and consequently increase some additional CPU time, the available processing power restrained the overall performance conspicuously. Nevertheless, we were able to successfully simulate some shorter workflows in this widely distributed and multiply used network.  
As anticipated, the considerably lower computing power due to slower and multiply shared servers, compared to our environment, led to a significant increase of startup and delivery times, especially for longer workflows and even with increased CPU priority. These experiments did not allow multimedia streaming in real-time. Hence, these results substantiate our assertion that P2P networks are not suitable for service oriented multimedia stream processing.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Study Area
#### 3.2.3 EPA eGRID Data  
Emission and Generation Resource Integrated Database (eGRID) data from the Environmental Protection Agency (EPA) was used to validate the training data. This data provides an inventory of environmental attributes of power plants in the United States by tracking air pollutants, such as SO\({}_{2}\), CO\({}_{2}\), and NO\({}_{2}\) per lb/MWh.  
The continuous emission monitoring system (CEMS) validates the data collected using the procedure, \"Quality assurance requirements for gas continuous emission monitoring systems used for compliance determination\". This procedure is primarily concerned with determining compliance by evaluating the efficacy of quality control (QC) and quality assurance (QA) of data provided by any operational CEMS. It specifies the minimal quality assurance requirements for controlling and quality of any CEMS data provided to the Environmental Protection Agency (EPA) [31]. Any owner of a CEMS must meet these minimal conditions to submit the data they obtained to ensure its validity.  
Additionally, the collected in situ EPA NO\({}_{2}\) data for this study came with uncertainty about the accuracy and under-sampling of the estimates. EPA releases a \"National Emissions Inventory\" (NEI) every three years as an estimate of emissions of criteria pollutants [32]. This study utilized remotely sensed data as a representation of in situ data. EPA NO\({}_{2}\) was used as the target variable to predict for this study.