esa-sceva/satcom-chunk-collection huggingface dataset:
# Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
## 3 Stackelberg Game Model and Problem Statement
### Optimization Problems
Namely, the revenue for users and the revenue obtained through macro base station uninstalling its partial load on partial spectrum. One-order derivative of \(\beta_{j}\) is solved for Equation (18). \[\frac{\partial U_{j}}{\partial\beta}=\log\left[\frac{\alpha^{*}W_{j}\log\left( 1+SINR_{j,i}^{*}/\beta_{j}\right)}{(\beta-1)S_{F_{j}}r_{0}}\right]-\frac{( \beta-1)}{\beta SINR_{j,i}^{*}\log\left(1+SINR_{j,i}^{*}/\beta_{j}\right)}-1 \tag{18}\]  
Further obtaining the second order derivative. \[\frac{\partial^{2}U_{j}}{\partial\beta^{2}}=-\frac{1}{\beta SINR_{j,i}^{*} \log\left(1+SINR_{j,i}^{*}/\beta_{j}\right)}-\frac{\beta/SINR_{j,i}^{*}+\log \left(1+SINR_{j,i}^{*}/\beta_{j}\right)}{SINR_{j,i}^{*}\beta^{*}\log^{2}\left( 1+SINR_{j,i}^{*}/\beta_{j}\right)}-1 \tag{19}\]  
Because \(\frac{\partial^{2}U_{j}}{\partial\beta^{2}}<0\), \(U_{j}\) is a concave function, with the presence of optimal solution \(\beta^{*}\). The optimal solution can be obtained through Equation (18). In the actual heterogeneous wireless network spectrum sharing model based on game model, players generally cannot know the strategy of other players or users at the moment, hence being unable to accurately solve Nash equilibrium according to linear equation set in Formula (19). Because of this, we need to further probe into the optimization solution seeking method instead of depending on other players' decisions at the moment. Assuming that players cannot know the strategy at the moment but know the strategy of players at the previous moment, then Nash equilibrium strategy with maximal system revenue can be obtained through iterative methods. In the next section, we will introduce one kind of intelligent gradient algorithm for completing optimization solution.
## 2 Materials and Methods
### Plant Sampling and TE Analysis  
The Trudan HL and the Biomass 133 sorghum were entirely harvested in area 2 in September 2019 (105 days of growth) and in October 2019 (131 days of growth), respectively. In addition, on areas 1 and 3, ten samples of both cultivars were collected in September 2019. Before harvest, samples of the aerial parts of sorghum were taken randomly at the same time as sediment samples. As a result, ten samples of sorghum per plot and per zone, i.e., thirty samples of each sorghum cultivar, were collected. Plants were cut at their base using a hand pruner and transported to the laboratory for analysis. The height of each sample of Biomass 133 sorghum was measured for comparison with expected height; for Trudan HL, this measurement was not useful, as this is a multicut cultivar. On the plots in area 2, all the remaining plants were cut with a hand pruner and then gathered and weighed using a portable hand scale to obtain the plot yields of the two cultivars. Plant materials were washed with tap and deionized water before drying (40 \({}^{\circ}\)C until constant weight) and ground to a powder using a blender. In addition, in order to obtain fresh and dry masses, the samples were weighed before washing and grinding. Following this preparation, 0.5 g of each plant sample was mineralized with HNO3 (10 mL, 69%) and heated in a microwave digester (Mars Xpress CEM). The solution was then made up to 50 mL with Milli-Q water and filtered (0.45 \(\upmu\)m, hydrophilic Teflon). The TE concentrations in the eluates were measured by ICP-OES (Agilent 5100) or ICP-MS (Agilent 7500). Onestandard reference material was used for analytical quality control (Branches and leaves of Bush \"NCS DC 73349,\" NCS Testing Technology, China, [[http://www.ncsstandard.com](http://www.ncsstandard.com)]([http://www.ncsstandard.com](http://www.ncsstandard.com)), accessed on 5 June 2018). For all the digestion sets, recoveries were 1.38 for As, between 0.96 and 0.99 for Cu, 0.71 for Ni, between 1.03 and 1.05 for Pb, and 1.03 for Zn. No reference values were available for Cr in the reference material, and the reference value for Cd was lower than the LoQ.  
Using the TE concentrations in plants and total and extractable concentrations in sediment, the bioconcentration factor (BCF) [43] was calculated as follows:  
1. [label=(0)]
2. BCF ext = [TE] plant/[TE] sediment extractable
3. BCF tot = [TE] plant/[TE] sediment total
## 8 Optical Wireless Communication (OWC)
A contention-based MAC protocol for OWC was presented in [109, 110]. This MAC protocol can adjust itself to suit the network congestion. These kinds of MAC protocols are useful for networks with frequently changing users and moderate network requirements. For instance, shopping malls have mobile users who will not stay connected to the same AP for a long period of time. On the other hand, contention-free MAC protocols are useful for scenarios where the users have stringent requirements on the data rate and latency. For example, a VR gaming room has a consistent set of users who require high data rates and low latency. A contention-free MAC protocol for OWC with dynamic reconfigurability was presented in [111]. This study presents a contention-free MAC protocol that can change the service intervals and other parameters to suit the traffic requirements. From the developments happening in the physical and upper layers of OWC, it is clear that OWC is progressing as a viable WLAN in 6G that can address most of the pressing issues that 5G and other WLANs face today. For instance, the high-density deployments, increasing interference, and spectrum crunch in the low frequencies are easily addressable using OWC WLANs. Though the commercial products are yet to be developed, OWC will  
Figure 10: Split-plane architecture for OWC. Figure 9: Inter-MAC architecture. be an integral part of the 6G ecosystem with its lucrative features. The use cases of OWC in the 6G ecosystem will be similar to that of VLC. However, due to its ability to provide ultra-high data rates and low latency, OWC can complement 6G in environments such as smart cities where reliable high-data-rate communication becomes a must. For example, OWC could be used to transmit data between vehicle to vehicle and vehicle to infrastructure, including streetlights and traffic lights, enabling real-time communication and control of traffic flow of autonomous vehicles without using RF signals [112].
# MiFT: A Moment-Based Local Feature Extraction Algorithm
## 2 Scale Invariant Feature Transform and Modified Discrete Gaussian-Hermite Moment
### Scale Invariant Feature Transform  
The SIFT is a robust local feature descriptor comprising four stages.  
1. Scale space extrema detection. Search keypoint candidates on the basis of the extrema over all scale images. This constructs a Gaussian pyramid and finds the local extrema in DoG images.
2. Keypoint localization: Locate keypoints by removing unstable keypoint candidates having low contrast or poor localization along an edge.
3. Orientation assignment. Identify the orientation for each keypoint based on local image gradient information.
4. Build a descriptor for each keypoint.
## 2 Related Theory
### Robust Analysis with [PERSON] [25; 40] introduced a quadratic loss function to represent robustness, and The expected value of the function is defined as  
\[Q=E(L(f))=k[\sigma^{2}+(u-m)^{2}] \tag{3}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Definition** \\ \hline Functional element (FE): & Point, curve, or surface that belongs to a part in the assembly. A FE can be real, e.g., the plane surface of a block, or constructed, e.g., the axis of a cylinder or a median plane. \\ Functional requirement (FR): & An important condition to be satisfied between two FEs on different parts, e.g., a fitting condition. \\  & For tolerancing purposes, we associate coordinate frames to the toleranced FE in a pair, assuming there exists a set of virtual joints that can make the toleranced FE of the pair “move” relative to the other FE of the pair, to simulate manufacturing inaccuracies. \\ Functional element pair (FE pair) & Two FES on different parts or on the same part; includes a kinematic pair and internal pair. \\ small-displacement screw model & An expression which contains potential variations along and about all three Cartesian axes. \\ Global reference frame (GRF) & Frame in which the functional requirement and total dimensional chain is established. \\ Local reference frame (LRF) & Frame in which each functional element pair is established. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic concepts of the Jacobian model.  
where \(k\) is the constant, \(m\) is the target value, \(\mu\) is the mean of \(f\) and \(\sigma\) is the standard deviation of \(f\). Figure 1 represents the relation between the factors and the process.  
The noise factor is the one that the designer cannot control even though it causes variability, while control factors are design variables. the Taguchi method aims to minimize the loss function \(Q\) and optimize the robustness. It has been proved that minimizing loss function is equivalent to maximizing the signal-to-noise ratio when the response with m is referred to as the nominal-the-best type characteristic. The expression of \(S/N\) ratios for different characteristic types.  
Utilizing the design of experiments, a systematic procedure to perform robust design is developed. Generally speaking, first, the orthogonal arrays are utilized to implement experiments. Then from experiments' results, the \(S/N\) ratios are calculated and the optimum setting of control factors is acquired by analyzing the \(S/N\) ratios. Finally, with the \(S/N\) ratios maintained, a scale factor is selected to set the mean value to the target value.
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
## 3 Results and Discussions
### Radiation Pattern  
The radiation pattern of the proposed UWB antenna with a notch band is presented in Figure 8. The antenna offers a nearly omni-directional radiation pattern in the principal  
Figure 6: Simulated and measured gain of the proposed notch band extended UWB antenna.  
Figure 7: Current density of proposed notch band extended UWB antenna at various frequencies.  
H-Plane for both resonating frequencies of 3.5 GHz and 6.5 GHz. On the other hand, for E-Plane, a dipole-like bidirectional radiation pattern was observed for 3.5 GHz, while a slightly tilted bidirectional radiation pattern was observed for 13 GHz, as depicted in Figure 8. In general, a strong agreement between simulated and measured results was observed for both resonating frequencies. It is worth noting that the antenna offers stable radiation patterns at all resonating frequencies, ensuring its wideband operation.
## 3 Experimental Results
### Battery Tests
#### 3.1.2 Battery Pack Tests  
The battery pack was tested under the typical flight load profile to ensure its endurance when in flight. It should be noted that this flight profile is quite demanding. The final result of flight autonomy will be greater than that obtained in this test bench because the cut-off voltage will be lowered to 42 V when real operations will take part.  
Figures 19 and 20 correspond to the test performed to the battery pack under flight load profile. The first step of this profile demands 4 KW during 4 min. Figure 19 shows the voltage decrease due to the high current demand. In the second step, with a demanding power of just 1 KW, the voltage increases around 1.5 V  
As shown in Figure 21, the test starts at t = 0 s with 4.20 V per cell, which corresponds to a battery pack total voltage of 58.80 V (4.20 V \(\times\) 14 = 58.80 V).  
The test finishes at t = 4900 s with 3.42 V per cell, which corresponds to a battery pack total voltage of 47.88 V (3.42 V \(\times\) 14 = 47.88 V).  
This test results in an estimated autonomy of around 90 min (4900 s/60 s = 81.6 min).  
The battery cells cut-off voltage was stablished in 3.42 V to avoid unnecessary degradation of cells during the different endurance tests.  
This cut-off voltage will be lowered to 3.00 V [76] when real operations will take part.  
Taking into account this lowered cut-off voltage level, it can be assumed that the total estimated autonomy will overcome the 90 min goal, achieving the total time of 5500 s (5500 s/60 s = 91.66 min).
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
## 4 Real-Time Experiments and Analysis
### Time- and Shape-Related Performance Measures  
Performance evaluation has two main tasks: Evaluate the robustness of the system in terms of the control structure used and its settings and evaluate the impact of noise. Control robustness has traditionally been evaluated on the basis of frequency criteria and sensitivity functions. If we realize that its primary goal was to avoid oscillating and too slow solutions, the experimental evaluation in the time domain must be able to capture these two moments.  
In order to evaluate the responses in the presence of noise, the appropriate performance indicators should be used. The most basic indicator is the calculation of the total variation (\(TV\)) [13]. This is mostly used for evaluating excessive control effort, but in \(TV\) it is mixed together with the meaningful control activities. Thus, when wishing to get not just some data proportional to the excessive activities, but a performance measure offering a clear mathematical and physical interpretation, such a more detailed performance evaluation can be achieved by calculating the deviations from monotony. This can be calculated as total variation of a variable exceeding the expected meaningful variable changes. So, for example, for a monotonic output transition between \(y_{0}\) and \(y_{\infty}\) (as after the setpoint change in Figure 5) it may be derived as  
\[TV_{0}(y)=\sum_{i}|y_{i+1}-y_{i}|-|y_{\infty}-y_{0}| \tag{42}\]  
The input signal for a monotonic output change of the considered first order system [50] consists of two distinct monotonic intervals (signal increasing and decreasing and vice versa). Such a shape will be denoted as one-pulse (1P). In measuring deviations from 1P, we have to apply the previous evaluation from monotony twice. For example, for \(u(t)\) with initial and final values \(u_{0}\) and \(u_{\infty}\) and monotonic intervals separated by an extreme value (or an interval at a saturation, as in Figure 5) \(u_{m}\
otin(u_{0},u_{\infty})\)  
\[TV_{1}(u)=\sum_{i}|u_{i+1}-u_{i}|-|2u_{m}-u_{\infty}-u_{0}| \tag{43}\]  
In this figure, \(TV_{1}(u)\) corresponding to \(T_{c}=0.15\) will be significantly higher than for \(T_{c}=0.6\). The algorithm for the calculation of the performance indicators may be found in [20]. Performance measures corresponding to transients in Figure 5 are in Figure 6.
## 6 Application II: Fast Filtering of Time-Vertex Signals
### Time-Vertex Signals  
We consider a weighted, undirected graph \(G=\{\mathcal{V},\mathcal{E},\mathbf{W}_{G}\}\) with \(N\) vertices, where \(\mathcal{V}\) is the set of vertices, \(\mathcal{E}\) is the set of edges, and \(\mathbf{W}_{G}\) is the weighted adjacency matrix. The combinatorial graph Laplacian is defined as \(\mathbf{L}_{G}:=\mathbf{D}_{G}-\mathbf{W}_{G}\), where \(\mathbf{D}_{G}\) is diagonal with \(\mathbf{D}_{G}(i,i)\) equal to the degree of the \(i\)th vertex. At each vertex, we observe a time series of \(T\) observations. Thus, the time-varying graph signal can be represented as a matrix \(\mathbf{X}\in\mathbb{R}^{N\times T}\), where \(\mathbf{X}_{i,j}\) is the value on the \(i\)th vertex at the \(j\)th time. Figure 8 shows an example of a time-vertex signal.
## 7 Results
### Delay Analysis  
Due to the slow microSD card speed, a 200 ms delay was implemented after each image capture to prevent system delays. Time intervals between captures were measured using the board time in the image name, accounting for exposure and self-induced delays, with the name generated before imaging begins. Significant delays during mode transitions were observed, likely caused by changes in imaging parameters. To address this, outliers in the delay dataset were removed using the Interquartile Range method (IQR), defining outliers as delay values outside \(Q1-1.5\times IQR\) or \(Q3+1.5\times IQR\). Figure 9 shows the distribution of delays for high-resolution 2048 \(\times\) 2048 imaging. With the exception of a few anomalies, negligible or no delay was observed in both the low and medium-resolution modes.
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 4 Experimental Details and Analysis
### Comparisons on Satellite Video Datasets
#### 4.3.1 Quantitative Evaluation
Small-sized objects with similar surroundings may cause tracking drift and degrade tracking effects. Compared to SiamRPN++, RAMC gains the AUC by 5.9% of the success plot. In comparison to ECO, RAMC achieves a 7.8% gain in the success plot. Compared to CFME, RAMC also gains the AUC by 6% of the success plot. The LDES achieves promising performance, with an AUC of 0.724, ranking second in the success plot, since it employs a block coordinate descent (BCD) solver to find the best state for coping with illumination changes and deformations. However, RAMC improves the AUC by 5.0% in the success plot by extracting the motion information contained in adjacent \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Trackers** & **Framework** & **Features** & **MR** & **MTD** & **Success Plot** & **Precision Plot** & **FPS** \\ \hline KCF (TPAMI 2015) & KCF & HOC & – & – & 0.296 & 0.568 & **231.53** \\ SAMF (ECCV 2014) & KCF & HOG + CN & – & – & 0.373 & 0.702 & 48.03 \\ BACF (ICCV 2017) & KCF & HOG & – & – & 0.508 & 0.884 & 52.98 \\ STRCF (CVPR 2018) & DCF & ConvFeat + HOG + CN & \(\surd\) & – & 0.550 & 0.851 & 18.89 \\ AutoTrack (CVPR 2020) & DCF & HOG + CN & – & – & 0.606 & 0.916 & 51.90 \\ CFME (TGRS 2020) & KCF & HOG & – & \(\surd\) & 0.613 & 0.855 & **104.36** \\ ASRCF (CVPR 2019) & KCF & ConvFeat + HOG & \(\surd\) & – & 0.621 & 0.935 & 21.17 \\ SiamRPN (CVPR 2018) & SiameseFC & ConvFeat & \(\surd\) & – & 0.625 & 0.910 & 62.76 \\ Staple (CVPR 2016) & DCF + HI & HOG + CH & \(\surd\) & – & 0.625 & 0.856 & **85.32** \\ GPS-DCF (ICCV 2019) & DCF & ConvFeat + HOG + CN & \(\surd\) & – & 0.630 & 0.940 & 3.20 \\ LDES (AAAI 2019) & DCF & HOG + CH & \(\surd\) & – & 0.647 & 0.824 & 21.72 \\ ECO (CVPR 2017) & CCF & ConvFeat & \(\surd\) & – & **0.651** & **0.944** & 2.63 \\ SiamRPN++ (CVPR 2019) & SiameseFC & ConvFeat & \(\surd\) & – & **0.723** & **0.951** & 33.03 \\ RAMC (proposed) & KCF & HOG + OF & \(\surd\) & \(\surd\) & **0.785** & **0.946** & 42.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of trackers and experimental results on datasets. The top three of each metric is bolded. “MR” = Mechanisms for Rotation. “MTD” = Mechanisms for Tracking Drift. For trackers, “TGRS” = IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. For framework, “KCF” = Kernelized Correlation Filter, “DCF” = Discriminative Correlation Filter, “SiameseFC” = Fully Convolutional Siamese Network, “II” = Integral Image and “CCF” = Continuous Convolution Filter. For features, “HOG” = HOG, “CN” = Color Names, “ConvFeat” = Convolutional Features, “CH” = Color Histogram and “OF” = Optical Flow. Figure 9: Success plot (**a**) and precision plot (**b**) of all algorithms. frames. Overall, the proposed RAMC can synergize the AE of the rotation branch and OPR of the translation branch to achieve accurate and robust tracking.
## 3 Results and Discussion
### Overview on PM\({}_{2.5}\) Composition in the Investigated Area
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Urban-Industrial**} & \multicolumn{3}{c}{**Port**} \\ \hline
**(ng m\({}^{-3}\))** & **Median** & **Max** & **Min** & **Median** & **Max** & **Min** \\ \hline Al & 302 & 1221 & 14 & 242 & 1104 & 11 \\ Zn & 13 & 90 & 0.12 & 13 & 129 & 0.14 \\ Fe & 96 & 358 & 2.5 & 58 & 417 & 0.03 \\ K & 340 & 1728 & 21 & 144 & 1246 & 29 \\ Na & 387 & 2584 & 50 & 316 & 1620 & 51 \\ Ca & 322 & 1268 & 19 & 302 & 1805 & 19 \\ Ti & 20 & 78 & 4.3 & 21 & 46 & 3.6 \\ Mg & 102 & 744 & 30 & 85 & 852 & 21 \\ V & 2.6 & 7.5 & 0.7 & 3.0 & 27 & 0.32 \\ Ni & 2.9 & 20 & 0.02 & 1.7 & 11 & 0.14 \\ Cu & 3.2 & 22 & 1.3 & 1.7 & 13 & 0.19 \\ Pb & 4.2 & 16 & 1.5 & 3.1 & 43 & 1 \\ Cr & 0.8 & 10 & 0.02 & 0.36 & 2.1 & 0.02 \\ Cd & 0.07 & 0.24 & 0.02 & 0.06 & 0.20 & 0.02 \\ Mn & 2.7 & 7.0 & 0.21 & 1.8 & 7.5 & 0.04 \\ Sb & 1.4 & 30 & 0.12 & 0.7 & 26 & 0.10 \\ Cl\({}^{-}\) & 36 & 1021 & 0.48 & 26 & 410 & 1.7 \\ NO\({}_{3}\)\({}^{-}\) & 243 & 1150 & 29 & 170 & 1942 & 29 \\ SO\({}_{4}\)\({}^{2-}\) & 2868 & 7876 & 859 & 2999 & 7373 & 835 \\ C\({}_{2}\)O\({}_{4}\)\({}^{2-}\) & 121 & 244 & 32 & 130 & 304 & 41 \\ Na\({}^{+}\) & 251 & 837 & 36 & 221 & 472 & 52 \\ NH\({}_{4}\)\({}^{+}\) & 987 & 2272 & 256 & 1072 & 3879 & 177 \\ K\({}^{+}\) & 219 & 1663 & 39 & 125 & 1018 & 19 \\ Mg\({}^{2+}\) & 45 & 120 & 5.5 & 33 & 105 & 6.1 \\ Ca\({}^{2+}\) & 231 & 730 & 59 & 144 & 1375 & 31 \\ WSOC & 1480 & 5713 & 243 & 1276 & 2745 & 243 \\ WSIC & 46 & 249 & 27 & 27 & 854 & 27 \\ OC & 2977 & 6960 & 509 & 1751 & 5977 & 395 \\ EC & 521 & 1371 & 195 & 384 & 1173 & 0.15 \\ TC & 4300 & 8785 & 1588 & 2816 & 7557 & 462 \\ PM\({}_{2.5}\) (\(\mu\)g m\({}^{-3}\)) & 17 & 28 & 6.0 & 14 & 27 & 4.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of all determined parameters. The samples have been grouped for prevalent wind directions in two macro-sectors: Urban-Industrial (i.e., samples collected when the prevalent wind direction was from the city of Brindisi or the industrial area) and Port (i.e., samples collected when the prevalent wind direction was directly from the port).
# Universal Behavior of the Image Resolution for Different Scanning Trajectories
## 3 Results and Discussion
### Scanning Pixel Coverage  
The fill factor (FF) is one metric used to gauge how well scanning procedures are working. It measures the proportion of active pixels to all pixels. The pixels that the scanning trajectory covered or touched are referred to as active pixels in this context. In other words, if the scan's trajectory passes over a pixel, that pixel is considered active. The FF is expressed as a percentage, where 100% corresponds to complete coverage of all the pixels in the FOV by the scanning trajectory.  
The pixel size was estimated by the \(H_{max}\) of each case, as it is known that the number of pixels in each FOV was chosen by dividing the width and height to half of the \(H_{max}\) to accurately represent the image. For example, Figure 4a shows the FF for different scanning trajectories assessed using the BC pixel size for various \(N_{P}\) values ranging from 10 to 400.  
Figure 4: Fill factor as a function of \(N_{P}\) values for all scanning trajectories using (**a**) bidirectional Cartesian pixel size, (**b**) triangular Lissajous pixel size, (**c**) sinusoidal Lissajous pixel size, and (**d**) radial Lissajous pixel size.  
According to Figure 4a-d, the highest fill factor among the four mentioned trajectories is RL. This trend was expected due to the dense pattern formed by the RL scanning trajectory. However, one of the main drawbacks of RL is that it scans only the center circular/elliptical area of the FOV. This makes it inappropriate for scanning rectangular/square areas, like in our case.  
BC has shown the worst result in terms of FF, as was mentioned in the beginning. It has the lowest image resolution or highest \(H_{max}\) value compared to other scanning trajectories, which can be seen in Table 2 and Figure 3e.  
Comparing TL and SL, it is clearly seen from Figure 4a-d that the SL has higher FF, which makes it more preferable. Figure 4 shows that if the trajectory has a lower \(H_{max}\), then the FF will be higher. In other words, FF and \(H_{max}\) are inversely related to each other.