esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries and Notations
### Some Definitions and Lemmas  
Before the control system is designed, some necessary preliminaries regarding fixed-time stability are introduced. Consider the differential equation:  
\[\dot{\mathbf{x}}(t)=\mathbf{f}(\mathbf{x}(t)),\;\mathbf{x}(0)=\mathbf{x}_{0} \tag{1}\]where \(\mathbf{x}=[x_{1},\ldots x_{n}]^{T}\in\mathbb{R}^{n}\) denotes the state vector, and \(\mathbf{f}(\mathbf{x}):\mathbb{R}^{n}\rightarrow\mathbb{R}^{n}\) represents a known vector field.  
**Definition 1.**_The equilibrium point of (1) is globally finite-time stable, and it there exists a finite time \(T(\mathbf{x}_{0})\) that is uniformly bounded and independent of initial states, i.e., for all \(\mathbf{x}_{0}\in\mathbb{R}^{n}\), there exists \(T_{\max}>0\), then \(T(\mathbf{x}_{0})<T_{\max}\). Thus, it is globally fixed-time stable [22]._  
**Lemma 1.**_A continuously differentiable, positive-definite, radially unbounded function \(V\) satisfying \(\mathbb{R}^{n}\rightarrow\mathbb{R}^{+}\cup\{0\}\), thus [24]:_  
_(1) \(V(\mathbf{x})=0\Rightarrow\mathbf{x}=0\)_  
_(2) Any solution \(\mathbf{x}\) of system (1) satisfies:_  
\[\dot{V}(\mathbf{x})\leq-aV(\mathbf{x})^{p}-bV(\mathbf{x})^{q} \tag{2}\]  
_where \(a>0\), \(b>0\), and \(0<p<1\), \(q>1\). The origin is globally fixed-time stable for the system (1), and the reaching time satisfies the following:_  
\[T(\mathbf{x}_{0})\leq T_{\max}=\frac{1}{a(1-p)}+\frac{1}{b(q-1)},\forall\mathbf{x}_{0 }\in\mathbb{R}^{n}. \tag{3}\]  
**Lemma 2.**_Consider the system [25]:_  
\[\dot{\mathbf{y}}=-asig^{p}(\mathbf{y})-bsig^{q}(\mathbf{y}),\ \ \mathbf{y}(0)=\mathbf{y}_{0} \tag{4}\]  
_The equilibrium point of the above system (4) is fixed-time stable, and its setting time depends on Equation (3)._
# Clinical Validation of a New Enhanced Stent Imaging Method
## 3 Results
### ESI Image Quality Evaluation
This called for a more quantitative assessment of underexansion which we validated prospectively in the second part of this research project. \begin{table}
\begin{tabular}{l c c c c c} \hline  & \multicolumn{2}{c}{**Observer 1**} & \multicolumn{2}{c}{**Observer 2**} & \(p\)**-Value** \\  & **SE** & **SB** & **SE** & **SB** & \\ \hline
**Stent strut visibility** & 1.28 \(\pm\) 0.73 & 1.35 \(\pm\) 0.68 & 0.98 \(\pm\) 0.69 & 0.96 \(\pm\) 0.61 & \textless{}0.001 \\
**Proximal stent edge visibility** & 1.46 \(\pm\) 0.79 & 1.42 \(\pm\) 0.77 & 1.42 \(\pm\) 0.64 & 1.38 \(\pm\) 0.62 & NS \\
**Distal stent edge visibility** & 1.43 \(\pm\) 0.77 & 1.46 \(\pm\) 0.76 & 1.46 \(\pm\) 0.59 & 1.44 \(\pm\) 0.61 & NS \\
**Total score** & 4.10 \(\pm\) 1.86 & 4.12 \(\pm\) 1.73 & 3.76 \(\pm\) 1.58 & 3.67 \(\pm\) 1.49 & \textless{}0.001 \\ \hline
**Inter-observer Kappa coefficients (\(n\) = 249)** & & & & \\  & **SE** & & & & SB \\  & 0.456 & & & 0.344 & \\   Proximal edge visibility & 0.434 & & & 0.498 \\   Distal edge visibility & 0.416 & & & 0.386 \\   Stent underexansion & 0.394 & & & 0.480 \\   Califications & 0.352 & & & 0.447 \\ \hline
**Intra-observer Kappa coefficients (\(n\) = 50)** & & & & \\   Stent strut visibility & 0.760 & & 0.557 & \\   Proximal edge visibility & 0.629 & & 0.710 & \\   Distal edge visibility & 0.647 & & 0.783 & \\   Stent underexansion & 0.584 & & 0.473 & \\   Califications & 0.674 & & 0.477 & \\ \hline  & **NS** = non-significant; **SB** = StentBoost; **SE** = StentEnhancer. Mean values of parameters graded out of 2 points and total score graded out of 6 points. & & \\ \end{tabular}
\end{table} 
Table 3: Qualitative analysis of SB and SE. Figure 2: Underexpansion images before and after post-dilation (arrows). (**A**) StentBoost image before post-dilation; (**B**) StentEnhancer image before post-dilation; (**C**) StentBoost image after post-dilation; (**D**) StentEnhancer image after post-dilation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **SE** & **SB** & _p_**-Value** \\ \hline \multirow{4}{*}{**Seen-dilation**} & Stent strut visibility & 1.28 \(\pm\) 0.73 & 1.35 \(\pm\) 0.68 & NS \\  & Proximal edge visibility & 1.46 \(\pm\) 0.79 & 1.42 \(\pm\) 0.77 & NS \\  & Distal edge visibility & 1.43 \(\pm\) 0.77 & 1.35 \(\pm\) 0.68 & NS \\  & Total score & 4.10 \(\pm\) 01.86 & 4.12 \(\pm\) 1.73 & NS \\ \hline \multirow{4}{*}{**Seen-dilation**} & Stent strut visibility & 0.98 \(\pm\) 0.69 & 0.96 \(\pm\) 0.61 & NS \\  & Proximal edge visibility & 1.42 \(\pm\) 0.64 & 1.38 \(\pm\) 0.62 & NS \\  & Distal edge visibility & 1.46 \(\pm\) 0.59 & 1.44 \(\pm\) 0.61 & NS \\  & Total score & 3.76 \(\pm\) 1.58 & 3.67 \(\pm\) 1.49 & NS \\ \hline \hline \end{tabular}  
**NS** = non-significant; **SB** = StentBoost; **SE** = StentEnhancer. Mean values of parameters graded out of 2 points and total score graded out of 6 points.  
\end{table} 
Table 4: Different mean values of parameters by observer.
## 4 Experimental Section
### Detailed Results on Different Types of Sentences  
To further validate the capability of REACT in extracting overlapping relational triples, we conducted two extended experiments on different types of sentences and compared the performance of REACT with that of the baselines.  
The detailed experimental results on three different datasets are shown in Figure 12. It can be seen that REACT achieved the best results in all three cases, proving that it has some advantages in addressing the relation overlapping problem.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Models** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & DE+CNN+EAM+EGM & 70.7 & 40.5 & 51.5 \\
2 & DE+LSTM+EAM+EGM & 74.3 & 44.7 & 55.8 \\
3 & DE+BiLSTM+EAM+EGM & 74.6 & 46.7 & 57.4 \\
4 & [PERSON]+CNN+EAM+EGM & 67.5 & 62.0 & 64.6 \\
5 & [PERSON]+LSTM+EAM+EGM & 68.3 & 63.7 & 65.9 \\
6 & [PERSON]+BiLSTM & 64.8 & 63.8 & 64.1 \\
7 & [PERSON]+BiLSTM+EAM+EGM & 69.0 & 64.3 & 66.5 \\
8 & [PERSON]+CNN+EAM+EGM & 69.2 & 60.1 & 64.3 \\
9 & [PERSON]+LSTM & 65.7 & 62.2 & 63.9 \\
10 & [PERSON]+LSTM+EAM & 66.4 & 65.1 & 65.7 \\
11 & [PERSON]+LSTM+EAM+EGM & 68.1 & 64.8 & 66.4 \\
12 & [PERSON]+BiLSTM & 66.2 & 63.9 & 65.0 \\
13 & [PERSON]+BiLSTM+EAM & 67.6 & 65.3 & 66.4 \\
14 & [PERSON]+BiLSTM+EGM & 67.8 & 64.7 & 66.2 \\
15 & (REACT) [PERSON]+BiLSTM+EAM+EGM & 68.5 & 66.0 & 67.2 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental results between REACT and the 14 model variants.  
We validated REACT's ability to extract relational triples from data containing different numbers of relational triples. Sentences were categorized into four classes, and the results are shown in Table 6. Again, REACT achieved excellent performance across all four classes. Although it is not surprising that the performance of most baselines decreased as the number of relational triples in a sentence increased, there are still some details that can be observed in the variation of the models' performance. Compared to these baselines, which are dedicated to addressing the relation overlapping problem, REACT exhibited the least degradation and achieved the best performance when confronted with complex situations.
## 4 Experimental Validation on Public Datasets
### Remaining Useful Life (RUL) Prediction
In terms of feature fusion technology, we propose a multi-dimensional mixed gating attention mechanism to overcome the limitations of traditional attention mechanisms in multi-dimensional feature interaction. By performing feature fusion across spatial and channel dimensions, this approach adaptively adjusts fusion weights, significantly enhancing the overall performance of the constructed health indicators.
# SDN-Based Routing for Backhauling in Ultra-Dense Networks
## 2 Related Literature
### Paper Contribution  
From above we can summarize that routing policies, specifically designed for UDNs backhauling, have not been yet widely investigated. The literature mainly refers to routing in wired and fixed networks, where the common goal is to minimize the maximum link use, thus avoiding that some links are particularly congested while others are underused. However, sufficient capacity to admit a new flow is always assumed.  
Differently, this paper focuses on UDNs taking into account that: (i) UDNs backhaul network may not be able to support all the traffic demand generated by cells, (ii) UDNs backhaul network is characterized by an irregular topology, changing in time and space, made by heterogeneous links and multi-hop connections.  
Hence, the paper contribution can be summarized as  
* a new optimization problem is defined for a capacity-limited backhauling in UDNs. In particular, the problem considers two types of traffic (data and control) according to the SDN paradigm, and aims at guaranteeing control-flow capacity demands and minimizing the amount of _unsatisfied data-flow demands_. This is the amount of data-flow capacity requested by cells that cannot be supported by the backhaul network. Moreover, the aim is fairly distributing the eventual lack of bandwidth (i.e., _unsatisfied requests_) among cells.
* a new algorithm is proposed to solve the problem. It differentiates the network behavior for control and data traffic, to satisfy their different constraints. Moreover, it takes into account not only the capacity of each link, but also its congestion. In particular, we propose a two-step algorithm based on a modified Dijkstra algorithm. It is designed for an SDN implementation that requires a global knowledge of the network and is performed centrally at the SDN controller.  
The paper is organized as follows. First we introduce the main concepts of SDN in Section 3, then the considered scenario is described in Section 4. Section 5 presents the problem formulation whose solution is proposed in Section 6. Finally, numerical results are presented in Section 7 and conclusions are drawn in Section 8.
## 1 Introduction
As a consequence of this, it will not flow and its degree of crosslinking will be more than 90%. Literature analysis revealed that, there are no known studies on the combined effect of both electron radiation and natural hemp fibers on PCL properties, in particular, mechanical ones. Admittedly, some similar studies can be found in the literature, however, they concern either other types of natural fibers or different polymers. For example, [PERSON] et al. investigated the effect of electron radiation on the properties of PLA [52] or PCL [53] containing 20% flax fibers with the addition of 1% to 3% TAIC. Understanding the combined effect of electron radiation and hemp fibers on the structure and mechanical properties of PCL is interesting from both cognitive and utilisation aspects. From the cognitive aspect it is important to know the relationship between the radiation dose, the amount of fibers in the polymer matrix and the properties of the obtained materials. From the utilisation aspect, modification of PCL with hemp fibers and electron irradiation has been carried out to obtain new materials susceptible to biodegradation. Furthermore, the use of hemp fibers in our study may assist in reducing the adverse effects of PCL's partial crosslinking and on its biodegradability. This inspired the authors of the present article to undertake investigations aimed at the comparison of the structure and mechanical properties of PCL/HF composites modified by electron radiation with doses in the range of 10-90 kGy. The comparison of changes in (i) the structure of the fibers extracted from obtained composites; (ii) the surface geometrical structure of sample fractures; (iii) the tensile strength; (iv) the elongation at break; (v) the flexural modulus; and (vi) the impact strength have been presented in this paper.
## 2 Materials and Methods
### Chemical Characterization of Components and PMBs  
Chemical characterization of component materials and of prepared PMBs was carried out with the purpose of investigating the potential interactions occurring between bitumen and polymers after mixing. Such a preliminary evaluation was deemed necessary since previous studies have proven that the final characteristics and stability of PMBs are dependent on the type of local interactions (i.e., physical or chemical) and on the relative miscibility of employed components [20; 21; 22].  
Neat bitumen was subjected to thin layer chromatography (TLC) analyses, also known as SARA analyses, for the evaluation of the relative percentages of Saturates, Aromatics, Resins, and Asphaltenes [23; 24]. In particular, tests were carried out by making use of the Iatroscan equipment (model MK-6), which requires the injection of small quantities (1 \(\mu\)L) of bitumen-dichloromethane solutions (10 mg/mL concentration) in porous quartz rods. Bitumen fractions are then identified by sequentially immersing the rod tips in a set of tanks containing n-hexane, toluene, and a mixture of dichloromethane (95%) and methyl alcohol (5%) [25]. The percentages of the four fractions are then assessed by means of a flame ionization detection (FID) unit, which measures the electrical current produced by combustion in hydrogen flame. SARA analyses were performed on the neat binder both in its unaged and RTFO-aged state.  
Component materials and PMBs were subjected to Fourier Transform Infrared Spectroscopy (FTIR) analyses for the identification of the main organic compounds and chemical groups [26]. Investigations were carried out with the Shimadzu 8400S equipment, which relies on attenuated total reflectance (ATR) technology and allows absorbance spectra to be recorded in the wavenumber range of 400 cm\({}^{-1}\) to 400 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\). In all cases, tests were performed on solid samples (1 g) directly placed on the scanning plate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Binder Code** & **Polymer Type** & **Polymer Quality** & **Polymer Dosage (\%)** \\ \hline NB & - & - & 0 \\ \hline SW3 & & Waste & 3.0 \\ SQ3 & & Premium quality & 3.0 \\ SW4 & SBR & Waste & 4.0 \\ SQ4 & & Premium quality & 4.0 \\ SW5 & & Waste & 5.0 \\ \hline PW3 & & Waste & 3.0 \\ PQ3 & & Premium quality & 3.0 \\ PW4 & PBR & Waste & 4.0 \\ PQ4 & & Premium quality & 4.0 \\ PW5 & & Waste & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bituminous binders considered in the investigation.
# Identification of Location and Camera Parameters for Public Live Streaming Web Cameras
## 4 Formulation of the Problem and Its Solution Algorithm
### Solution Algorithm
#### 4.1.2 Obtain the Radial Distortion Compensation Map  
Let \(\hat{\Phi}\) be the image obtained from \(\Phi\) by the transformation (10) and solution of Equations (11)-(13). Denote \(\Theta\) the mapping of \(\Phi\) to \(\hat{\Phi}\):  
\[\hat{\Phi}=(\hat{u},\hat{v})=\Theta(u,v;k_{1},k_{2},f_{u},f_{v},c_{u},c_{v})=( f_{u}x_{c}/z_{c}+c_{u}f_{v}y_{c}/z_{c}+c_{v}). \tag{17}\]  
We create the image \(\hat{\Phi}\) according to the pinhole camera model. The model transforms a straight line in space into a straight line in the image.  
Let \(P_{im}^{a}=(u^{a},v^{a}),P_{im}^{b}=(u^{b},v^{b})\), and \(P_{im}^{c}=(u^{c},v^{c})\) in \(F_{im}\) and  
\[\hat{P}_{im}^{a}=(\hat{u}^{a},\hat{v}^{a})=\Theta(u^{a},v^{a}),\hat{P}_{im}^{b} =(\hat{u}^{b},\hat{v}^{b})=\Theta(u^{b},v^{b}),\hat{P}_{im}^{c}=(\hat{u}^{c}, \hat{v}^{c})=\Theta(u^{c},v^{c}). \tag{18}\]  
We can find \(k_{1}\) and \(k_{2}\) values that minimize the sum of distances from pixels \(\hat{P}_{im}^{b}\) to the lines passing through \(\hat{P}_{im}^{a}\) and \(\hat{P}_{im}^{c}\). We calculate the distance as:  
\[\frac{|(\hat{u}^{c}-\hat{u}^{a})(\hat{v}^{a}-\hat{v}^{b})-(\hat{u}^{a}-\hat{u }^{b})(\hat{v}^{c}-\hat{v}^{a})|}{\sqrt{(\hat{u}^{a}-\hat{u}^{c})^{2}+(\hat{v} ^{a}-\hat{v}^{c})^{2}}} \tag{19}\]  
for each triplet \((P_{im}^{a},P_{im}^{b},P_{im}^{c})\). This approach is a variant of the one described, for example, in [7] (pp. 189-194). OpenCV offers the realization of \(\Theta^{-1}\) (initUndistorRectifyMap). It is fast, but we need to invert it for our case. We solve Equations (11)-(13) (including version for \(v=c_{v},u\
eq c_{u}\)) to obtain \(\Theta\). The \(\Theta^{-1}\) is polinomial from \(\hat{u},\hat{v}\) (see (7)-(10)).
## 3 Yaw Angle Control Design
### Design of DE  
By referring to the literature [41], the design of the DE for estimating the nonlinear term \(f_{r}\) can be divided into the following steps:  
Step 1: design of the nominal model  
In the absence of \(f_{r}\) in Formula (23), the nominal model in the continuous-time domain can be given using  
\[\dot{r}_{m}=b_{r}\delta_{r} \tag{24}\]  
The discrete-time version is written as  
\[r_{m}(k+1)=r_{m}(k)+Tb_{r}\delta_{r}(k) \tag{25}\]  
where \(r_{m}\) is the state of the nominal model, and \(T\) is the sampling period;  
Step 2: DE formulation  
The DE is designed as follows:  
\[\left\{\begin{array}{l}\hat{f}_{r}(k)=\frac{\Delta r_{r}(k)}{I}+\hat{\phi}(k) \cdot\Delta\delta_{r}(k)\\ \\ \hat{\phi}(k)=\hat{\phi}(k-1)+\frac{\eta\left[\Delta r(k)-\hat{\phi}(k-1) \Delta\delta_{r}(k-1)\right]\Delta\delta_{r}(k-1)}{\mu+\left|\Delta\delta_{r}( k-1)\right|^{2}}\end{array}\right. \tag{26}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach Category** & **Literatures** & **Defects** \\ \hline LOS & [2; 3; 4; 5; 6; 7] & Poor robustness, no attitude control system \\ vector field & [8; 9; 10; 11; 12; 13; 14] & Complicated theories, Poor robustness, no attitude control system \\ virtual target following & [15; 16; 17; 18; 19; 20; 21] & Too many virtual targets, poor robustness, no attitude control system \\ L\({}_{1}\) guidance & [22; 23; 24; 25; 26; 27; 28] & Poor robustness, no attitude control system \\ Frenet & [21; 29; 30; 31; 32] & Poor robustness, no attitude control system \\ \hline \hline \end{tabular}
\end{table}
Table 1: The existing approaches and their defects.  
where \(\hat{f}_{r}\) is the estimated value of \(f_{r}\), \(\epsilon_{r}(k)=r(k)-r_{m}(k)\), \(\Delta\epsilon_{r}(k)=\epsilon_{r}(k)-\epsilon_{r}(k-1)\), \(\Delta\delta_{r}(k)=\delta_{r}(k)-\delta_{r}(k-1)\), \(\Delta r(k)=r(k)-r(k-1)\), \(\mu>0\), \(\eta\in(0,2]\), and \(\hat{\phi}(0)\) is specified by users.  
A stability analysis of the DE can be seen in Appendix C in [42].
# The Impact of Polycentric Structure on CO\({}_{2}\) Emissions: Evidence from China
## 3 Results
### Heterogeneity Analysis  
The spatial structures and CO\({}_{2}\) emissions were heterogeneous because of the different features in each city. We emphasized the heterogeneity resulting in the administrative hierarchy and the dependence on resources in this context. First, we grouped the sample into municipalities directly under the central government and other cities. Second, we hypothesized that the impact of polycentricity on CO\({}_{2}\) varied between resource-based cities and others because of the resource curse. The empirical results were presented in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** \\  & **Carbon** & **Carbon** & **Carbon** & **Carbon** & **Carbon** & **Carbon** \\ \hline Q & \(-0.052\)*** & \(-0.051\)** & \(-0.050\)*** & \(-0.050\)*** & \(-0.050\)*** & \(-0.051\)*** \\  & (\(-2.77\)) & (\(-2.55\)) & (\(-2.60\)) & (\(-2.60\)) & (\(-2.59\)) & (\(-2.67\)) \\ Ingreencov & 0.030 & & & & & 0.031 \\  & (1.43) & & & & & (1.38) \\ \multirow{2}{*}{Inlpg} & & 0.003 & & & & 0.002 \\  & & (0.004) & & & & (0.41) \\ \multirow{2}{*}{Inws} & & & \(-0.005\) & & & \(-0.014\) \\  & & & (0.047) & & & (\(-0.28\)) \\ \multirow{2}{*}{Inhum} & & & & \(-0.162\)* & & \(-0.162\)* \\  & & & & & (\(-1.91\)) & (\(-1.81\)) \\ \multirow{2}{*}{Intem} & & & & & 0.022 & \(-0.007\) \\  & & & & & (0.85) & (\(-0.30\)) \\ Control & YES & YES & YES & YES & YES & YES \\ City FE & YES & YES & YES & YES & YES & YES \\ Year FE & YES & YES & YES & YES & YES & YES \\ Observations & 4473 & 4400 & 4513 & 4510 & 4364 \\ R-squared & 0.948 & 0.947 & 0.947 & 0.947 & 0.948 \\ \hline \hline \end{tabular} Notes: t-values are in parentheses; *, *, and *** represent the 10%, 5%, and 1% significance levels, respectively; the coefficients of the control variables (i.e., the secondary industry as a percentage of GDP, foreign direct investment, population density, the percentage of budget fiscal expenditure and technology expenditure on GDP, and the ratio of the employed population), year and time dummies are not reported to save space.  
\end{table}
Table 6: Robustness check: The impact of polycentric spatial structures on CO\({}_{2}\) emissions.  
Columns (1) and (2) in Table 8 compared the impact of polycentric structure on the CO\({}_{2}\) emissions of municipalities directly under the central government and the others. The results demonstrated that polycentricity had no significant impact on the CO\({}_{2}\) emissions of municipalities while reduced the CO\({}_{2}\) emissions of other cities. Columns (3) and (4) presented the relationship between polycentric structure and CO\({}_{2}\) emissions in resource-based cities and other cities, respectively. The results indicated that the polycentric structure affected CO\({}_{2}\) emissions neither in municipalities nor resource-based cities while significantly reduced CO\({}_{2}\) emissions in other cities.
## 3 Literature Review
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Dataset Details** & **Paper Findings** & **Limitations** & **Future Work** \\ \hline VRAI [12] & A total 137,613 images of 13,022 vehicles with & Outperforms existing methods in vehicle ReID & Comparison scope, domain specificity, annotation complexity, scalability, and real-world & Explore transfer learning, enhance scalability, integrate advanced techniques, focus on real-world applications, and improve annotation strategies. \\ \hline FOR-Instance [13] & Five collections from & Supports both instance & Potential overfitting, lack of generalizability to other forest types, challenges with unclassified points. & Explore transfer learning, enhance scalability, integrate advanced techniques, focus on real-world applications, and improve annotation strategies. \\ \hline FOR-Instance [13] & Five collections from & Supports both instance & Potential overfitting, lack of generalizability to other forest types, challenges with unclassified points. & Incorporate more data types, develop advanced deep learning architectures, study tree species classification, and conduct longitudinal studies on forest changes. \\ \hline VERI-Wild [14] & Over 400,000 images of 40,671 vehicle IDs & FDA-Net outperforms existing methods, achieving highest Rank-1 and Rank-5 accuracies. & Potential biases due to urban district focus and dataset-specific adversarial scheme. & Explore more challenging real-world factors, generate comprehensive datasets, and leverage GANs to improve cross-view ReID performance. \\ \hline UAV-Assistant [15] & Data synthesis pipeline & Smooth silhouette loss & Lack of real-world data & Optimize parameters, explore additional loss \\  & combining egocentric & enhances 3D pose & poses a challenge to generalizability, and determining optimal kernel size for smoothing filter. & Functions, and validate approach in real-world scenarios. \\  & & & & \\ \hline UAV-Gesture [17] & Contains 119 & Annotates body joints & Limited gesture set and non-expert actors may & Leverage dataset for gesture and action recognition in UAV control, expand and refine dataset for broader research applications. \\ \hline UAV-Gesture [17] & Contains 119 & Annotates body joints & Limited gesture set and non-expert actors may & Leverage dataset for gesture and action recognition in UAV control, expand and refine dataset for broader research applications. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of research and findings of UAV datasets discussed.