esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Case Study 2-- ITMI: IoT  
Due to the immense value IoT brings to every organization, ITMI refers to the use of IoT in improving existing systems and processes. This enables it to increase operational efficiency, create better experiences, and unlock additional value for the running projects. For ITMI, the IoT and industry 4.0 reflect a growing focus on driving results using sensor-based data and creating analytically rich data sets.  
Figure 16 illustrates ITMI's IoT system architecture. Note that this case study continues the previous case study. One of the significant IoT projects maintained by ITMI, is the Inspection of the Railways of Quebec (IRQ) in Canada to detect any unrevealed cracks in railway tracks to avoid accidents. To achieve this, a rail robot (which is accomplished in case study 1) and a drone are connected or paired together so that they are synchronized and well geolocated. Both are connected to the internet via cellular or satellite networks depending on where the inspection is taking place. The sensor nodes attached to the rail  
Figure 14: Case Study 1: Examples of association relationship properties for role permissions.  
Figure 15: Case Study 1: Examples of NGAC authorization responses to Cypher statements.  
robot are used to inspect the railways and take images of cracks and technical problems. Synchronously, the drone also captures images of locations where cracks in railway tracks exist. All data and captured images are sent to ITMI's cloud server. In this case study, AC is based on users' roles, in addition to other attributes of subjects, objects, and the environment. Those who have access to the labs 24/7 can take out the Rail Robot and the Drone and operate them on site. This is when the status of this task is in progress and within the start and end date of the running project. Onsite, users must log into the machines using PIN codes, they have three possible attempts to use the correct PIN code to operate and control the machines. Otherwise, the machines, using a GPS tracking system, send alert messages with the geographical coordinates of the location to the manager, specialists, and technicians. In the following we describe the guidelines to perform the project tasks:  
* The specialists [PERSON] and [PERSON] with the technician [PERSON] can operate the Rail Robot and the Drone using the IoT device controller to control the machines and read the collected data and images.
* The adviser [PERSON] has full access to database tables at the ITMI cloud server where the collected data and images from the site are received from the Rail Robot and the Drone. He analyzes the obtained information, then writes/inserts the needed report information to the table Results.
* Before emailing the reports to the company maintaining the railways, the manager [PERSON] must read (and modify if needed), and confirm the report(s).  
All users are allowed to perform the specified actions if the task status is in progress and within the project start and end dates.
# A Full-Duplex LED-to-LED Visible Light Communication System
## 5 Summary  
In summary, we investigated the performance dependency on the wavelength of the transmitter and the receiver LED for LED-to-LED VLC. In order to find the optimal color set of transmitter and receiver LEDs according to [PERSON]'s channel capacity law, we measured the SNR and rise time of the LED-to-LED VLC system with all possible color combinations for the transmitter and receiver LEDs. Through the optimization process, we found out the optimal LED color set for LED-to-LED VLC, which was a green-to-yellow color set.  
In addition, we experimentally demonstrated half- and full-duplex LED-to-LED VLC using a green LED and a yellow LED as the transmitter and the receiver, respectively. The maximum data rate of 49 kbps with a BER of \(<2.5\times 10^{-4}\) was achieved using 32-QAM DCO-OFDM modulation and a reverse-biased circuit in a full-duplex LED-to-LED VLC system. We also investigated the interference effect in the full-duplex LED-to-LED VLC analytically and experimentally. We believe that LED-to-LED VLC technology will contribute to the wide spread of the VLC technology.  
Conceptualization, S.-M.K.; methodology, H.J.; software, H.J.; validation, H.J. and S.-M.K.; formal analysis, H.J.; investigation, [PERSON]; resources, [PERSON] and S.-M.K.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, S.-M.K.; visualization, [PERSON]; supervision, S.-M.K.; project administration, S.-M.K.; funding acquisition, S.-M.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1045956).  
The authors declare no conflict of interest.
## 2 Methodologies
### Stochastic Configuration Network (SCN)
#### 2.1.2 Network Model Construction  
If \(||\epsilon_{L-1}||^{2}>\varepsilon\) or \(L<L_{max}\), then add the \(L_{n}\) node. The input weights and biases are determined by the Universal Approximation Property according to formula Equation (3), as follows:  
\[h_{L}=\left[g_{L}\left(\omega_{L}^{T}x_{1}+b_{L}\right),g_{L}\left(\omega_{L} ^{T}x_{2}+b_{L}\right),\ldots,g_{L}\left(\omega_{L}^{T}x_{N}+b_{L}\right) \right]^{T} \tag{3}\]  
\[\xi_{Lq}=\frac{\left\langle\epsilon_{L-1,q^{\prime}}^{T},h_{L}\right\rangle^{ 2}}{{h_{L}}^{T},h_{L}}-(1-r-\mu_{L})\left\|\epsilon_{L-1,q}\right\|^{2} \tag{4}\]  
where \(q=1,2,\ldots,m;r\in(0,1)\), \(h_{L}\) denotes the output of the \(L\)-th node in the implicit layer. Let \(w_{j}\) and \(b_{j}\) represent the candidate parameters of the \(L\)-th node. Given a non-negative real number sequence \(\{\mu_{L}\}\) with \(\lim\limits_{L\rightarrow+\infty}\left\|f-f_{L}\right\|=0\) and \(\mu_{L}\leq 1-r\). The parameter of the candidate node that satisfies the condition \(\xi_{L}=\sum_{q=1}^{m}\xi_{L,q}\geq 0\) with the maximum value is used as the parameter for the \(L\) node.  
The output weight is determined through a least squares evaluation, as follows:  
\[\beta=arg\ \underset{\beta}{\text{min}}\left\|H\beta-Y\right\|^{2}=H^{+}Y \tag{5}\]  
where \(\left\|\cdot\right\|_{F}^{2}\) denotes the Frobenius norm, while \(H^{+}\) represents the Moore-Penrose generalized inverse matrix of \(H\).
# An Overview of Vehicular Communications
## 4 Vehicle-To-Everything
Furthermore, the values of the packet delivery rate and latency in the scanning period are not as good as in the literature. Therefore, there has been the need for  
Figure 2: V2V, V2I, and V2X communications. native technologies for vehicular communications. In particular, for instance, already several years ago, General Motors evaluated the capabilities of a car equipped with DSRC [64], in V2X contexts [65]. Regarding the V2X and PCW architectures, it can be practiced a prevention system based on cameras and Visible Light Communication (VLC) [66; 67; 68; 69]. Nevertheless, this solution is severely influenced by environmental circumstances, because if the weather or road conditions are not right, it can be difficult to obtain accurate data. Furthermore, there are various computational limits in three-dimensional recognition and errors in image processing. Another PCW solution can be represented by a driving assistance system, such as Adaptive Cruise Control (ACC) [70] and the AEB [71], employing millimeter-wave radar [72], LiDAR (Laser Imaging Detection and Ranging) [73], and other specific sensors in the vehicle. These sensors obtain relative speed and distance from objects, calculating the difference in time and frequency respectively between the signals sent and those received. However, a limitation is represented by any weak signals covered by noise or by the application at high travel speeds and the distance between vehicles, which makes the system considered inadequate in complex scenarios. It can be adopted an approach based on Bluetooth Low Energy (BLE) [74] to overcome the limits of Wi-Fi, VLC, and control sensors. This wireless standard can be useful in PCW environments as it allows bidirectional communications with the lowest power consumption and with good reception values. Moreover, it is already installed in all the latest generation smartphones and aims to be the communication standard for the Internet of Things (IoT) [75]. Finally, considering the evolution of the LTE-V2X and the emerging 5G [76], the entirely automated driving and innovative vehicular services can be accomplished with joint innovation through cross-industry collaboration.
## 2 Materials and Methods
### Statistical Analysis  
All statistical analyses were performed using Statistica version 10 (StatSoft Inc., 2011, Tusla, OK, USA). Descriptive data analysis was applied to assess the mean and standard error. A nonparametric Mann-Whitney U test was performed to estimate the significant differences between the control (MRS_C) and each experimental group. \(p\) values less than 0.05 were assumed as statistically significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Abbreviation** & **Full Name** & **Forward Primer** & **Reverse Primer** & **Product Size (bp)** \\ \hline PPARy NM\_001127330.2 & Peroxiosome proliferatorator-activated receptor gamma, transcript variant 2 & AGGGCATCTGACGGAAA & CGAAACTGGCACCCTTGAAA & 164 \\ \hline Gps2 NM\_001357906.2 & G protein pathway suppressor 2 & ACAAGTGCCTTACGACCCGG & GGAAATGCCTGATGGGCCT & 183 \\ \hline Slc2a1 NM\_011400.3 & Solute carrier family 2 (faciliated glucose transporter), member 1 & GGAATGCGCGTGTGCATCCT & TCTTCAGCACACTCTTGGCC & 202 \\ \hline IRS-1 NM\_010570.4 & Insulin receptor substrate 1 & TTAGGACCATGGGCCA & CGTGGTCTGTTGCAA & 212 \\ \hline PI3K NM\_001077495.2 & Phosphoinositide-3-kinase regulatory subunit 1 & ATACTGTGCTGACC & GCTCTGCTTCCCTGCAAATA & 193 \\ \hline Slc2a4 NM\_009204.2 & Solute carrier family 2 (faciliated glucose transporter), member 4 & CGTTGGTGTCCGGTCCTTTA & AGCTCTGCCA AATGAACA & 220 \\ \hline
3b4 NM\_007475.5 & Ribosomal protein, large, P0 & TTATAACCCTGAGTCTGACCATGATG & GCCTTGTACCATTATGATG & 147 \\ \hline Actb NM\_007393.5 & \(\beta\)-actin & CCTCATGTCCAACACAGICC & GTACTCTGCTGATCC & 211 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primer sequences used for Real-Time PCR (q-PCR) analysis.
## 6 Summary
### Nomenclature  
\begin{tabular}{l l} \(A\) & cross section area packed bed, m\({}^{2}\) \\ \(a\) & lattice constant metal oxide crystal, m \\ \(a_{p}\) & specific surface area per unit volume pellet, m\({}^{-1}\) \\ \(a_{g}\) & specific surface area per unit volume grains, m\({}^{-1}\) \\ \(C\) & concentration, mol/m\({}^{3}\) \\ \(C_{\text{MO}}\) & molar concentration solid metal oxide, mol/m\({}^{3}\) \\ \(D\) & diffusion coefficient, m\({}^{2}\)/s \\ \(d_{0}\) & reactor diameter, m \\ \(d_{p}\) & pellet size, m \\ \(E\) & axial dispersion coefficient packed bed, m\({}^{2}\)/s \\ \(E_{a}\) & activation energy, J/mol \\ \(G\) & volume expansion,m\({}^{3}\)/kg \\ \(k_{Df}\) & mass transfer coefficient diffusion process, m/s \\ \(k_{g}\) & external mass transfer coefficient, m/s \\ \(k_{Rx}\) & mass transfer coefficient reaction process, m/s \\ \(k_{s}\) & kinetic rate function surface reaction, m/s \\ \(k_{s}^{\prime}\) & kinetic rate function, m\({}^{4}\)/(mol s) \\ \(k_{a}\) & deactivation rate constant, (mol/m\({}^{3}\))\({}^{-nr^{\prime}}\) \\ \(k_{0}\) & kinetic prefactor, m/s \\ \(L\) & bed length, m \\ \(M\) & molecular weight, kg/mol \\ \(m_{s}\) & mass of sorbent, kg \\ \(N\) & molar flux, mol/m\({}^{2}\)/s \\ \(Q\) & volumetric gas flow rate, m\({}^{3}\)/s \\ \(R\) & gas constant, \(R=8.315\) J/(mol K) \\ \(R_{g}\) & grain radius, m \\ \(R_{p}\) & pellet radius, m \\ \(r\) & reaction rate, mol/(m\({}^{2}\) s) \\ \(S\) & specific surface area, m\({}^{2}\)/kg \\ \(T\) & temperature, K \\ \(u_{int}\) & interstitial velocity, \(u_{int}=Q/(\varepsilon_{b}A)\), m/s \\ \(V\) & Volume reactor shell, \(V=AL\), m\({}^{3}\) \\ \(X\) & conversion of solid sorbent, - \\ \(x_{i}\) & weight fraction metal oxide \(i\), - \\ \end{tabular}
## 3 System Model and Problem Formulation
### Lifetime Model  
Network lifetime [27] is concerned with the period in which the network can maintain its desired functionality. It can be defined as the time till the first node in the network dies, called nodal lifetime; or it can also be defined as the time till a certain proportion of the nodes die. We will use the former definition as sensor network lifetime in the subsequent discussions in the rest of this paper.  
For each sensor node in the network, its energy consumption may involve many factors. For simplicity, here we neglect the impact of other secondary factors and only attach importance to three main factors: transmitting data, receiving data and aggregating data. Therefore for an undirected graph _G(V,E)_, the nodal lifetime of node \(\
u(\
u\in\)_V_) can be described as follows:  
\[l\left(G,v\right)=\frac{E_{r}\left(v\right)}{\sum f+\sum E_{r}+\sum E_{R}} \tag{6}\]  
where \(E_{r}(v)\) indicates residual energy of node \(v\). \(\sum E_{r}\), \(\sum E_{R}\) and \(\sum f\) are the energy of node \(v\) used to transmit data, receive data and aggregate data if it is a aggregation point. According to previous description, a network ends up with the first node depleting its energy, so we can easily formulate network lifetime as follows [28,29]:  
\[l\left(G\right)=\min_{v\in\
u}l\left(G,v\right) \tag{7}\]  
From the above formulas, it can be obviously seen what kinds of factors are significant to the uneven energy consumption issue, which can help us to explore a more effective algorithm to extend network lifetime in the right direction.
## 9 Results and Findings
### The Majority of Interfaces Are Not Designed for Users with Disabilities
#### 9.1.1 Interfaces Do Not Consider Issues Pertaining to Visual Impairments  
Visual impairments were not taken into consideration. In some cases, the use of color proved ineffective for people with color vision defect, thus making the task of identifying certain actions using color too complicated for this type of user. This issue can be seen in Figure 19 below; a user who perceives color is able to easily identify which button should be pressed, whereas those individuals with color vision defect find the task of differentiating buttons virtually impossible.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Problems \& Findings** & **Related Guidelines** \\ \hline \hline  & - Alternate and complementary digital resources \\  & - Help \& instructions \\
6. The interface requires ‘independent’ add-ons in & - Efficiency \\  & order to offer omnichannel processes & - Cognitive processes \\  & - Interface feedback \\  & - Error recovery \\  & - Error prevention \\ \hline  & - Efficiency \\
7. Greater focus on efficiency than on learning process & - Cognitive processes \\  & - Error recovery \\  & - Error prevention \\  & - Interface feedback \\ \hline  & - Cognitive processes \\
8. Face-to-face human ‘support staff’ become & - Error recovery \\   indispensable & - Error prevention \\  & - Interface feedback \\  & - Help \& instructions \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 19: San Pedro Garza Garcia’s parking meter interface. On the left side (**a**), the buttons’ colors are obvious for a person with color vision; on the right (**b**) a person with color vision defect cannot identify actions.  
This problem translates into longer decision-making and interaction times. A person without any form of visual impairment takes under one second to respond, whilst a person with color vision defect takes on average four seconds to respond. In addition, 40% of these users also fail to choose the correct option.
## 3 CCUS3O Model  
The following UAV swarm surveillance scenario is considered in this work: several UAVs equipped with ad hoc communication capabilities take off from different bases to cover a common area (see Figure 2a with blue squares as bases of UAVs). These UAVs evolve as a swarm that needs to cover as much area as possible, as fast as possible, while remaining as connected as possible. This connectivity aspect is crucial in such a distributed system, as it will enhance communication within the swarm. The local information of each UAV will, therefore, spread faster, which in turn will result in improved performance of the global system (i.e., swarm). In this context, the information exchanged by UAVs is the current solution according to their distributed knowledge. A formalisation of a solution is described in Section 3.1.4. Once the UAVs have finished covering the area, they must return to their base (i.e., initial starting point). Such a surveillance scenario can find numerous civil applications that require a fast and efficient coverage of a large area, such as search and rescue missions, forest fire or pollution detection.  
[PERSON] et al. [31; 32] designed the Coverage of a Connected-UAV Swarm (CCUS) optimisation problem for that purpose. It optimises the coverage of an area by a swarm of UAVs with two objectives: coverage speed and swarm connectivity. This work proposes to extend CCUS to a three-objective model, referred to as CCUS3O, which targets coverage time, coverage rate and connectivity. What motivates the introduction of a third objective is that the coverage objective in CCUS includes both the coverage time and the coverage rate in one single value. Such a scalarisation introduces a bias that not only can negatively impact the performance, but also reduces the explainability of the objective value. Using three objectives in CCUS3O, thus, permits us to have more atomic objectives. The remainder of this section first presents a formal definition of instances and solutions (see Section 3.1), followed by the evaluation of solutions according to the three objectives (see Section 3.2).
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 1 Introduction
### Current Gaps  
The sell strategy underscores the concept of customer ownership, typically reserved for specialized missions, while SataaS excels in delivering cost efficiency. Based on the characteristics of different strategies in the space supply chain, the satellite data industry is undergoing a transformative shift with the rise of satellite-as-a-service (SataaS), challenging  
Figure 1: Diagram of satellite-as-a-service mechanism.  
traditional ownership models. Thus, the SO is confronted with a dilemma: how to balance the pros and cons of different distribution channels--selling satellites, SataaS, or a combination of both through dual-channel strategies. This decision is critical for optimizing profitability in an industry marked by launch risks and data transmission challenges.
# XLNet-Based Prediction Model for CVSS Metric Values
## 4 Experiments and Results
### Comparative Study
#### 4.3.3 Comparison with Other Similar Works
\begin{table}
\begin{tabular}{c l l c c c c c c c} \hline \hline
**Metric** & **Model** & **AV** & **AC** & **PR** & **UI** & **S** & **C** & **I** & **A** \\ \hline \multirow{4}{*}{Accuracy} & XLNet & **0.9625** & **0.9562** & **0.9161** & **0.9440** & **0.9642** & **0.9303** & **0.9358** & **0.9423** \\  & Decision Tree & 0.9405 & 0.9404 & 0.8690 & 0.9033 & 0.9510 & 0.8879 & 0.8925 & 0.9049 \\  & K-Nearest Neighbors & 0.9410 & 0.9414 & 0.8529 & 0.8609 & 0.9069 & 0.8403 & 0.8283 & 0.8671 \\  & Multilayer Perceptron & 0.9472 & 0.9485 & 0.8801 & 0.9204 & 0.9571 & 0.9031 & 0.9070 & 0.9181 \\  & Naive Bayes & 0.9188 & 0.9277 & 0.8296 & 0.8742 & 0.8957 & 0.8408 & 0.8260 & 0.8735 \\  & Logistic Regression & 0.9479 & 0.9526 & 0.8907 & 0.9246 & 0.9585 & 0.9083 & 0.9113 & 0.9252 \\ \hline \multirow{4}{*}{Precision} & XLNet & 0.8932 & 0.8614 & 0.8777 & **0.9405** & 0.9486 & **0.9132** & **0.9253** & 0.8927 \\  & Decision Tree & 0.9399 & 0.9387 & 0.8677 & 0.9033 & 0.9506 & 0.8874 & 0.8922 & 0.9045 \\  & K-Nearest Neighbors & 0.9395 & 0.9331 & 0.8489 & 0.8613 & 0.9052 & 0.8364 & 0.8260 & 0.8647 \\  & Multilayer Perceptron & 0.9462 & 0.9453 & 0.8794 & 0.9202 & 0.9563 & 0.9019 & 0.9064 & 0.9174 \\  & Naive Bayes & 0.9167 & 0.9177 & 0.8362 & 0.8785 & 0.9055 & 0.8515 & 0.8369 & 0.8718 \\  & Logistic Regression & **0.9465** & **0.9498** & **0.8875** & 0.9248 & **0.9586** & 0.9081 & 0.9112 & **0.9247** \\ \hline \multirow{4}{*}{Recall} & XLNet & 0.8685 & 0.7791 & 0.8568 & **0.9386** & 0.9236 & 0.9030 & **0.9179** & 0.8336 \\  & Decision Tree & 0.9405 & 0.9404 & 0.8690 & 0.9033 & 0.9510 & 0.8879 & 0.8925 & 0.9049 \\  & K-Nearest Neighbors & 0.9410 & 0.9414 & 0.8529 & 0.8609 & 0.9069 & 0.8403 & 0.8283 & 0.8671 \\  & Multilayer Perceptron & 0.9473 & 0.9485 & 0.8801 & 0.9204 & 0.9571 & 0.9031 & 0.9070 & 0.9181 \\  & Naive Bayes & 0.9188 & 0.9277 & 0.8296 & 0.8742 & 0.8957 & 0.8408 & 0.8260 & 0.8734 \\  & Logistic Regression & **0.9479** & **0.9526** & **0.8907** & 0.9246 & **0.9585** & **0.9083** & 0.9113 & **0.9252** \\ \hline \multirow{4}{*}{F1} & XLNet & 0.8800 & 0.8139 & 0.8662 & **0.9395** & 0.9355 & **0.9078** & **0.9214** & 0.8521 \\  & Decision Tree & 0.9401 & 0.9395 & 0.8683 & 0.9033 & 0.9508 & 0.8877 & 0.8923 & 0.9047 \\  & K-Nearest Neighbors & 0.9400 & 0.9348 & 0.8503 & 0.8611 & 0.9060 & 0.8374 & 0.8265 & 0.8654 \\  & Multilayer Perceptron & 0.9465 & **0.9465** & 0.8797 & 0.9203 & 0.9565 & 0.9021 & 0.9065 & 0.9177 \\  & Naive Bayes & 0.9081 & 0.8935 & 0.7943 & 0.8700 & 0.8756 & 0.8190 & 0.8090 & 0.8676 \\  & Logistic Regression & **0.9454** & 0.9442 & **0.8853** & 0.9239 & **0.9568** & 0.9053 & 0.9100 & **0.9230** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The effect of traditional machine learning models and fine-tuned XLNet on the CVSS v3 dataset.
# Myths and Realities about Genetically Modified Food: A Risk-Benefit Analysis
## 4 The Application of GMOs to Agriculture and Food Production
### Physico-Chemical Crop Resistance  
A number of plants depend on geographical locations and very specific climates to grow, which limits not only the crop viability of some regions but also more output. GE might be a way to help crops adapt to different growing circumstances [15]. For example, maize is grown mostly by applying dry farming practices, even though it does not well manage drought [107]. Recent research has shown that genome editing at a negative regulator of ethylene response, the ARGOS8 locus, generates drought-tolerant crops [108]. After being bruised or sliced, most crops' physical properties are lost. The expression of a particular polyphenol oxidase (PPO) gene causes _Agaricus bisporus_, commonly known as white button mushrooms, to turn brown. Recently, a group from Pennsylvania State University developed a deletion in the family of genes encoding PPO and created non-browning mushrooms [109].
# RT-GAN: GAN Based Architecture for Precise Segmentation of Railway Tracks
## 4 Experiment
### Comparison with the State-of-the-Art Segmentation Networks
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Encoder** & **SIZE (MB)** & **FLOPS (10\({}^{6}\))** & **FPS** & **IoU (Aug.)** & **IoU (No Aug.)** \\ \hline SegNet [7] & Vgg16 & 29.44 & 160.68 & 32.8 & 0.6871 & 0.8098 \\ PSPNet [31] & Cspdarknet53 & 42.85 & 55.56 & 42.99 & 0.5598 & 0.6937 \\ Deeplabv3+ [9] & Xception & 54.7 & 83.01 & 30.08 & 0.6169 & 0.7884 \\ ALSN [14] & Cspdarknet53 & 27.67 & 62.3 & 35.0 & 0.6962 & 0.7974 \\ SETR\_MLA [32] & Vit-large & 308.19 & 315.5 & 11.29 & 0.4301 & 0.5583 \\ SETR\_PUP [32] & Vit-large & 308.78 & 364.98 & 10.73 & 0.5632 & 0.7652 \\ SegFormer [33] & Mit\_b2 & 24.7 & 21.21 & 45.97 & 0.5086 & 0.7017 \\ SegFormer [33] & Mit\_b5 & 81.88 & 64.19 & 15.9 & 0.5079 & 0.7113 \\
**RT-seg (ours)** & & & & **0.7177** & 0.8463 \\
**RT-GAN (ours)** & & & & **0.8134** & **0.8807** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of methods. Figure 7: Image segmentation results for different networks. From left to right, DeeplabV3+, Segformer (mit_b5), ALSN and RT-GAN (ours).