esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The present study demonstrates that LE can effectively prevent H\({}_{2}\)O\({}_{2}\)-induced oxidative stress, DNA damage, apoptosis, and mitochondrial dysfunction via antioxidant action in C2C12 murine myoblasts. These findings suggest that the mechanisms underlying the cytoprotective effects of LE involve the AMPK and Nrf2/HO-1 signaling pathways and that the inhibitory effect of LE on H\({}_{2}\)O\({}_{2}\)-mediated ROS induction, DNA damage, apoptosis, and mitochondrial dysfunction involve the AMPK signaling pathway. In addition, the findings of the present study also confirm that LE promotes the differentiation of C2C12 myoblasts into myotubes (Figure 11). These results implicate LE as an effective agent for the prevention and treatment of diseases caused by oxidative stress.  
Figure 11: Schematic pathway of the effects of LE on oxidative stress-mediated DNA damage, apoptosis, and cell differentiation. AMPK, 5\({}^{\prime}\) adenosine monophosphate-activated protein kinase; ARE, antioxidant response elements; Bax, Bcl-2 associated X protein; Bcl-2, B-cell lymphoma-2; Compound C, AMPK inhibitor; HO-1, heme oxygenase-1; HAZX, H2A histone family member X; Keep1, Kelch-like ECH-associated protein 1; MyoD, myogenic differentiation 1; Nrf2, nuclear factor erythroid 2-related factor 2; PARP, poly(ADP-ribose) polymerase; sMaf, small Maf.  
**Author Contributions:** Writing--original draft preparation and formal analysis, Y.H.K. and J.Y.J.; data curation, J.H.L.; supervision, Y.W.C.; project administration, Y.H.C. and N.D.K.; funding acquisition, N.D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by BK21 FOUR Program by Pusan National University Research Grant, 2021 (Y.H.K.). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2022R1A6A3A01085858) (J.Y.J.). This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1F1A1051265) and the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07044648) (N.D.K.).  
**Data Availability Statement:** All data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# RT-GAN: GAN Based Architecture for Precise Segmentation of Railway Tracks
## 1 Introduction
This is an automatic generation capability for unknown regions, and based on this consideration, a GAN-based accurate segmentation framework for railway tracks, RT-GAN, is proposed, which improves the segmentation accuracy for slender linear targets, such as rail tracks, by introducing gradient information and adversarial training. RT-GAN consisted of two parts: a track segmentation network, termed RT-seg, which generates the track segmentation image, and a track discriminator, which determines whether the segmentation image is true or not. Compared with using the track segmentation network alone, the track discriminator and the track segmentation network supervise each other by adversarial training, which can obtain higher accuracy. In practice, only the track segmentation network is being used, which does not affect the efficiency of the segmentation method. The main contributions of this study include the following:  
1. A framework for accurate segmentation of railroad tracks, RT-GAN, is proposed. The framework treats the segmentation of track targets as an image-to-image generation task and enables the generator RT-seg of RT-GAN to more accurately exclude misidentified similar targets and reason about the occluded track regions through adversarial training. 2. A training process optimized for the continuous gradient features of track images, a gradient loss function and a discriminator input containing gradient images are designed to guide the generator RT-seg to focus on linear features of railroad tracks faster and more accurately. 3. A UAV-based railway image dataset named \"iRailway\", including illumination changes, occlusions and various complex backgrounds, which can reflect the challenges related to real-world railway images is included. The rest of this article is organizised as follows. In Section 2, the work related to the semantic segmentation of railway targets and GAN-based image segmentation methods is outlined. In Section 3, the proposed RT-GAN framework for track segmentation in UAV images is presented, including the track segmentation network, track discriminator and training. In Section 4, the experiment and analysis are discussed. In Section 5, the conclusion is presented. The dataset and code for DFR are available at github.com/ksws0 499733/RTGAN.
## 3 Case Study Application
### WWTP Application and Modelling  
There are many different possible settings within the above-mentioned stages. Hence, for the purpose of analysis, this article will consider the following settings for these stages:  
1. Primary stage: It starts with collecting wastewater from the city through the sewage system, which flows into the facility after being screened by two self-cleaning screens in load-sharing configuration with a 65% overcapacity each (meaning that each screen can withstand up to 65% of the workload), then grit removal is performed by a single cyclone separator to dispose of medium-size elements to finally arrive at the primary clarification process, which is performed by four primary clarifiers in a load-sharing configuration with 30% overcapacity where suspended solids are collected through settling, this last collected material is known as \"primary sludge\" or waste activated sludge (WAS). 2. Secondary stage: After primary clarification, the process is divided into two: on the one hand, wastewater collected from the primary clarifier flows into a secondary biological treatment, which takes place in a set of two anaerobic and four aerated basins where the anaerobic basins are configured with baffles in an \"N\" pattern for phosphorus removal and set in a load-sharing configuration with 60% overcapacity, the aerated basins are also set in a load-sharing configuration, but with a 35% overcapacity each. Effluent from the basins goes into a secondary clarification process performed by four clarifiers in a load-sharing configuration with 30% overcapacity each. From here, most of the return activated sludge (RAS) is sent back into the anaerobic zone, and a portion is sent back to the primary clarifier. Effluent from the secondary clarification process is then disinfected by a single disinfection unit with chlorine (and then dechlorinated in the same unit) before being discharged into the environment. On the other hand, waste activated sludge from the primary clarifier combined with the RAS sent back from the secondary clarifier flows to three primary anaerobic digesters in a 3/2 partial redundancy (meaning that only two clarifiers are needed to withstand the complete workload) before going into three secondary anaerobic digesters also in a 3/2 partial redundancy to finally arrive at two belt thickeners (one at 2 m and the other at 3 m) in a series configuration to reduce the water content, which is sent to the primary clarifier. Thickened and digested sludge is stored in one storage tank before going into landfills. The graphical layout of the above description is provided in Figure 1, and details of the equipment functions can be found in Table 1.  
Following the proposed methodology and the exposed logical configuration of the system, an availability analysis is performed for each level using the MTBF and MITTR individual data for each level and element of the system. Level disaggregation of the plant identifies four levels, from the singular elements in the lower levels to the level of the system as follows:
## 4 Conclusions  
In this study, we proposed the procedure to determine the allowable pitch angle (APA) region that the relative pitch angle deviations should adhere to by performing ultimate load analysis under aerodynamic imbalance for a NREL-5 MW offshore wind turbine. We also performed pattern analysis using the artificial neural network (ANN) to obtain meta models for the APA region for each wind speed.  
1. The results of the ultimate load analysis under aerodynamic imbalance indicate that the difference in the resultant moment tends to increase largely as the relative difference of pitch angle between the blades increases. On the other hand, the resultant force is insignificant compared to the resultant moment.
2. We proposed a procedure to determine the APA region for each wind speed, and also determined that the proposed APA region expands like a cone as the pitch angle increases. This seems to occur when reducing the load of the wind turbine due to the aerodynamic stall with the increase in the APA. In addition, we identified that the relative difference in the pitch angle between the blades was only possible in a small area. This approach may be used to reduce the blade loads due to the relative pitch angle deviation under aerodynamic imbalance.
3. We proposed the pattern analysis procedure to implement the meta models for APA region at each wind speed using the artificial neural network (ANN), which is one of the machine learning techniques. The prediction results for the APA region using the ANN very well matched the APA analysis results by ultimate loads analysis. This approach may be used to reduce blade loads due to the relative pitch angle deviation under aerodynamic imbalance.
4. This decision can only be generalized to this wind turbine. Applicability to other wind turbines will require further testing and studies. In addition, further studies considering comparative evaluations with other researchers' IPCs are recommended.  
Writing-original draft preparation, B.-S.K.; investigation, D.-Y.J.; resources, Y.-J.J., writing-review and editing, K.-W.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea Government (MOTIE) (20213030020120, 20213030020380).  
The authors declare no conflict of interest.
## 4 Experiments, Results, and Discussion  
### Dataset and Environment  
We demonstrate the performance of the improved algorithm through comparative experiments on the publicly available datasets DOTA [41] and Hrsc2016 [42]. The DOTA dataset contains 2806 images of different scales, with resolutions as small as 800 \(\times\) 800 and as large as 4000 \(\times\) 4000. It contains 15 categories of data, with a total of 188,282 instances.  
The Hrsc2016 dataset is a remote sensing image dataset for ships. It mainly includes various ships on the sea or on the shore. It was released by Northwest Polytechnic University in 2016. There are 1061 remote sensing images with different resolutions from  
Figure 5: Circular smooth label.  
300 \(\times\) 300 to 1500 \(\times\) 900, including a training set with 436 instances and a test set with 181 instances, and 444 test sets with 2976 instances.  
We resized the image according to the image pyramid and cropped the DOTA dataset into 21046 pcs sub-images of 1024 \(\times\) 1024 size. Similarly, we trimmed the Hrsc2016 dataset to 768 \(\times\) 768 size sub images. During the experiment, we used the DOTA dataset for horizontal object detection and the DOTA dataset and Hrsc2016 dataset for rotationally invariant object detection. Table 1 shows the relevant tools and the configurations used during the experiments.
## 3 System Model  
The ECG signal is a multi-segment sequential, periodic signal. The value of the signal generally appears as a signal close to zero, and its waveform is composed of abrupt peaks returning to the baseline. The ECG signal appears as a form of sparse waveform distribution, and the signal has a certain periodicity. When the ECG signal is calculated by the third-order difference, it can be obtained that most of the values in the difference signal have tended to or approximated to zero. Therefore, the ECG signal can be regarded as a sparse signal.  
An ECG recording signal affected by noise and baseline wander after collection can be viewed as the following three parts: the original clean ECG signal \(x\); the baseline wander \(f\), and the Gaussian white noise \(n\). Therefore, a mixed signal of length \(N\) can be modeled as follows:  
\[y=x+f+n \tag{7}\]  
When the signal itself is sparse or close to sparse representation, signal processing can be performed by the sparse optimization correlation method. It is well known that the L norm [38] is a representative of convex sparsity. The problem of sparse signal recovery can be transformed into an optimization problem for the following objective function. Therefore, the problem of sparse noise reduction is attributed to the optimization of the norm of the problem of minimization under the condition of data fidelity.  
\[\text{min}F(x)=\frac{1}{2}\|y-Mx\|_{\text{p}}^{2}+\alpha\|x\|_{1} \tag{8}\]  
where \(\|\bullet\|_{p}\) denotes \(p\) norm, \(\alpha\) is the regularization factor, \(M\) is the measurement matrix, and the row vectors are orthogonal to each other. According to the signal model, the non-convex optimization problem of sparse signal estimation is expressed as a basis tracking denoising problem model, in which regularization parameters and signal norm control errors and sparsity are introduced.
## 4 Case Study
### Optimizing Design Schemes
#### 4.2.4 Optimizing the Design Scheme  
On the basis of Equations (8) and (9), we calculated the \(S_{i}\), \(R_{i}\) and \(Q_{i}\) of the three schemes respectively and arranged them in ascending order. The calculated values of the scheme evaluation are shown in Table 4.  
According to the evaluation VIKOR method best scheme, the schemes are arranged in ascending order of their compromise value \(Q_{i}\) and are sorted into scheme 1, scheme 2 and scheme 3. Condition 1 is satisfied due to inequality \(Q(a_{2})-Q(a_{1})\geq 1/(m-1)\); a1 is the best ranking among Si and Ri values and the order of the group utility value and the individual regret value is the same as the compromise value, so condition 2 is satisfied. As a consequence, scheme 1 is the optimized scheme.
## 3 DprE1: The Hot TB Target of the Moment
### Why DprE1 Is a Promiscuous Target
This binding pocket, which conceivably constitutes the binding site of the physiological electron acceptor of DprE1, shows a very broad specificity, thus explaining the promiscuity of DprE1 for inhibitors. A broad specificity for inhibitors is a necessary but not sufficient condition for a target, to be considered promiscuous. Indeed, as promiscuous targets usually emerge during phenotypic screening, this implies that the inhibitors identified have antitubercular activity, so they can reach the cellular compartment in which the target resides. It is noteworthy that among the obstacles for the antitubercular drug discovery process, there is the difficulty of the compounds to cross the cell wall and the cellular membranes. Moreover, the presence of efflux pumps that can extrude the compounds outside the cells, and potential cytosolic inactivation processes of drugs lead to a lack of activity. In this context, it is noteworthy that one of the most successful pathways for antitubercular discovery is the cell wall compartment [59]. Recently, it was demonstrated that DPA biosynthesis partly occurs also outside the cytosolic membrane, and in particular, DprE1 was localized into the periplasm [60]. How DprE1 can reach the periplasmic compartment is still not clear, despite this protein, as well as its partner DprE2, do not possess any export signal for the translocation across the cytosolic membrane [60]. Anyhow, this localization allows drugs against DprE1 not only to easily reach the target, but also to escape several resistance mechanisms such as efflux pump or potential cytoplasmic inactivating enzymes. Figure 2: Crystal structure of _Mycobacterium tuberculosis_ (Mtb) DprE1 and detail of the inhibitor binding sites. The superimposition of the structures of DprE1, recrystallized with both covalent and noncovalent inhibitors (PBTZ-169, PDB: 4 NCR [49] with carbon atoms in grey; Ty38c, PDB: 4P8K [58] with carbon atoms in cyan; TCA1, PDB: 4 KWS [55] with carbon atoms in magenta; CT325, PDB: 4 FF6 [43] with carbon atoms in violet), shows how the compounds have significantly overlapping binding sites, located in front of the isoalloxazine ring of the FAD cofactor (with carbon atoms in green). The black arrow points to the Cys387 side chain.
## 5 Numerical Examples
### Example 1: Thin Plate
\begin{table}
\begin{tabular}{l l l l} \hline
**Parameter** & & **Initial Design** & **Optimized Design** \\ \hline  & \(H\)/m & 0.01 & 0.0436 \\ Design variables & W/m & 0.01 & 0.0131 \\  & \(t_{1}\)/m & 0.002 & 0.001164 \\  & \(t_{2}\)/m & 0.002 & 0.0010 \\ Maximum displacement/m & 4.12 & 0.359 \\ Local frequency/Hz & 22.504 & 60.771 \\ Order of the local frequency & 3 & 3 \\ Total mass/kg & 125.22 & 125.31 \\ \To verify the accuracy of the approximation method, we conducted a modal analysis of the plate with the present parameters, which resulted in the local modal shape shown in Figure 8, and the result of static analysis is shown in Figure 9. Approximate similarities of the initial and the present local modal shapes were found from Figure 8, which corresponded to the above Assumption 2. Upon further observation, the local frequency also met the design requirement and there was only a relative error of 1.29%. In Reference [18], there was the same plate example of local frequency constraint, where the local mode identification technique was applied. The optimal weight was 125.29 kg while the local frequency was 60.003 Hz after five iterations. Compared to the previous result, with the transformation method, the optimal weight was 0.02 kg heavier and the local frequency was 0.7 Hz higher, which were close, but with fewer optimization iterations. That indicated the relative validity of this method. Figure 8: Local mode of the optimized model (local frequency = 60.77). Figure 7: Objective iteration curve.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell Edge
#### 3.4.4 Spectral Efficiency  
To see the impact of the variation in the \(P_{\text{max}}\) limit on the system performance, the spectral efficiency for the cell average and at the specific position of the UE in the cell was computed from Equations (12) and (13) using the received SIR given in Equation (26).
## 2 Study Area and Data
### Satellite Data  
In 2013-2014, 84 RADARSAT-2 (RS-2), 10 TerraSAR-X (TSX) and 8 COSMO-SkyMed (CSK) images were acquired. One GeoEye-1 image (Copyright of DigitalGlobe, presently it is MAXAR Technologies) was acquired over Meighen Island in September 2013, one SPOT-6 (Copyright of Airbus Defence an dSpace) mono image over Prince Patrick Island and three SPOT-6 stereo pairs over Borden Island in April of 2014. During the analysis, 40+ Landsat 8 and MODIS images were also analyzed. In addition, 32 TDM single-pass InSAR pairs and one Pleiades stereo pair were acquired over Baffin Bay in 2016. One stereopair of WorldView-2 (Copyright of DigitalGlobe, presently it is MAXAR Technologies) and 17 TDM InSAR pairs (Copyright of DLR) over sea ice near the Axel Heiberg Island in 2014. For the images used in the following figures, the specific image acquisition dates are provided.
# Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations
## 6 Conclusions  
This paper presents a fault-free protection level equation for the CLAS PPP-RTK service that broadcasted correction quality messages. The performance of the proposed protection level equations was tested using 7 days of the GNSS observation data and the CLAS L6 messages obtained at a base station in Tokyo, Japan, which was located within the CLAS service region. The test results with 7 days of GNSS data showed that the HPL and VPL were always larger than the HPE and VPE of the zero-baseline VRS-RTK solution. Furthermore, fault-free protection-level violations were not observed. It should be noted that most HPL and VPL values were less than 1 and 3 m, respectively. Since a rail automation, which is a very demanding problem from an integrity point of view, requires Horizontal Alert Limit of 1m [29], the proposed protection level would be able to fulfill stringent integrity and availability requirements for many applications using PPP-RTK services.  
Protection-level equation development, E.K.; GNSS and CLAS data processing, J.S., Y.S. and S.K.; validation, P.-W.S., S.P. (Sulgee Park) and S.P. (Sanghyun Park). All authors have read and agreed to the published version of the manuscript.  
This research was part of the project titled \"Development of Ground-based Centimeter-level Maritime Precise PNT Technologies\", funded by the Ministry of Oceans and Fisheries, Korea (No. PMS5110). This work was also supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1A2C2006028).  
The authors thank [PERSON] for his help with the preparation of this paper.  
The authors declare no conflict of interest.
## 4 Problem Definition and Optimization Approaches
### Greedy Approach
```
1:repeat
2: wait until a task arrives
3:\(t\leftarrow\) newly arrived task
4:if task arrived from an edge device then
5: insert task \(t\) into local execution queue
6:else
7:\(w_{t}\leftarrow\) the average task workload obtained from the workload approximation algorithm
8:\(l_{s}\leftarrow\) retrieve local execution queue task count
9:\(s_{s}\leftarrow\) retrieve transmission queue total task size
10:\(s_{c}\leftarrow\) retrieve transmission queue task count
11:\(E_{t}\leftarrow\) local power consumption per floating point operation
12:\(E_{t}\leftarrow\) local power consumption per second of data transmission
13:\(f_{t},f_{r},r\leftarrow\) local flops, remote flops, connection data rate
14:\(p_{t}\gets E_{t}.w_{t}\)\(\triangleright\) approximating local execution power consumption
15:\(p_{t}\gets E_{t}.l(t)/r\)\(\triangleright\) approximating transmission power consumption
16:\(d_{l}=(l_{s}+1).w_{t}/f_{l}\)
17:\(d_{r}=(l(t)+s_{s})/r+(r_{c}+s_{c}+1).w_{t}/f_{r}\)
18:if\(\alpha.d_{l}+\beta.p_{l}\geq\alpha.d_{r}+\beta.p_{t}\)then
19: insert task \(t\) into local execution queue
20:else
21: insert task \(t\) into transmission queue
22:endif
23:endif
24:until all tasks have arrived
```  
**Algorithm 2** Greedy algorithm  
```
1:while true do
2:\(q\leftarrow\) number of tasks in local queue
3:\(e\leftarrow\) the unique identifier of this edge
4:for all node in connected nodes do
5: transmit \((e,q)\) to node
6:endfor
7: wait for \(c\) seconds
8:endwhile
```  
**Algorithm 3** Edge state update algorithm