esa-sceva/satcom-chunk-collection huggingface dataset:
# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 3 Proposed Method
### Baseline Model  
The proposed model is demonstrated in Figure 2. The architecture of our auto encoder-decoder is based on the U-Net model [15]. For skin lesion segmentation, we choose DenseNet [29] as the backbone of the U-Net in order to counter the diverse skin appearance and the low contrast between the lesion and the background. The down-sampling and pooling operations in the conventional encoder cause the reduction in spatial information and affect the efficiency of object localization. The loss of detailed features also impedes the ability to discriminate the ambiguity of the obscure target inside the background. In DenseNet, the connectivity among low and high layers reserves more features from the last layers and has richer patterns so that the top layers gain features from all complexity levels. To further improve the convergence rate and the regularization, we employ the deep supervision block at the decoder. The outputs of the decoder's layers are fused with the last prediction. Since our whole network is pretty heavy in terms of the number of parameters, while we only provide the cues at the last output to make the model learn the semantics in skin lesion, the model may miss meaningful information during the gradient propagation from the head layer to all of the lower layer. Deep supervision helps the gradients, not only from the last block but from every block, flow straight back to the low-level layers, avoiding the effect of gradient vanishing. This technique not only cuts down the training time but also increases the accuracy.
# Numerical and Statistical Analyses of Tsunami Heights with the L-Moments Method
## 5 Concluding Remarks  
According to [PERSON] et al. [10], similar tsunami height distributions appear in fixed, small coastal areas, regardless of the undersea earthquake characteristics. However, as the number of earthquakes increases, various tsunami height distributions might occur in a given area because tsunamis are affected by various topographical effects, such as refraction and diffraction during propagation, depending on the location of the earthquake. In general, higher tsunami heights are observed when earthquakes occur in adjacent locations, but there could be other aspects such as terrain effects, as Cases 13 and 14 describe here. Therefore, the assumption that tsunami heights will follow the log-normal distribution in smaller regions is not always correct.  
For disaster prevention, because it is impossible to apply the optimal tsunami height distribution type for each earthquake, it might be efficient to establish a disaster prevention plan by selecting one optimal distribution type by integrating all of the historical earthquake and tsunami events. Through this approach, realistic and economical preparations could be made for creating hazard maps and for designing sea-facing population centers and port facilities.  
Finally, the results of the study shall be used by a civil defense authority to establish a mitigation plan against possible but unexpected tsunami attacks in Korea.  
**Author Contributions:** Introduction was prepared by C.K. and Y.-S.C. Analytic definitions and parameters were prepared by C.K., Y.-S.C. and K.-Y.P. Results were obtained by C.K. and Y.-S.C. Analysis of the results and the Conclusion section were written by C.K., Y.-S.C. and K.-Y.P.  
**Funding:** This research received no external funding.  
**Con  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Sadong**} & \multicolumn{6}{c}{**Imwon**} \\ \hline  & **NLogL** & **BIC** & **AIC** & **NLogL** & **BIC** & **AIC** \\ \hline GEV & \(-\)141,891 & \(-\)283,746 & \(-\)283,776 & 8,417.091 & 16,868.87 & 16,840.18 \\ GP & \(-\)145,255 & \(-\)290,474 & \(-\)290,503 & 9,413.569 & 18,861.82 & 18,833.14 \\ GL & \(-\)74,693.1 & \(-\)149,362 & \(-\)149,382 & 42,424.56 & 84,872.24 & 84,853.12 \\ LN & \(-\)145,031 & \(-\)290,338 & \(-\)290,358 & 6,094.269 & 12,211.66 & 12,192.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Goodness-of-fit measures of tsunami heights at the Sadong and Imwon Ports.
# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 3 Results
### Inversion of Winter Wheat Water Content Using Sentinel-2 Data  
Table 4 shows the gray relational analysis for the five vegetation indices that correlated best with the water content of winter wheat. They are successively from first to last: NDWI, NDVI, SIWSI3, SMI2 and NDVI2. Used gray relational analysis, three of the indices (NDWI, NDVI and SIWSI3) were selected to build the water content inversion model, which is expressed as Formula (14). Figure 3 presents the regression model for winter wheat water content built from the five vegetation indices, which shows that each model produced relatively similar precision.  
Figure 4 shows the inversion results of winter wheat crop water content model which was constructed by multiple linear regression based on Sentinel-2 data. The model randomly selected 38 sample points and produced an accuracy of R = 0.644, RMSE = 0.018, nRMSE = 14.89%. The verification step using 20 verification sample points produced an accuracy of R = 0.632, RMSE = 0.021, nRMSE = 19.65%. The Sentinel-2-based model results were better than the inversion results using a single vegetation index.  
\[\mathrm{w}\mathrm{c}=-0.576\mathrm{NDWI}-0.628\mathrm{NDVI}+0.849\mathrm{ SIWSI3}+0.539 \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Index** & **Correlation** & **Sort** & **Recommended Number of Vegetation Indices** \\ \hline NDWI & 0.834 & 1 & \\ NDVI & 0.811 & 2 & \\ SIWSI3 & 0.759 & 3 & \\ SMI2 & 0.713 & 4 & \\ NDVI2 & 0.709 & 5 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gray relational analysis results.  
Figure 4: Estimation of winter wheat water content using a regression model containing three different vegetation indices and 38 sample points for modeling (**a**) and 20 sample points for verification (**b**).  
Figure 3: Estimation of winter wheat water content using a regression model containing five different vegetation indices. MSI2-based regression model (**a**), NDVI-based regression model (**b**), NDVI2-based regression model (**c**), NDWI-based regression model (**d**) and SIWSI3-based regression model (**e**).
# Improved PBFT Algorithm Based on Comprehensive Evaluation Model
## 3 TB-PBFT Algorithm
### Primary Node Selection  
The PBFT algorithm uses rotation to select the primary node. If the primary node has malicious behavior, it will lead to the failure of consensus and seriously affect the efficiency of the system consensus. Therefore, this study divides the consensus node into many groups, and each group selects the primary node as the group representative to participate in the global consensus. In addition, the multi-primary node approach is adopted to reduce the impact on the consensus algorithm when a single primary node is a malicious node, and the collaborative consensus of multi-primary nodes is more in line with the purpose of decentralization of the blockchain. However, this approach further reduces the probability of malicious nodes becoming primary nodes and improves the efficiency of the consensus algorithm. In this paper, the selection of the primary node in each consensus group is based on the comprehensive evaluation model, and the selected primary node is more consistent with the preferences of all nodes. The flowchart of primary node selection is shown in Figure 4. The specific steps are as follows:Step 1. In each consensus group, each node is used as a decision node to determine the index weight of other nodes by using the entropy weight method.  
Step 2. The preference rankings of decision node \(k\) on other nodes are obtained according to the TOPSIS method.  
Step 3. A Borda count is used to calculate the cumulative score of all decision nodes on each node, and obtain the final ranking result according to the comprehensive score.  
Step 4. The node with the highest comprehensive score is set as the primary node in the group, if there are multiple nodes with the same highest scores, one is randomly selected as the primary node in the group.  
Therefore, combining the comprehensive evaluation model with the consensus algorithm, and the performance and behavior of nodes as the basis for decision nodes to vote, each decision node not only needs to choose the best node, but also needs to sort all the nodes. The comprehensive score of each node is determined by all decision nodes. Therefore, the primary node selected by the comprehensive evaluation model will be safer and more reliable, thus ensuring the stability of the blockchain consensus system.
## 7 Challenges and Limitations
### Regulatory and Standardization Issues  
Regulatory constraints and the lack of standardized frameworks for RFEH further hinder its large-scale deployment. Current wireless communication regulations, such as those implemented by the Federal Communications Commission (FCC) and the International Telecommunication Union (ITU), primarily focus on communication and safety, not on energy-harvesting considerations [114]. As a result, many RF energy-harvesting initiatives must operate within strict power emission limits, limiting the amount of RF energy available for collection. One major issue is the absence of frequency bands specifically allocated for energy harvesting, which forces systems to rely on ambient RF signals not intended for power transfer. This creates unpredictability in energy availability and complicates legal compliance when deploying intentional RF power sources. Furthermore, the use of dedicated RF transmitters for power delivery risks exceeding Specific Absorption Rate (SAR) limits, raising health and safety concerns. To address these chal lenges, researchers and industry bodies have begun proposing new regulatory models that include power transfer allowances within existing spectrum allocations. This includes low-power dedicated RF transmitters governed by duty-cycle constraints or time-division multiplexing to minimize interference with traditional communication services. For example, frequency-sharing protocols have been proposed that allow the coordinated use of a spectrum by communication and energy-harvesting devices, subject to adaptive control based on real-time spectral occupancy [114]. Additionally, the coexistence of dedicated RF power transmitters with conventional communication networks raises concerns about spectrum congestion, fairness, and interference. These coexistence issues have spurred work on dynamic spectrum access (DSA) and cognitive radio-based energy-harvesting systems, which allow harvesters to intelligently detect and utilize idle spectrum without disrupting licenced users. In parallel, policy discussions are emerging around enabling unlicensed low-power RF power transmission zones in dense IoT environments, similar to Wi-Fi and Bluetooth regulations. Recent efforts have begun exploring standardization for wireless power transfer and energy-harvesting systems, particularly in 5G and IoT contexts, but comprehensive regulatory guidelines remain lacking. Standardization bodies such as IEEE P2668 and ETSI are beginning to draft interoperability standards and performance benchmarks for energy-harvesting modules, including guidelines for rectenna efficiency, output power levels, and safe integration with IoT networks. However, these are still in the early stages and require broader consensus among stakeholders [115]. Without proper frameworks, the deployment of dedicated energy-harvesting infrastructures or collaborative energy-harvesting from existing networks faces significant uncertainty and legal barriers.
## 3 Experiments and Results  
### Experiment Setup  
Experimental validation was performed on a simulated environment using Gazebo simulator [44] version 9, and on real hardware using a Universal Robot 3 e-series with a control frequency of up to 500 Hz. The robotic arm had a force/torque sensor mounted at its end effector, and a Robotiq Hand-e parallel gripper. In both environments, training of the RL agent was performed on a computer with an Intel i9-9900k CPU and a Nvidia RTX-2080 SUPER GPU. To control the robot agent, we used the Robot Operating System (ROS) [46] with the Universal Robot ROS Driver (ROS driver for Universal Robot robotic arms developed in collaboration between Universal Robots and the FZI Research Center for Information Technology [[https://github.com/UniversalRobots/Universal_Robots_ROS_Driver](https://github.com/UniversalRobots/Universal_Robots_ROS_Driver)]([https://github.com/UniversalRobots/Universal_Robots_ROS_Driver](https://github.com/UniversalRobots/Universal_Robots_ROS_Driver))). The experiment environment of the real robot is shown in Figure 5.
# Exploring Gait Recognition in Wild Nighttime Scenes
## 4 The GaitDN Dataset
This not only aids in enhancing model performance in complex environments but also provides a crucial data foundation for developing more robust and adaptive gait recognition algorithms. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Dataset** & **Year** & **Subject** & **Seq** & **Cam** & **Data Type** & **Wild** & **Daytime Data** & **Nighttime Data** \\ \hline CASIA-A & 2003 & 20 & 240 & 3 & RCB, Silk. & \(\times\) & \(\times\) & \(\times\) \\ CASIA Infrared & 2006 & 153 & 1530 & 1 & Infrared, Silk. & \(\times\) & \(\times\) & \(\checkmark\) \\ CASIA-B & 2006 & 124 & 13,640 & 11 & RGB, Silk. & \(\times\) & \(\times\) & \(\times\) \\ OU-HSR speed & 2010 & 34 & 306 & 1 & Silk. & \(\times\) & \(\times\) & \(\times\) \\ OU-HSR-LP & 2012 & 4007 & 31,368 & 2 & Silk. & \(\times\) & \(\times\) & \(\times\) \\ OU-LP Bag & 2018 & 62,528 & 187,584 & 1 & Silk. & \(\times\) & \(\times\) & \(\times\) \\ OU-MVLP & 2018 & 10,307 & 288,596 & 14 & Silk. & \(\times\) & \(\checkmark\) & \(\times\) \\ OU-MVLP Pose & 2020 & 10,307 & 288,596 & 14 & 2D Pose & \(\times\) & \(\checkmark\) & \(\times\) \\ GREWV & 2021 & 26,345 & 128,671 & 882 & Silk., 2D/3D Pose, Flow & \(\checkmark\) & \(\checkmark\) & \(\times\) \\ GaitD & 2022 & 4000 & 25,309 & 39 & Silk, 2D/3D Pose, 3D MeshdSMPL & \(\checkmark\) & \(\checkmark\) & \(\times\) \\ GaitDN & - & 1009 & 3300 & 3 & 2D Pose & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of publicly available datasets for gait recognition. Wild, Daytime Data, and Nighttime Data indicate whether the dataset was captured in the wild, has daytime data, and has nighttime data, respectively.
## 3 Shaping in TSN
### Complexity of Shapers Configuration  
Assuming network paths are fixed, the scheduling problem complexity involves the finding of a configuration for the given shapers to provide the desired QoS guarantees required by the traffic mix. This depends on the choice of the shaping mechanism. Configuring SP has the lowest complexity as it only requires assigning a priority to each queue. CBS and ATS, on the other hand, require the tuning of credit/tokens-related parameters which is still simple, but adequate for the audio/video domain as such flows typically do not have very strict latency requirements.  
However, configuration complexity increases when using mechanisms aimed at synchronous traffic. For example, CQF requires the optimization of four main parameters:  
* Cycle duration: A long cycle duration may increase jitter and violate the maximum latency requirement of the streams. On the other hand, a short cycle duration may be insufficient for transmitting the frame during the allotted slot.
* The alignment of the start and end times of the cycle among the involved switches: All switches must agree on the starting time of the cycles, which must be enforced by a centralized or decentralized configuration.
* The cycle boundaries within a switch: As the reception of a frame happens during a time interval, the receiving and the sending switches shall assign this time interval to the same given cycle. Otherwise, the forwarding of the receiving switch would be delayed by an additional cycle.
* The earliest and the latest times that a switch could transmit a frame within a cycle: This is important to minimize the impact of time synchronization errors. If TSN switches are not properly synchronized, a switch may send a frame at cycle \(i\) and the next switch may receive it during cycle \(i-1\) or cycle \(i+1\). This may lead to a situation where the accumulated end-to-end latency could violate the requirements.  
Proper configuration for TAS requires the calculation of the Gate Control List (GCL) for each switch. This involves identifying the GCL cycle, the base time that identifies when to start applying the GCL, the time offsets for triggering the sending of the HTS streams, the state (open or closed) of the gates at every moment, and the states transition timestamps. TAS scheduling is proven to be an NP-hard optimization problem [23]. Therefore, we focus the rest of this survey on analyzing and discussing the different solution approaches from the literature.
## 3 Experiments and Results  
### Experimental Setup  
To test the effectiveness of the improved method based on the traditional optical flow method and the accuracy of the data obtained in practical applications, an experiment is carried out. The experiment involves measuring the simulated vibration of an MTS hydraulic piston and comparing the vibration displacement data calculated by the traditional optical flow method and its improved program with the actual data output from the experiment, aiming to test the optimization effect of the improved method.  
Figure 4: The computational process of obtaining the real displacement from the drone video.  
Figure 3: Selection of a Stationary Reference Point to Mitigate and Eliminate Camera Shake Effects.  
The experimental equipment setup is shown in Figure 5. The camera was placed directly in front of the MTS hydraulic press by using a tripod with the aim of capturing the vibration video at the correct camera angle and position. The MTS hydraulic press was connected to a computer terminal and first, the relevant parameters were set. A simple sinusoidal form of motion is set up by the computer to adjust the different frequencies and amplitudes of vibration required. After start-up, the machine can run stably for a long time. The video recorded by the camera has a resolution of 1920 \(\times\) 1080 and a frame rate of 30 fps. The captured vibration video is then fed into the computer for processing, and the MATLAB (2024) program detects the characteristic points of the vibration video captured by the camera using the Harris angle detection function. Finally, the displacement increments in the \(x\)-axis and \(y\)-axis directions for two adjacent frames are output by a program that calculates the equations using the optical flow method, and the results for all frames are summed.
# Analyzing Student Behavioral Patterns in MOOCs Using Hidden Markov Models in Distance Education
## 2 Materials and Methods
Similar to the Viterbi algorithm, with the Baum-Welch algorithm, we use the \(\alpha\) and \(\beta\) values obtained from the Forward-Backward process to calculate the variable \(\gamma\). The parameter \(\gamma_{t}\) is calculated in both Viterbi and Baum-Welch algorithms in the same manner and with the same data. Focusing on this theory raises several important research issues. To identify a behavior model for an individual student or a group of students given some observed and hidden sequences, we employ the Forward-Backward, Baum-Welch, and Viterbi algorithms. Another significant issue is the ability to predict the most likely sequence of transition among Moodle states for a large number of students. We address this issue by employing the Viterbi algorithm, along with outcomes from the Baum-Welch and Forward-Backward algorithms, for a sample of 4037 students.
# Studies on the Electrochemical Behavior of Sulfite on Incoloy 800 in a Neutral Environment
## 3 Results  
### Cyclic Voltammetry  
To identify the processes occurring at the anode, cyclic voltammograms (CVs) were recorded on Incoloy 800 in 1 mol L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) in the absence and presence of different concentrations of sodium sulfite: 10\({}^{-3}\), 10\({}^{-2}\), 10\({}^{-1}\), 5 \(\times\) 10\({}^{-1}\) and 1 mol L\({}^{-1}\), respectively. In order for the voltametric plots to be as obvious as possible, the experiments were performed under potentiodynamic conditions at an increased scan rate (50 mV s\({}^{-1}\), 3 rd cycle). All recorder CVs were recorded in a series of five cycles. The cyclic polarization plots obtained under these conditions are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Element** & **Fe** & **Ni** & **Cr** & **Cu** & **C** & **Mn** & **S** & **Si** & **Ti** & **Mo** \\ \hline Weight\% & 43.3 & 33.1 & 21.6 & 0.11 & 0.09 & 0.69 & 0.003 & 0.43 & 0.57 & 0.06 \\ \hline \end{tabular}
\end{table}
Table 1: Elemental composition of Incoloy 800.  
Figure 1: Cyclic voltammograms (3 rd cycle) recorded on the Incoloy 800 electrode in 1 mol L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) solution without and with different sulfite concentrations, scan rate 50 mV s\({}^{-1}\), (**a**) all CVs depicted, (**b**) solutions with sulfite ions added.  
In Figure 1b, the limiting current level characteristic of the oxidation process of sulfite ions can be easily observed. The limit current density values are proportional, increasing with the increase in the added sulfite concentration.
## 4 Tracking
### Experiment and Analysis  
In this Section, we use experiments to show these two points: one is that the estimation method relies heavily on the accuracy of the models. If the model is not accurate enough, the estimating results can not get high performance. The other is that for the complex nonlinear system, none of the above mentioned filters, such as EKF, UKF, CKF and even PF, has outstanding advantage, and their performance still needs further improvement.  
Here, the simulated 2D RFID tracking system developed in [43] was used as the experiment platform, which described a typical nonlinear estimation problem. The RFID readers were placed in the specific area and the measurement space of each reader with high detection rate were shown with circles (from inside to outside in contour map) in Figure 7. In the white area of the figure, the distance information of the target can only be obtained with low detection rate. The reference trajectory of the target is given with 'black' as terisk.  
The tracking covariance was defined as \(Cov=\sqrt{X_{\text{cov}}^{2}+Y_{\text{cov}}^{2}}\), where \(X_{\text{cov}}\) and \(Y_{\text{cov}}\) are the estimation covariance of horizontal and longitudinal axes, respectively.