esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results Description
### Experiment and Result Analysis
#### 5.4.3 Postprocessing Experiment  
The accurate segmentation of the cardiac region is important for diagnoses and subsequent image registration. Therefore, it is necessary to complete the segmentation task in images with a higher pixel resolution. If the network is trained on the original 2048 \(\times\) 2048 X-ray images, the hardware requirements are too high. Even the labeled ground truth image is 1024 \(\times\) 1024. All of the networks in this paper were trained and tested by scaling down the images to 128 \(\times\) 128 pixels. The contours of the extracted region predicted by the network segmentation are labeled as the heart in the higher pixel image. Therefore, it is necessary to postprocess the images segmented by PDNet. If the binary region is directly enlarged, the predicted segmentation edge is distorted and difficult to fit to the real edge, yielding an insufficient segmentation result. The segmented images are postprocessed using the ASM algorithm as follows, taking the last network as an example.  
As shown in Figure 12, since the image is large, only the key areas of the image are extracted to improve the visualization. The green line in Figure 12 shows the directly enlarged outline of the cardiac region segmented by the PDNet network. The blue line in Figure 12 shows the postprocessed prediction result of the ASM algorithm. The red line in Figure 12 shows the ground truth cardiac region. A comparison indicates that although all of the image areas are similar, the postprocessed curve is closer to the ground truth than the enlarged outline; there is no jagged shape, and the contour fits the heart well. Therefore, the use of the ASM algorithm for postprocessing can achieve accurate segmentation of cardiac regions in large images.
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 5 A Selective MEC Service Model Based on a Multitier Architecture  
In order to deploy on-demand MEC services in the KREONET-S environment, MEC resource selection and allocation mechanisms play a significant role in the performance of the MEC service. In this section, we present a multitier MEC selection model considering end-to-end service latency between users and MEC nodes, and efficient service resource utilization using the hierarchical MEC service architecture mentioned in Section 2.  
We assume that we know the locations and resource capabilities of multitier MEC service entities. In addition, the wide-area SDN infrastructure (KREONET-S) with wireless access networking and MEC capability is set up in advance. For the flexible availability of MEC resources, in the proposed multitier MEC selection model, dynamic MEC resource allocation and management are supported in the MEC application environment where virtual network functions on VMs and/or containers within the selected MEC nodes are independently instantiated and terminated on demand. The notations associated with the multitier MEC selection model are listed in Table 2.
## 2 Methods
The thermodynamic quantities determined for three sub-binary systems must be extrapolated to obtain the values for each ternary system. A geometric model was chosen for this purpose, as discussed in the introduction section. Apart from enthalpy of formation, the effect of atomic radius differences was determined by mismatch entropy (\(S_{\sigma}\)/k\({}_{B}\)) calculations, on the basis of the relation given by [PERSON] et al. [36], while the glass forming ability was additionally estimated using the glass forming ability parameter \(\Delta P_{HS}\)[22], defined as the product of chemical enthalpy and normalized mismatch entropy (\(\Delta H^{chem}\Delta\frac{S_{\sigma}}{k_{B}}\)). A more detailed description of the utilized method can be found in [25]. Basic parameters used for the calculations are listed in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Chemical** & \(\varphi\) (V) & \(n_{us}\) (Arb. Units) & **V (10\({}^{-6}\) m\({}^{3}\)/mol)** & **(10\({}^{10}\) Pa)** & **(10\({}^{10}\) Pa)** & **(10\({}^{10}\) Pa)** \\ \hline Ce & 3.18 & 1.69 & 21.62 & 2.395 & 1.197 & 3.64 \\ \hline Fe & 4.93 & 5.55 & 7.09 & 16.83 & 8.152 & 2.56 \\ \hline Nd & 3.19 & 1.73 & 20.58 & 3.268 & 1.452 & 3.64 \\ \hline Si & 4.70 & 3.38 & 8.60 & 9.888 & 3.973 & 2.34 \\ \hline Zr & 3.45 & 2.80 & 14.00 & 8.335 & 3.414 & 3.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic parameters used in the calculations of enthalpies of formation [24; 37].
# A Review of Deep Learning Applications for Railway Safety
## 8 Discussion and Conclusions
### Performance Optimization
#### 8.1.3 New Data Source  
Most prior studies were conducted with a limited data source. For example, [PERSON] et al. [40] emphasized the necessity of improvement of data quality. Their data were collected in limited circumstances, such as fixing the camera angle when taking the image data. In addition, [PERSON] et al. [8] argued that experiments in various railway conditions, such as the angle or length of the rail, should be conducted to develop a fault detection system. Furthermore, [PERSON] et al. [65], who developed a method for rail defect inspection based on vibration signals, commented that considering various train types and driving speeds can help improve performance.  
While most studies about artificial intelligence for railway safety have utilized image or vibration data, some studies have explored other data sources to improve model performance. For example, [PERSON] et al. [94] proposed using LiDAR sensor data to understand situations and components in 3D railway images. [PERSON] and [PERSON] [9] utilized acoustic emission signals from rails for rolling contact fatigue. Furthermore, artificial intelligence could be improved and optimized by harnessing various situational features in the railway domain. [PERSON] et al. [68] developed an efficient model for detecting machine-learning-based train wheel defects by additionally considering the exterior characteristics of the train.
## 2 Materials and Methods
### Data Preprocessing  
#### 2.4.1 Feature Classification  
During the UAV flight shooting, the hyperspectral images were distorted owing to the influence of external environmental factors. Therefore, a manual visual inspection method was used to remove such poorly imaged images. The remaining images were imported into the Spec-VIEW software for radiation correction so that the brightness values of the remote sensing image elements were converted into spectral reflectance. The corrected images were further screened for usable data using the ENVI 5.3 software. In the screened images, 100 pure image elements were extracted, including Artemisia frigida, Bare soil, Stipa breviflora, and others (mats and small flags). The spectral reflectance curves were plotted, as shown in Figure 3.  
As shown in Figure 3, in the full wavelength range, the spectral reflectance curve of Feature 4 exhibited the strongest fluctuation and largest curve difference compared to the other features. One and three had prominent \"peaks\" and \"troughs\" in the spectral reflectance curve from 550 nm to 690 nm, but the difference in the reflectance fluctuation between them was more pronounced. The spectral reflectance curve of Feature 2 had a high growth rate and was similar to that of linear growth. These spectral reflectance fluctuation differences provide the potential for the fine classification of grassland features. The hyperspectral image data were cropped to 550 lines \(\times\) 550 samples \(\times\) 256 bands to facilitate data processing.  
In Figure 3, 1 represents _Artemisia frigida_, 2 represents _Bare soil_, 3 represents _Stipa breviflora_, and 4 represents the _others_.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 3 System Model  
### Problem Statement  
EHRs are important, sensitive, and private data that must always be kept secure and available. Intentional or unintended security risks may compromise healthcare systems. EHRs contain confidential data for medical diagnosis and care, so it must be exchanged regularly by various parties. If unauthorized people can access patient records then the data's confidentiality and availability are compromised. The security goal of the healthcare industry is to ensure the availability, confidentiality, and integrity of their services. Besides that, access to EHRs should be restricted to protect the data's security and privacy by preventing unauthorized entities from changing the meaning of the EHR. However, all systems that interact with the patients must respect their privacy and the data owners (patients) must have full control of their EHRs. Recently, several studies have proposed blockchain technology as a practical solution that can protect data integrity and availability but it does not protect data sharing confidentiality since any transaction in the blockchain is visible to the public [2; 47]. Besides, access authorization is needed to protect the privacy of EHRs and the interoperability can assist patients in managing their EHR access rights [46; 48].
## 3 Numerical Simulations
### Verification of Numerical Results
#### 3.1.1 Heave RAO of a Spherical Float  
Figure 3 depicts a spherical float with a diameter of 10 m and a draft of 5 m to verify the RAO calculation. The mass of the float was 261,800 kg. The water depth was infinite, and the density of the water was 1000 kg/m\({}^{3}\). The float was modeled with 7200 quadrilateral elements. The float has one degree of freedom, with the motion constrained except for vertical (heave) motion. Frequency-domain hydrodynamic parameters (added mass, radiation-damping coefficient, and diffraction force) were obtained using  
Figure 2: White noise spectrum to calculate the RAO of a floating body.  
WAMIT. Among the external forces acting on the float, the diffraction and radiation forces were applied as linear forces.  
A time history of wave elevation was generated using the white noise spectrum shown in Figure 2. The frequency ranged from 0.1 rad/s to 4.0 rad/s, and irregular waves were generated with 196 regular component waves. A simulation result of 3600 s after a 100 s ramping period was used to remove the initial transient motions.  
Figure 4 compares the heave RAOs of the half-submerged sphere with the results of a previous study [41]. While [41] obtained RAOs in regular waves of respective frequencies, the present method calculated the RAOs at all frequencies from one time-domain simulation using cross- and auto-spectral densities. The two independent results agreed well across all frequency ranges. In general, the results of the body-nonlinear analysis were similar to those of linear analysis over the entire frequency range for the given example.
## 4 Thermal Network Correction  
### Correction Procedure  
Based on the characteristics of the aerial camera thermal network model, its correction procedure has been designed and is depicted in Figure 4, providing a clear overview of the entire correction process for the thermal network model of the aerial camera.  
The correction process reveals a complementary relationship between the thermal network model and the thermal experiments. Firstly, the efficacy of the thermal network model in accurately representing the characteristics of aerial cameras must be validated through a comparison of its temperature calculations with those obtained from thermal experiments. Secondly, the structure and parameters of the thermal network model must be refined using the data gained from thermal experiments. Thirdly, the reliability of the experimental data must be assessed through an error analysis of the thermal network model. It can thus be seen that both the thermal network models and the thermal experiments are indispensable, providing a solid foundation for the improvement of the thermal control design of aerial cameras and the enhancement of the reliability of thermal control systems.  
Figure 4: Procedure diagram for correcting the thermal network model of aerial cameras.  
In our preceding study [28], transient thermal tests of the aerial camera were conducted under \(11\) distinct heating conditions to obtain a more comprehensive set of experimental data, which is essential for correcting the thermal network model comprehensively. As illustrated in Figure 5, the heating films are enveloped around the lens barrel, thereby facilitating the implementation of diverse heating conditions. Temperature sensors are affixed at designated locations corresponding to each node of the thermal network model, thus enabling real-time monitoring of temperature fluctuations. Additionally, to assess the reliability of the experimental data, temperature errors attributed to the inaccuracies in the thermal parameters were studied. The results demonstrated that the measured temperature errors were within the calculated error ranges, indicating that the transient thermal test data were highly reliable and suitable for application in correcting the thermal network model.
# Versatile Video Coding-Post Processing Feature Fusion:
## 5 Conclusions  
This paper introduces VVC-PPFF, a novel CNN-based post-processing filter approach that leverages the QP map as prior knowledge, combining it with input reconstructed images and feature fusion from earlier and deeper layers. The network architecture is designed to accommodate variability in QP ranges and image sequence sizes, enabling improved image quality. Additionally, the method employs skip connections between the input and residual to achieve reconstruction quality closely matching the original video. The proposed approach demonstrates substantial coding gains for both RA and LD scenarios, across a range of QP values from low to high.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14188276/s1](https://www.mdpi.com/article/10.3390/app14188276/s1)]([https://www.mdpi.com/article/10.3390/app14188276/s1](https://www.mdpi.com/article/10.3390/app14188276/s1)), Table S1: The compression performance with and without proposed method for RA configuration; Table S2: The compression performance with and without proposed method for LD configuration; Table S3: PSNR comparison of proposed method vs. without proposed method for 8 and 16 feature extraction blocks in RA configuration; Table S4: PSNR comparison of proposed method with 8 and 16 feature extraction blocks in LD configuration.  
Investigation, validation, methodology, writing--original draft, T.D.; conceptualization, investigation, X.L.; supervision, project administration, funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from Kyung Hee University in 2023 (KHU-20230874); the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2023-00220204, Development of Standard Technologies on next-generation media compression for transmission and storage of Metaverse contents).  
Not applicable.  
The data presented in this study are openly available in [40, 41].  
The authors declare no conflicts of interest.
# Design of a Human Muscle-Powered Flying Machine
## 6 Structural Development of the Human-Powered Aircraft
### Preliminary Stability Analysis  
For the stability analysis, the XFLR5 program was utilized. Introducing the values of total mass and center of mass using the Plane Inertia option enabled the determination of the pitching moment acting on the human-powered aircraft structure. In the preliminary stability assessment, reliance was placed on the plot depicting the relationship between the pitching moment coefficient \(C_{m}\) and angle of attack \(\alpha\) (Figure 15a). This plot must exhibit a negative slope, as it corresponds to the behavior of the aircraft during flight. When the aircraft descends, its angle of attack is reduced and often negative. In such cases, a positive pitching moment causes the aircraft's nose to rotate upwards, helping to counteract the descent. Conversely, when the aircraft ascends and its angle of attack is positive, the pitching moment is negative and aims to direct it downwards.  
On the graph (Figure 15a), the pitching moment is neutral at an angle of attack of \(2^{\circ}\), indicating a zero pitching moment condition. To achieve a neutral pitching moment at an angle of attack of \(0^{\circ}\), the horizontal stabilizer was set at an angle of attack of \(2.2^{\circ}\). In this configuration, it was checked on the plot (Figure 15b) whether the moment coefficient was neutral, and the lift coefficient was positive. Additionally, based on the plot (Figure 15c), it was determined that the human-powered aircraft exhibits the highest value of \(C_{l}/C_{d}\) in this configuration during horizontal flight.  
For clearer explanations and to aid understanding of the aerodynamic analyses performed in recent sections, we would like to supplement this paper with descriptive flow chart visuals of the complete procedure (Figure 16).  
Figure 15: Graphs of the: (**a**) pitching moment coefficient \(C_{m}\) against the angle of attack \(\alpha\), (**b**) pitching moment coefficient \(C_{m}\) against the lift coefficient \(C_{l}\), (**c**) lift coefficient versus the drag coefficient \(C_{l}/C_{d}\) depending on the angle of attack \(\alpha\).
## 3 Decomposition and Assignment of Observation Missions  
Observation and sampling density depends on the rate of change of ocean phenomena. These variations impose various requirements for marine vehicles such as motion mode, communication mode, and endurance. In this study, observations of meso-scale and micro-scales dynamic processes with scale less than 100 \(\times\) 100 km\({}^{2}\) is discussed. Typical objectives of an ocean observation mission may include physical oceanography, biological, and ecology. Observation missions can be decomposed into coverage observation in a regional area, ocean feature tracking, emergency response observations, vertical profile observations, and hierarchical observations. Suitable vehicles should be chosen for a particular observation mission. As a result, the basic issues are mission decomposition and marine vehicle selection.
# Dynamic Modeling and Validation of Peak Ability of Biomass Units
## 3 Dynamic Modeling of Biomass Unit Peak Ability
### Pressure-Power Model  
It is assumed that the regulating stage pressure of steam turbines is directly proportional to the product of the main steam pressure and the steam turbine valve opening. Under stable conditions, the regulating stage pressure of steam turbines can be described by the following formula,  
\[P_{1}=K_{8}\mu_{T}P_{T}, \tag{20}\]  
where \(P_{1}\) (Mpa) is the pressure of the regulating stage. \(\mu_{T}\) (%) is the opening of the steam turbine valve. \(K_{8}\) is the static parameter to be identified. The dynamic expression of Equation (20) is  
\[c_{7}\frac{\mathrm{d}P_{1}(t)}{\mathrm{d}t}=K_{8}\mu_{T}(t)P_{T}(t)-P_{1}(t), \tag{21}\]  
where \(c_{7}\) is the dynamic parameter to be identified.  
The steam flow of the steam turbine and the pressure of the governing stage can be approximately linear [28], and its dynamic expression is  
\[c_{8}\frac{\mathrm{d}D_{T}(t)}{\mathrm{d}t}=K_{9}P_{1}(t)-D_{T}(t), \tag{22}\]  
where \(c_{8}\) and \(K_{9}\) are the dynamic and static parameters to be identified, respectively.  
During normal operation, the steam flow rate of the steam turbine \(D_{T}(t)\) determines the efficiency of the steam turbine and the power of the generator [29],  
\[N_{e}=K_{10}D_{T}(t) \tag{23}\]  
where \(N_{e}\) (MW) is the output power of the steam turbine. \(K_{10}\) is the static parameter to be identified.  
To sum up, the feed-heat module, heat-pressure module and pressure-power module of a biomass power plant can be described by the following formulas:  
\[\begin{cases}\frac{\mathrm{d}B(t)}{\mathrm{d}t}=\frac{1}{c_{1}}(K_{1}\mu_{B}(t- \tau)-K_{2}N_{e}(t)B(t)(1-A_{ar}))\\ \frac{\mathrm{d}Q_{2}(t)}{\mathrm{d}t}=\frac{1}{c_{2}}(K_{3}N_{e}(t)B(t)-Q_{r} (t))\\ \frac{\mathrm{d}T(t)}{\mathrm{d}t}=\frac{1}{c_{3}}(Q_{r}(t)-K_{4}N_{e}(t)-K_{ 5}T(t))\\ \frac{\mathrm{d}Q(t)}{\mathrm{d}t}=\frac{1}{c_{4}}(Q_{r}(t)-K_{4}N_{e}(t)-Q(t) )\end{cases} \tag{24}\]  
\[\begin{cases}\frac{\mathrm{d}P_{\mathrm{f}}(t)}{\mathrm{d}t}=\frac{1}{c_{3}}(Q (t)-K_{6}\sqrt{P_{\mathrm{b}}(t)-P_{\mathrm{T}}(t)})\\ \frac{\mathrm{d}P_{T}(t)}{\mathrm{d}t}=\frac{1}{c_{6}}(K_{7}\sqrt{P_{b}(t)-P_{ \mathrm{T}}(t)}-D_{\mathrm{T}}(t))\end{cases} \tag{25}\]  
and  
\[\begin{cases}\frac{\mathrm{d}P_{\mathrm{f}}(t)}{\mathrm{d}t}=\frac{1}{c_{7}}( K_{8}\mu_{T}(t)P_{\mathrm{T}}(t)-P_{1}(t))\\ \frac{\mathrm{d}D_{\mathrm{T}}(t)}{\mathrm{d}t}=\frac{1}{c_{8}}(K_{9}P_{ \mathrm{f}}(t)-D_{\mathrm{T}}(t))\\ N_{e}(t)=K_{10}D_{T}(t)\end{cases} \tag{26}\]  
**Remark 1**.: _Based on the discussions above, one can learn that this model is based on first principles, where the biomass power plant system is divided into three processes, the feed-heat process, heat-pressure process and pressure-power process. Note that the dynamic parameters \(c_{1}\)-\(c_{4}\) represent the inertia time constant of the first module, \(c_{5}\)-\(c_{6}\) correspond to the second module and \(c_{7}\)-\(c_{8}\) are associated with the third module. These dynamic parameters represent the thermal inertia of biomass boilers, which could limit the ramp rate._
## 5 Proposed Method
### Routing Overhead  
Routing overhead is equal to the ratio of the total failed data packets in the data transfer process and control packets in the route discovery and maintenance processes to all the packets produced in the network. The first scenario tests the routing overhead based on the packet sending rate. It is displayed in Figure 16. According to this figure, there is a direct relationship between PSR and routing overhead, meaning that when the packet sending rate goes up, the routing overhead increases because high PSR increases the collision probability and the packet loss due to network congestion. As a result, the need for retransferring packets increases, and it increases routing overhead. The second scenario has evaluated the overhead in various methods based on the signal transmission radius (STR), and its results are shown in Figure 17. This figure shows that high STR leads to low overhead because the packet delivery rate is high, which reduces the need for retransferring data packets. According to Figure 16, which evaluates the overhead based on PSR, QFHR reduces the overhead by 30.23% and 41.4% compared to QGrid and GPSR, respectively. However, the proposed method increases the overhead by approximately 9.36% and 11.34% compared to IRQ and IV2 XQ, respectively. Moreover, according to Figure 17, which shows the overhead based on the signal transmission radius, QFHR decreases overhead by 21.99% and 25.63% compared to QGrid and GPSR, respectively. However, it has a higher overhead than IRQ (11.26%) and IV2 XQ (25%). IV2 XQ uses historical traffic information stored in the server memory in the Q-learning-based routing process to discover paths between network intersections, meaning that it does not exchange any control packet for discovering these routes. As a result, the overhead is extremely low in this method. Whereas, in QFHR, RSUs are responsible for discovering routes between intersections, and traffic information is periodically updated. This has increased routing overhead in this scheme. GPSR has the worst routing overhead due to the local optimum problem. QGrid also has high routing overhead because it has not used any congestion mechanism. As a result, when increasing  
Figure 14: Average number of hops in different approaches with regard to PSR.  
Figure 15: Average number of hops in different approaches with regard to STR.  
congestion in the network, packet loss increases. This increases the need for retransferring data packets.