esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Experiment 2: Training Strategy Selection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strategy** & **DSC** & **IoU** & **HD (mm)** & **Volume (Bland–Altman)** & **Volume (Correlation)** \\ \hline
**2.5D** & **0.820 \(\pm\) 0.041** & **0.725 \(\pm\) 0.052** & **10.353 \(\pm\) 4.974** & 153.943 \(\pm\) 50.149 & **0.734 \(\pm\) 0.090** \\ \hline
2D & 0.804 \(\pm\) 0.058 & 0.711 \(\pm\) 0.062 & 25.663 \(\pm\) 18.724 & 151.915 \(\pm\) 55.747 & 0.726 \(\pm\) 0.073 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of 2D and 2.5D strategies utilizing the Efficient-UNet model. All metrics are calculated in a 3D context. The comparison of automatically and manually obtained volumes (computed in pixels) was performed using a Bland–Altman plot (fourth column), as well as the correlation coefficient (fifth column) for organ volumes. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Organ**} & \multirow{2}{*}{**Strategy**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-6}  & & & & \multicolumn{2}{c}{**Volume**} \\ \cline{3-6}  & & **IoU** & **DSC** & **HD (mm)** & **Bland-Altman** & **Correlation** \\ \hline \multirow{2}{*}{Left Lung} & 2D & **0.895 \(\pm\) 0.033** & **0.944 \(\pm\) 0.018** & 14.492 \(\pm\) 13.280 & 225.900 \(\pm\) 101.430 & **0.978 \(\pm\) 0.009** \\ \cline{2-6}  & **2.5D** & 0.895 \(\pm\) 0.031 & 0.944 \(\pm\) 0.017 & **9.443 \(\pm\) 1.953** & **216.580 \(\pm\) 78.003** & 0.976 \(\pm\) 0.008 \\ \hline \multirow{2}{*}{Right Lung} & 2D & **0.912 \(\pm\) 0.011** & **0.953 \(\pm\) 0.006** & 70.157 \(\pm\) 43.920 & **209.380 \(\pm\) 47.580** & **0.994 \(\pm\) 0.001** \\ \cline{2-6}  & **2.5D** & 0.904 \(\pm\) 0.017 & 0.949 \(\pm\) 0.009 & **9.739 \(\pm\) 2.853** & 236.69 \(\pm\) 69.311 & 0.992 \(\pm\) 0.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of 2D and 2.5D strategies for organ segmentation using the Efficient-UNet. All metrics are computed in 3D. The comparison of the volumes (computed in pixels) obtained automatically and manually was performed by applying the Bland–Altman plot (fourth column), as well as the correlation coefficient (fifth column) for the organ volumes. The best results are highlighted in bold.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 9 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Research Paper** & **Purpose** & **Method** & **Dataset** & **Performance/Result** & **Limitations** & **Advantages** \\ \hline \multirow{4}{*}{[PERSON] et al., 2016 [75]} & \multirow{4}{*}{Detection of skull and abdomen} & \multirow{4}{*}{\begin{tabular}{c} TCNN (spatial and temporal \\ features) \\ \end{tabular} } & 323 videos (6–8 s duration, and temporal features) & \begin{tabular}{c} Accuracy range \\ of 70\% to 98\% \\ available) \\ \end{tabular} & \begin{tabular}{c} Accuracy is \\ data-dependent \\ \end{tabular} & \begin{tabular}{c} Multilabel \\ classification \\ \end{tabular} \\ \hline \multirow{4}{*}{[PERSON] et al., 2017 [76]} & \multirow{4}{*}{\begin{tabular}{c} Detection of fetal \\ standard planes \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} CNN and LSTM \\ (spatial and temporal \\ features) \\ \end{tabular} } & 1231 videos (2–5 s duration, 17–48 \\  & & & frames per video, \\  & & & 227 \(\times\) 227 \\  & & & & resolution, not \\  & & & & publicly \\  & & & available) & & \\ \hline \multirow{4}{*}{[PERSON] et al., 2021 [77]} & \multirow{4}{*}{\begin{tabular}{c} Detection of fetal \\ standard planes \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} CNN and RNN \\ (spatial and temporal \\ features) \\ \end{tabular} } & 1443 videos & \multirow{4}{*}{\
\begin{tabular}{c} Accuracy = \\ resolution, not \\ publicly \\ available) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Some standard \\ planes are \\ difficult to \\ classify \\ \end{tabular} } & \multirow{4}{*}{Low training time} \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of ultrasound video detection studies. For newcomers venturing into the field of medical diagnostic videos using deep learning, it is imperative to follow a systematic approach to effectively utilize the existing models for their research. Firstly, it is highly recommended for newcomers to go through various literature reviews of medical videos and images to gain a comprehensive understanding of the current state-of-the-art methods and their practical applications in the medical domain. As discussed earlier, LSTM, 3D CNNs, and transfer learning models are some of the promising approaches for beginners to start with. In addition, it is highly advisable to stay updated with newly emerging concepts and technologies in the field of deep learning. For instance, explainable AI [84] is an increasingly important topic that enables clinicians to better interpret and explain the predictions made by deep learning models. Similarly, Federated Learning [85] is another recent advancement in the field of deep learning that allows multiple institutions to collaboratively train models on their respective datasets without sharing sensitive patient data. By being aware of these emerging concepts and technologies, newcomers can broaden their horizons and enhance their understanding of the latest trends and developments in the field of medical diagnostic using deep learning.
## 3 Results and Discussion
### Limitations of the Method and Further Research  
It should be noted that, during our research, we encountered several problems related to the lack of open data regarding:  
1. Standardized public transport data, such as schedules, lines, the GPS positions of the stations, and the number of passengers per means of transportation. For instance, the schedule is available only in PDF format, making the transfer of data in a usable format rather difficult.  
2. Population data, consisting of the number of inhabitants and their distribution in the city, the main employers, and the number of workplaces. The most recent available population data were provided by the National Population Census in 2011, and the population number is only available at the district level but not at the level of neighborhoods/residential areas.  
Figure 15: Tram position in several timeslots, each color representing the vehicle position for a specific tramline (e.g., light-green points represent the vehicles operating on tram line no. 8; red points, vehicles on tramline 21) (���❝ OpenStreetMap contributors). (**a**) 05:01. (**b**) 07:51. (**c**) 16:43. (**d**) 23:21.  
The lack of data currently impedes the precise calculation of accessibility for the population living at walking distances from public transport stops/stations.  
Romanian cities are not registered on the platform dedicated to Open Mobility Database (\"OpenMobilityData--Public Transit Feeds from around the World\" n.d.), even though 1247 public transport operators and authorities from 673 cities around the world have included their mobility data on this website. The lack of these data raises difficulties in analyzing the reliability of the public transport network.  
The Traffic Analysis Zones used in the Sustainable Urban Mobility Plan 2016-2030--Bucharest-Ilfov Region are based on the data available from the 2011 Census. However, they provide useful information on the modal split in the region: public transport is used for home-based work trips by 33.2% of the population and by 54.9% for home-based education trips.  
Under these circumstances, it becomes difficult to correctly estimate the catchment area of a stop/station (the existing population within a walking distance of 400 m) and the passenger flow per station in a specific hourly range.  
The study did not consider the introduction of new tramlines, but when they are decided upon, the databases (GIS and schedule) can be easily updated and the iterations can be run under the new circumstances.  
The capacities of the vehicles are available, but the numbers of passengers boarding and alighting are not publicly available. For this reason, it is not currently possible to correlate the vehicle capacity with the demand and schedule. The presented model was conceived to allow the addition of other features such as the number of passengers per station and the vehicle capacity.  
The developed method could be very convenient for a low number of lines that use a common route segment.
## 6 The Proposed Fault Indicators
The approach proposed in Figure 11 can be applied for on-line fault detection, since the rotor position, stator currents, and ZSVC can be acquired continuously, and the calculation of the current angle \(\alpha\), the FFT (fast Fourier transform) of the current and ZSVC waveforms, and the fault indicators is very fast. Tables 4 and 5 summarize both the threshold value and the harmonics, which are based on the fault indicators \(FI_{I}\) and \(FI_{ZSVC}\) proposed in this section. The values of the fault indicators shown in Tables 4 and 5 have been calculated from the amplitudes of the harmonics presented in Tables 2 and 3. It is noted that when using a computer with an Intel core i5-6400T CPU of 2.2 GHz and 16 GB of RAM, the total time required to calculate the \(i_{d}\) and \(i_{q}\) currents from the phase currents, the current angle \(\alpha\), the FFT of the currents/ZSVC, and the faults indicators is about 3 ms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Harmonic** & **Fault Indicator (\(FI_{I}\))** \\ \hline
**Near rated operating conditions** & \(FI_{I}=I_{9 th,harmonic}-I_{1 st,harmonic}\) \\ Healthy (dB) & \(-37.91\) \\ Faulty (dB) & \(-34.14\) \\ Threshold value & \(-35\) \\ \hline
**Low-load near \(d\)-position conditions (current angle \(30^{\circ}\))** & \(FI_{I}=I_{3 rd,harmonic}-I_{1 st,harmonic}\) \\ Healthy (dB) & \(-24.38\) \\ Faulty (dB) & \(-31.40\) \\ Threshold value & \(-31\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Eccentricity fault indicator of the analyzed fPMa-SynRM based on the stator currents harmonics. Figure 11: Flowchart of the proposed approach including the fault indicators. \begin{table}
\begin{tabular}{c c} \hline \hline
**Harmonic** & **Fault indicator (\(FI_{ZSVC}\))** \\ \hline
**Near rated operating conditions** & \(FI_{ZSVC}=ZSVC_{9 th,harmonic}-ZSVC_{1 st,harmonic}\) \\ Healthy (dB) & \(-33.39\) \\ Faulty (dB) & \(-50.05\) \\ Threshold value & \(-48\) \\ \hline
**Low-load near \(d\)-position conditions (current angle \(30^{\circ}\))** & \(FI_{ZSVC}=ZSVC_{3 rd,harmonic}-ZSVC_{1 st,harmonic}\) \\ Healthy (dB) & \(-1.04\) \\ Faulty (dB) & \(-3.89\) \\ Threshold value & \(-3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Eccentricity fault indicator of the analyzed fPMa-SynRM based on the ZSVC harmonics.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 1 Introduction
Deep Long Short-Term Memory (LSTM) neural network was created, and in particular we also focused on the processing of the data's own features. Due to the power of machine-learning models, the processing of the data itself is often ignored. The main contributions are the following:  
1. The output of the training set was optimized by a Savitzky-Golay filter, and the deep LSTM network trained by this reduced overfitting and exhibited better generalization performance. 2. The optimal configuration parameters of the digital filter were determined. With such a configuration, the noise can be filtered out well and the original features retained, and the prediction model will not be overfitted due to the learned noise. 3. More accurate multi-step predictions were achieved based on machine learning and digital filtering, which can improve the timeliness of the prediction information. The content of the article is organized as follows: The content of Section 2 is about the basic concepts of the methods and models used. Section 3 is about data presentation and comparative analysis in practical analytical calculation cases. Section 4 summarizes all the content and gives the conclusion.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 2 Materials and Methods
### Theoretical Calculation of Adsorption Capacity  
The theoretical calculation of the adsorption capacity of \(\alpha\)-tocopherol acetate was conducted using the Langmuir equation, with the following assumptions: (1) monolayer adsorption, meaning that the surface has limited space; (2) the molecule does not alter the occupied surface area; and (3) there is no interaction between the adsorbate and the adsorption of further molecules [51].  
Since vitamin E adsorption occurs in the mesopores [34; 49], instead of using the BET surface (\(S_{BET}\)) to determine the theoretical monomolecular and maximal adsorption capacity of \(\alpha\)-tocopherol acetate (\(q_{theo,mono,max}\)), the results of the BJH surface area (\(S_{BJH}\)) for each mesopore class, derived from the BJH method, were utilised (Equation (1)). The BET surface area (\(S_{BET}\)) is not a good indicator for the adsorption of the vitamin E, unlike the BJH surface area (\(S_{BJH}\)) [25; 34]. Depending on the pore surface area (\(S_{BJH}\)), the theoretical adsorption capacity will vary while other parameters are kept constant. The calculation of the theoretical adsorption capacity was conducted in duplicate, as it was based on each BJH result (\(n\) = 2).  
\[q_{theo,mono,max}=\frac{M\times S_{BJH}\times 1000}{A_{M}\times N_{A}} \tag{1}\]  
- \(q_{theo,mono,max}\): adsorption capacity of \(\alpha\)-tocopherol acetate (mg/g);  
- \(M\): molar mass (=472.754 g/mol);  
- \(S_{BJH}\): BJH surface area (m\({}^{2}\)/g);  
- 1000: conversion factor;  
- \(A_{M}\): molecular surface area of \(\alpha\)-tocopherol (=1.1305 \(\times\) 10\({}^{-18}\) m\({}^{2}\));  
- \(N_{A}\): Avogadro number (=6.022 \(\times\) 10\({}^{23}\) mol\({}^{-1}\)).
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
## 2 Materials and Methods
### Quality Analysis
#### 2.6.3 Distance Analysis  
The point clouds were compared based on their corresponding distances using two different methods. The first method is the cloud-to-cloud distance measurement (called C2C). It describes an essential step in aligning and comparing two point clouds. This process aims to register two or more point clouds with each other to ensure their spatial correspondence. The second method is the M3C2 (multiscale model-to-model cloud comparison) algorithm, in which the point clouds are viewed as surface models. The distances between these models are calculated using approximate levels. Compared to the M3C2 algorithm, the cloud-to-cloud comparison is a coarser comparison method that requires less computational effort. The M3C2 algorithm, on the other hand, provides a detailed analysis based on the smallest changes in the surface of the point cloud. These two analyses were carried out for the two spherical camera models and the laser scanner.  
Initially, the point clouds were imported into Cloud Compare software (v2.12 beta) for point cloud analysis. It was important that the data were in the same coordinate system or could be mapped onto it using suitable transformations. In the comparison process, manual control point selection was carried out. Initially, one of the point clouds was selected as a reference (LS). This served as the basis for aligning the other point clouds. The Cloud Compare C2C toolbox leverages common algorithms to calculate an initial estimation of the transformation between the reference and target point clouds. The calculated initial transformation was refined by the equivalent control points to achieve the optimal match of the point clouds. Between the reference and the target point clouds, the direct distance to the nearest neighbor was calculated and iterative methods were also used to minimize the deviations between the points.  
The M3C2 algorithm calculates distance by measuring the distance along a local normal vector estimated from each point's neighborhood. This approach takes into account the local surface orientation in the distance computations. To find the locally averaged change between the two clouds, the algorithm projects search cylinders along the local normal vectors, therefore avoiding the need for meshing or gridding, which can introduce geometry errors in the terrain model [31]. The distance between the point clouds was determined within a cylinder defined on the reference point cloud, the center of which formed the normal of the average position of the points. The diameter of the cylinder was important to ensure the higher resolution of the result. If the cylinder diameter was too large, the average position of the points was determined from a larger surface [32]. This process was repeated for the pairs of point clouds: LS with X2, LS with X3, and X3 with X2.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 3 System Performance Analysis
### Relaying Model Formalization  
To model locations of vehicles along the lane, we utilize the shifted Poisson process. Recall that the probability density functions (pdf) of the distances [30] to the \(i\)-th neighbor in the Poisson process of vehicles follows Erlang distribution in the following form [31]:  
\[f_{i}(x)=\frac{2{(\pi\lambda)}^{i}}{(i-1)!}x^{2i-1}e^{-\pi\lambda x^{2}},\;x> 0,\;i=1,\ldots,N. \tag{6}\]  
The random distances to the nearest neighbors in the shifted Poisson process can be obtained by conditioning the minimum separation distance between vehicles. These separation distances between centers of the vehicles are given by \(d_{s}=t_{s}v+l_{v}\) leading to  
\[g_{i}(s)=\frac{2{(\pi\lambda)}^{i}}{(i-1)!}(s-d_{s})^{2i-1}e^{-\pi\lambda(s-d _{s})}^{2},i=1,\ldots,N. \tag{7}\]  
where \(g_{i}(s)=0\) when \(s<id_{s}\).  
When direct LoS communications are not feasible, it might still be possible to operate via a side reflection on an adjacent lane if both conditions are satisfied: (i) the vehicle-reflector is located in a proper position, and (ii) the blocker vehicle is not close enough to block the LoS towards the reflector. Denote the distance between the two communicating vehicles by \(r\). Then, the first condition means that the projection distance from the transmitter to reflector vehicle \(d_{r}\) should fall into the range  
\[\frac{r-l_{v}}{2}-r+\frac{w}{\tan(\delta_{R})}\leq d_{r}\leq\frac{r+l_{v}}{2}+ r-\frac{w}{\tan(\delta_{R})}, \tag{8}\]where \(\delta_{R}=\arctan\left(\frac{2\alpha_{r}}{\tau}+\frac{\alpha}{2}\right)\) and \(\alpha\) is the half-power beamwidth (HPBW) of the antenna radiation pattern. The second condition is defined by the following range for the distance to the blocker \(d_{O}\)  
\[d_{O}^{R}\geq\frac{w_{r}d_{r}}{w}. \tag{9}\]  
For under-vehicle propagation, the reflection off the road is always possible, provided that the blocker does not bar the propagation path leading to the range  
\[d_{O}^{U}\geq tg\delta_{U}(H_{T}-H_{V}), \tag{10}\]  
where \(\delta_{U}=\arctan\left(\frac{r}{H_{T}}\right)-\frac{\alpha}{2}\).
## 3 Results and Discussion
### Evaluation Index System Construction
#### 3.1.1 Construction Principles  
The evaluation of the service quality of the fresh agricultural produce supply chain platform should combine the common characteristics of the general supply chain and the inherent characteristics of the fresh agricultural products themselves. The principles of systematicity, comprehensiveness, scientificity, simplicity, representativeness, and hierarchy should be followed in the design of a service quality evaluation index system.
###### Abstract  
Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment.  
cross-layer; edge computing; graph learning; IIoT; task migration
# Study on the Prediction Method of the Ultra-Low-Cycle Fatigue Damage of Steel
## 3 ULCF Life Prediction for the Q345 qC Base Material and Weld Joints
### ULCF Predictions for Base Material and Weld Joints
#### 3.2.2 Fatigue Life Prediction Based on the CDM Model
Given the large dispersion of weld materials and the influence of the welding process, the error between the predicted and test values of the weld specimen is larger than that of the base metal specimen. The post-fracture path is simulated, as shown in Figures 9 and 10, to analyse the fracture process of steel and its weld joint. For the base metal specimen, the difference between the number of cycles and fracture life is only 2-3 cycles when the crack width arrives at half of the base metal specimen width. This result indicates that the crack propagates rapidly. Cracks develop from the cross section centre to the edge, and their direction of development is perpendicular to the axis direction of the \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(\Delta\epsilon\)\%} & \multicolumn{3}{c}{**Predicted**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & \(N^{p}\!_{f}\) & \(N^{p}\!_{r}\) & \(N^{p}\!_{f}\!/N^{p}\!_{r}\) & \(N_{f}\) & \(N_{r}\) & \(N_{f}\!/N\!_{r}\) \\ \hline
7.0 & 60 & 64 & 93.8\% & 77 & 82 & 93.9\% \\
8.0 & 47 & 50 & 94.0\% & 59 & 63 & 93.7\% \\
9.0 & 38 & 41 & 92.7\% & 44 & 48 & 91.7\% \\
10.0 & 31 & 33 & 93.9\% & 35 & 37 & 94.6\% \\ \hline \end{tabular}
\end{table} 
Table 6: Relationship between fatigue and fracture lives of the base metal specimen in the CDM model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(\Delta\epsilon\)\%} & \multirow{2}{*}{\(N^{p}_{f,C}\) (**Predicted**)} & \(N\!/\!\! specimen. The predicted post-fracture path is consistent with the test results, as shown in Figure 5a. For the weld specimen, the crack initiates at the centre of the cross section. With the increase in cycle numbers, the fatigue crack develops from the centre of the cross section to the surface of the specimen, near the narrow weld metal zone. Then, the fatigue crack expands along the interface between the weld metal zone and HAZ. Lastly, an oblique crack is formed and thus leads to complete fracture. The test results in Figure 5b correctly demonstrate the post-fracture path of the weld specimens. Figure 10: Development of cracks in the weld specimen (strain range, 7%). (**a**) \(N=27\), (**b**) \(N=35\) and (**c**) \(N=38\). Figure 9: Development of cracks in the base metal specimen (strain range: 9%).