esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Name** & **Based on** & \begin{tabular}{c} **Publication** \\ **Year** \\ \end{tabular} & **Hardware** & **ECC Cracks** & \
\begin{tabular}{c} **Training** \\ **Resolution** \\ \end{tabular} \\ \hline MixCrackNet & Multiscale Attention & 2024 & 3090 & No & \(512\times 512\) \\ \hline
\begin{tabular}{c} Dynamic Semantic \\ Segmentation \\ \end{tabular} & Encoder–Crosso–Decoder & 2023 & 3060 & No & \(256\times 256\) \\ \hline Typical-RC-AI & VGG-16 & 2018 & 1080 Ti & No & \(224\times 224\) \\ \hline \multirow{2}{*}{DcsNet} & Resnet18+ & \multirow{2}{*}{2023} & Titan XP & No & \(640\times 544\) \\  & Pyramid Pooling & & & & \\ \hline DeepCrack & & 2019 & Titan-X & No & \(544\times 384\) \\ \hline CrackGauGAN & Generative AI & 2024 & 3090 Ti & No & \(256\times 256\) \\ \hline Ji’s Model & DeeplabV3+ & 2020 & Quadro P4000 & No & \(512\times 512\) \\ \hline SDDnet & Encoder+Decoder+ASPP & 2019 & 4 xTitan X & No & \(1024\times 512\) \\ \hline \multirow{2}{*}{CrackNex} & Resnet 101+ & \multirow{2}{*}{2024} & Titan RTX & No & \(400\times 400\) \\  & Fusion Module & & & & \\ \hline STRnet & & 2022 & Titan XP & No & \(1024\times 512\) \\ \hline \multirow{2}{*}{CSegNet} & DeeplabV3+; & \multirow{2}{*}{2024} & \multirow{2}{*}{3080} & \multirow{2}{*}{No} & \multirow{2}{*}{\(560\times 380\)} \\  & Transformer & & & & \\ \hline IDSnet & & 2022 & Titan XP & No & \(640\times 480\) \\ \hline FPHBN & & 2019 & Titan X & No & \(480\times 320\) \\ \hline Unet++ & U-Net & 2019 & 2080 Ti & No & \(512\times 512\) \\ \hline Attention-Unet & U-Net & 2019 & & No & \(48\times 48\) \\ \hline AFFnet & ResNet & 2019 & & No & \(224\times 224\) \\ \hline CrackSegnet & SegNet & 2021 & 1080 Ti & No & \(512\times 512\) \\ \hline Segnet & Segnet & & 1060 & No & \(256\times 256\) \\ \hline CrackUnet & U-Net & 2021 & 1080 Ti & No & \(256\times 256\) \\ \hline FCN & FCN & 2019 & 1070 & No & \(227\times 227\) \\ \hline ECC1 & U-Net & 2022 & 3090 & Yes & \(512\times 512\) \\ \hline ECC2 & U-net & 2023 & 3090 Ti & Yes & \(480\times 352\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Crack segmentation models and their training requirements. proposed. Based on this, scanning lines are used to compute crack parameter length and width and the number of complex cracks directly from inaccurate/incomplete segmentation maps. 4. Novel benchmarking methodologies were proposed for (A) testing the network on a wide variety of realistic cases of different concrete surfaces and (B) automated testing for the efficiency of image processing units. The results demonstrate that the proposed pipeline works well with variations in surface and crack features caused by constituents and surface preparation techniques encountered in concrete structures.
## 7 Discussion  
A tabular overview of PAT segmentation and quantification is shown in Table 1, while an overview of EAT segmentation and quantification methods is shown in Table 2. We have presented a wide range of methods for EAT and PAT segmentation. More traditional image processing methods like active contours and region growing can offer good, reproducible segmentation results but are often sensitive to noise or require manual intervention and verification. Machine learning and deep learning methods can provide fully automatic segmentation with robust segmentation performance but are often hard to interpret and fail in unforeseen ways. There is a comparatively small number of papers published on the subject of EAT and PAT segmentation when compared to other cardiac segmentation tasks [55].  
In literature, EAT segmentation inter-observer variability ranged in percentage from 7% to 14% [6; 23; 24] and in Dice scores from 0.89 to 0.91 [7; 25; 26]. To be considered valuable for use in both a clinical setting and in studies, an automatic method for EAT quantification should achieve results better or at least the same as the inter-observer variability. The lack of annotated data, especially large datasets, means that it is hard to objectively compare different methods, and one can not simply look at Dice scores to determine how accurate a method is. In this analysis, we took into account not only reported results but also the number of patients the method was validated on, as well as other potential ways the results might not be representative. The vast majority of papers attempt to segment EAT and PAT on CT images, with the notable exception of [PERSON] et al. [51], who use MRI images.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Automatic** & **Method** & **Dataset** & **Segm. Time** & **Result** \\ \hline [PERSON] et al. [54] & fully & deep learning & CT; 422 patients & - & DSC = 0.94 \(\pm\) 0.03 \\ [PERSON] et al. [51] & fully & 3D, atlas-based, graph-based & MRI; 6 patients & - & DSC = 0.82 \(\pm\) 0.06 \\ [PERSON] et al. [50] & semi & region growing & CT; 105 patients & \(>\)20 s & acc. 94\% \(\pm\) 6\% \\ [PERSON] et al. [52] & fully & fuzzy connectedness & CT; 23 patients & 1 min/slice & acc. 99\% \(\pm\) 0.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pericardial adipose tissue segmentation methods. DSC is the reported Dice coefficient. Segmentation time is the reported time to segment a single CT scan, unless specified otherwise.
# Study on the Improvement of a Collision Avoidance System for Curves
## 5 Discussion  
This study examined the improvement of the performance of a curvilinear coordinate-based AEB system. The proposed AEB system derived collision risks for each road environment by using the position and velocity information of the host and target vehicles, which were obtained through V2V communication. In addition, as the proposed AEB system calculated relative distances by converting the information of the host and target vehicles from an orthogonal coordinate to a curvilinear coordinate, the geometrical complexity of roads could be considered when determining the actuation time of the AEB system.  
To verify the proposed AEB system, a simulation was conducted by linking MATLAB/Simulink and PreScan. Furthermore, the effectiveness of the proposed algorithm was analyzed by comparing the simulation results between the proposed AEB system and the conventional AEB system for a curve with constant curvature and a reversed curve.  
The conventional and proposed AEB systems produced the following simulation results. Both AEB systems showed similar performance for a curve with constant curvature. However, for a reversed curve, the conventional AEB system, which did not reflect the geometric factors of the curve, could not accurately estimate the relative distance, a necessary parameter for deriving a collision risk. Accordingly, the conventional AEB system was delayed in actuating the braking, such that the final result was close to a collision. On the other hand, the proposed AEB system generated an average relative distance of 1 m. Thus, the proposed AEB system had a better performance than the conventional AEB system.  
This study proposed a curvilinear coordinate-based AEB system that considered various curvatures. The results revealed that the proposed AEB system could be effective in various driving environments. Finally, since this system can be applicable to road environments other than curves, it will contribute to advancing AEB technology.  
Conceptualization, G.K.; methodology, formal analysis G.K. and J.L.; software, validation, writing, J.L.; supervision, project administration, B.K.  
This research received no external funding.  
This work was supported by the 2016 Research Fund of University of Ulsan.  
The authors declare no conflict of interest.
## 3 Advanced Multidimensional Techniques for Aerosol WSOM Analysis
### Excitation-Emission Matrix (EEM) Fluorescence Spectroscopy for Water-Soluble OA Analysis  
EEM fluorescence spectroscopy is a high sensitivity and non-destructive 3D technique, widely used to compare and discern the dynamics of chromophoric water-extracted organic matter from field- and chamber-generated air particles [10; 34; 63; 77; 78; 79; 80; 81; 82; 83; 84; 85]. The acquisition of an EEM fluorescence spectrum involves the collection of sequential fluorescence emission (Em) spectra at successively increasing excitation (Ex) wavelengths. The Em spectra obtained are concatenated to produce a plot in which the fluorescence intensity is displayed as a function of Ex and Em wavelengths. EEM fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) modeling has made it possible to further resolve the complex 3D EEM spectra into its dominant fluorescent components and quantify each component's contribution to the total fluorescence [77]. In this regard, the reader is encouraged to refer to the tutorial of [PERSON] et al. [86] in the practical application of PARAFAC to fluorescence datasets, using complex organic mixtures fluorescence dataset. When applied to investigate the fluorescence features of chromophoric WSOM in atmospheric aerosols from different environments (urban, forest, marine, and pristine), EEM-PARAFAC aided in the classification and source identification of chromophores in atmospheric organic aerosols [10; 34; 63; 77; 78; 79; 80; 81; 82; 83; 84; 85]. The acquired EEM-PARAFAC data are of utmost importance to shed light on the pivotal role played by the WSOM in the optical properties and photochemical reactivity of atmospheric OA.  
An additional interesting application of multidimensional analytical approaches was shown by [PERSON] et al. [82], where EEM and HR-MS were applied for the characterization of the chromophoric WSOM in urban, forest, and marine aerosols. Statistical analyses of PARAFAC and non-negative matrix factorization (NMF) were applied to EEM and HR-MS spectra to resolve the different components of WSOM that exhibit different chemical-structural and fluorescence characteristics. Using EEM-PARAFAC, the authors reported the presence of three types of water-soluble chromophores, two with fluorescence characteristics similar to those of the so-called HULIS (which the authors defined as HULIS-1 and HULIS-2) and one with fluorescence characteristics similar to those of protein compounds (designated as PLOM) [82]. The HR-MS analyses indicated that the chromophoric components of HULIS-1 and HULIS-2 were associated with highly and less-oxygenated structures, respectively. HULIS-1 was ubiquitous in all water-soluble organic aerosol samples, whereas HULIS-2 and PLOM were abundant only in terrestrial and marine aerosols, respectively [82]. This combined application of multidimensional EEM and HR-MS techniques, aid by chemometric tools, set the basis for new analytic strategies aiming at the chemical structural identification and further understanding of the origin and fate of chromophoric WSOM in atmospheric aerosols.
## 4 Results and Analysis
### Network Parameter Evaluation
#### 4.2.3 Study of Bytes Transmitted in Payload and Transmission Time  
We investigated the impact of bytes transmitted after encryption on message transmission time. For each encrypted message, payload size after encryption and the total transmission time were recorded, as shown in Figure 19. The statistics are shown in Table 6. This dataset was used to analyze how encryption affects message size and network performance by comparing transmission time and encrypted message size with correlating size and transmission time.  
From Table 6 and Figure 19, it is evident that the distributions of bytes transmitted and transmission time are right-skewed. In the case of bytes transmitted, the mean (179.61 bytes) is more than the median (144 bytes). This indicates that a few large sizes in the payloads are pulling the mean upwards, and there are predominantly lower payload sizes. The trend is the same for transmission time. Regarding the spread and variability, transmitted bytes have a greater standard deviation (133.46 bytes), indicating that there is a wide dispersion from the mean and significant variability in payload sizes. In most cases, transmission  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Bytes Transmitted** & **Transmission Time (s)** \\ \hline Number of Data & 31 & 31 \\ \hline Mean & 179.61 bytes & 1.0655 \\ \hline Standard Deviation & 133.46 bytes & 0.0575 \\ \hline Min & 48 bytes & 1.0020 \\ \hline
25 th Percentile & 96 bytes & 1.0135 \\ \hline Median & 144 bytes & 1.0416 \\ \hline
75 th Percentile & 204 bytes & 1.1010 \\ \hline Max & 816 bytes & 1.2304 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical summary of bytes transmitted and transmission time.  
Figure 19: Bytes transmitted in payload after encryption (**left**) and transmission time (s) (**right**) for 31 different messages with varying sizes transmitted across hosts h1 and h4.  
times are more stable and constant compared to the sizes of the payload and correlation coefficient between transmitted bytes and transmission time, as shown in Figure 20.  
The scatter plot in Figure 20 visually represents the correlation between bytes transmitted and transmission time. Most of the data points are clustered together, with a regular trend of larger payload sizes taking longer to transmit. The trend line representing the correlation has a definite upward slope. The graphical representation of the positive correlation emphasizes the statistical computation of the direct relationship between the variables. The strong positive correlation coefficient value of nearly 1 indicates that with the increase in payload size, transmission time also increases proportionally.
## 4 Parameter Derivation
### Soil Ionization  
To illustrate the behavior of high-frequency soil conductivity and relative soil permittivity, the Scott model is habitually considered [37; 38]. To determine the mathematical formulation, Equations (27) and (28) should have been substituted with conventional soil conductivity and relative permittivity coefficients.  
\[\epsilon_{\mathrm{r}}(\mathrm{f})=5.491+0.946\;\sigma_{0}-1.097\;\mathrm{f}+0. 069\;\sigma_{0}^{2}-0.014\;\mathrm{f}\;\sigma_{0}+0.067\;\mathrm{f}^{2} \tag{27}\]  
\[\sigma(\mathrm{f})=0.028+1.098\;\sigma_{0}-0.068\;\mathrm{f}+0.036\;\sigma_{0}^ {2}-0.046\;\mathrm{f}\;\sigma_{0}+0.018\;\mathrm{f}^{2} \tag{28}\]  
However, there are many air-filled pores among the soil components; as shown in Figure 4, the soil model ionization has to be modified to represent the are transitions in air-filled pores, additionally.  
Figure 3: Fast relaxed vector fitting (FRVF) flowchart diagram.  
Therefore, the frequency-dependent consideration of soil conductivity and relative permittivity is not just enough. The model that could represent the full soil behavior is shown in Figure 5, that consists of a paralleled arc resistance and its capacitance series to the CIGRE resistance. The CIGRE model is mathematically formulated in (29) and (30).  
\[\mathrm{R_{g}}(I)=\frac{\mathrm{R_{0}}}{\sqrt{1+\frac{\mathrm{I_{ lighting}(t)}}{\mathrm{I_{g}}}}} \tag{29}\]  
\[\mathrm{I_{g}}=\frac{\mathrm{E_{0}}}{2\pi\mathrm{\sigma_{g}}R_{0}^{2}} \tag{30}\]  
where \(\mathrm{E_{0}}\) is the critical electric field intensity of the soil, and \(\mathrm{R_{0}}\) is the ground resistance with low current at low frequency. Consequently, the capacitance of air-filled pores is presented in (31) [39].  
\[\mathrm{C_{air}}=\frac{\mathrm{\rho_{0}\epsilon_{0}}}{\mathrm{R_{arc}}} \tag{31}\]  
where \(\mathrm{R_{arc}}\) is the arc resistance that created in the air-filled pores when the magnetic field could not be strengthened by the air. \(\mathrm{R_{arc}}\) is formulated with [PERSON]'s combined theory [40].
## 4 Discussion
### Performance Metrics Differences  
When examining the performance metrics, the study identified notable differences between the cycling and running tests. Cycling demonstrated higher peak power and mean power outputs, whereas the running tests were characterised by a higher fatigue index. This suggests a distinct stress profile for each test type, impacting athlete performance differently. This conclusion is supported by the distinct performance metrics (PP, MP, FI, and RPE) and energy system contributions when cycling- and running-based RSA assessments are compared. Recording the power output at 5 m intervals during each 35 m sprint in the running test might have revealed a higher PP associated with specific sprints, compared to the cycling test. This study observed important increases in blood lactate levels post-exercise; yet, no notable difference was found in the Lactate delta (mmol\(\cdot\)L\({}^{-1}\)) between the cycling and running tests. Determining the precise contribution of glycolysis is challenging, partly due to the variability in measurement timings across different activity modes. Furthermore, participants reported higher RPE values during cycling compared to running, while the FI% was elevated in the running test, as detailed in Table 2. Previous studies suggest that the distinct physiological characteristics evaluated in running and cycling may be attributed to running being inherently more familiar than cycling [36].The interplay between muscular strength and the optimal pedalling frequency partially explains the heightened perception of effort observed in cycling [37]. The FI% is influenced by the nature of recovery--active or passive--in repeated sprint tests employing different sprint protocols (running and cycling) [38]. Research indicates that passive recovery exerts a more favourable impact on FI% compared to active recovery [37; 39]. These findings corroborate the results of our study, wherein athletes typically engage in more passive forms of rest (such as sitting) during cycling tests compared to running tests: \"While both modalities included passive rest between sprints, the difference in energy demand between standing rest (in the running modality) and seated rest (in the cycling modality) could potentially explain some of the discrepancies in FI%\".
# Soil Spectral Behavior Related to Its Load-Bearing Capacity Based on Moisture Content
## 3 Results
### Bevameter Results (Pressure-Sinkage Relationship)  
The pressure-sinkage curves of the sandy loam soil resulting from the Bevameter test were read through the CATMAN 4.5 software (see Table 1). The results are shown in Figure 4.  
At each moisture content, the sinkage increases with the applied pressure (applied force divided by the plate area). The obtained pressure-sinkage relation at each moisture content complies with the pressure-sinkage equation suggested by [PERSON] in 1959 that is based on elastic half-space Boussinesq theory (Equation (2)) [29; 31; 32; 33; 34].  
\[\mathrm{P}=\mathrm{k}\left(\frac{\mathrm{Z}}{\mathrm{D}}\right)^{n}, \tag{2}\]  
Figure 4: Sandy loam pressure–sinkage relationship at different moisture contents.  
where: p: the applied pressure; D: the diameter of the plate; Z: the soil vertical deformation (sinkage); k: the sinkage modulus.  
Moreover, [PERSON] fitted the plate pressure-sinkage curve using Equation (3):  
\[\mathrm{p}\cong\mathrm{k}\mathrm{z}^{0.5}, \tag{3}\]  
where k is a modulus of inelastic deformation, and 0.5 is the exponent of sinkage [35].  
The above two equations show that the sinkage is directly proportional to the pressure applied, and that is clear in the obtained curves.  
At different moisture contents, with the same applied pressure, the resulting sinkage increases with the increase in moisture content. The pressure-sinkage curves follow the same trajectory (hyperbolic) at different moisture contents. At the maximum applied pressure (around 25 bar), it is recognized that the highest sinkage is at the highest moisture content and that the sinkage increases with the increase in the moisture at the fixed pressure.
## 5 Discussion
In terms of optical images, the planet images have three advantages. (1) Planet Satellite can provide high frequency, medium-high-resolution (3-4 m) remote sensing images, which ensure to acquire detailed temporal and spatial information of landslides. Especially for the landslides with high density, we can follow the principles of image interpretation to prepare the landslide inventory based on Planet images. (2) High data coverage efficiency: The Planet small satellite constellation has more than 170 satellites, which can achieve global daily coverage. In the past, it was impossible to obtain images covering the entire earthquake zone in one month or even few months after the earthquake (such as the Wenchuan, Nepal, and Lushan earthquakes), which brought great difficulties to the construction of the seismic-landslide database [31; 32; 51]. The Planet satellite changed this status, and the images can be acquired within a few days after the earthquake, which greatly increases the acquisition speed of landslides. (3) Planet images are merit for the high precision and slight noise. This ensures the accuracy and completeness of landslides interpretation. Otherwise, this study further proves the feasibility and reliability of the two types of machine learning methods for large-scale, high-density seismic landslide susceptibility mapping. Previous studies [11; 52] have shown that machine-learning methods are more accurate than Newmark and human empirical models. However, the machine-learning method has high dependence on the comprehensive and objective landslide dataset, which makes the method have low efficient, and the assessment results are mostly years after the earthquake, which led to the fact that scientific research results could not be applied to practice [27]. But, the planet images can be acquired within a few days after the earthquake, a comprehensive and objective earthquake landslide database can be obtained quickly, then the machine learning method was used to conduct accurate landslide susceptibility mapping, which can be applied to landslide disaster prevention and mitigation in earthquake-stricken areas in the short-term after the earthquake. In this study, we completed the landslide inventory within 1 month after the earthquake, so that the assessment results can be applied to the medium-term resettlement and reconstruction, and are helpful to earthquake landslide prevention and mitigation.