esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Welding Defects Tracked by YOLOv4 Algorithm
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.2 Comparison of Test Results  
The detected defects are illustrated in Figures 13-16, corresponding to the models listed in Table 5, from YOLOv4-cs2 to SSD, respectively. The tests primarily focus on images of pores, slag inclusion, incomplete penetration, and combinations of all three types of defects. From Figure 13, it is evident that YOLOv4-Tiny failed to detect all instances of pores, while the CenterNet model misclassified some pores as slag inclusion. In Figure 14, which shows images of slag inclusion, the SSD model incorrectly identified one region as a pore and failed to detect a small slag inclusion.  
Figure 15 clearly demonstrates that, during the detection of incomplete penetration, YOLOv4-cs1 missed the defect. Additionally, the detection frames of YOLOv4-Tiny and SSD identified locations that do not contain any defects. In the final inspection of images containing all three types of defects, it is evident that the YOLOv4-cs2 and YOLOv3 models  
Figure 14: Slag inclusion (blue frames). (**a**) YOLOv4-cs2, (**b**) YOLOv4-cs1, (**c**) YOLOv4, (**d**) YOLOv3, (**e**) YOLOv4-Tiny, (**f**) Centernet, (**g**) SSD.  
Figure 13: Pores (red frames).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Pore** \(\mathit{AP}\) & **Slag Inclusion** \(\mathit{AP}\) & **Incomplete Penetration** \(\mathit{AP}\) & \(\mathit{mAP}\) \\ \hline YOLOv4-cs2 & 92\% & 96\% & 74\% & 87.5\% \\ YOLOv4-cs1 & 90\% & 96\% & 72\% & 85.79\% \\ YOLOv4 & 88\% & 92\% & 75\% & 84.81\% \\ YOLOv3 & 88\% & 95\% & 76\% & 86.30\% \\ YOLOv4-Tiny & 71\% & 84\% & 43\% & 65.93\% \\ Centernet & 87\% & 93\% & 74\% & 84.75\% \\ SSD & 83\% & 91\% & 49\% & 74.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of \(\mathit{AP}\) and \(\mathit{mAP}\).  
perform better overall, with YOLOv4-cs2 showing greater accuracy in detecting incomplete penetration defects.
## 4 Modified Primary Compression Index Model
### Model Validation  
A bioreactor test considering the temperature effect was used for model validation. There was no degradation inhibition treatment in the test, so it was necessary to consider the influence of degradation on settlement. For the method of degradation settlement calculation, we adopted a method from the literature [34]. The sample was divided into five layers, each 24 cm in height. The schematic of the test is shown in Figure 11, and the initial parameters of the test are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Age** & \(\mathbf{t}\) (\(\mathbf{\mathrm{{}^{\circ}}}\mathbf{\mathrm{C}}\)) & **Size (cm)**  
The settlement achieved in the test is shown in Figure 12. The settlement and duration of primary compression in each layer of the samples was determined by the method in Section 2.3 of this article. The end times of primary compression in test 1 and test 2 were 7.11 d and 9.69 d; thus, temperature has an important impact on the settlement and duration of the primary compression in MSW.  
Figure 11: Schematic of the bioreactor test considering temperature effect.  
Figure 10: Relationship between the MPCI and WCI in MSW.  
Figure 12: Layered settlement over time in test 1 and test 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Test 1** & **Test 2** \\ \hline Temperature (\({}^{\circ}\)C) & 25 & 40 \\ \hline Vertical stress (kPa) & 42.4 & 42.4 \\ \hline Organic matter content (\%) & 60 & 60 \\ \hline Initial density (g/cm\({}^{3}\)) & 0.976 & 0.985 \\ \hline Dry unit weight (kN/m\({}^{3}\)) & 6.56 & 6.6 \\ \hline Moisture content (\%) & 32.9 & 32.9 \\ \hline Void ratio & 2.0 & 2.0 \\ \hline Wet mass (kg) & 237.3 & 237.3 \\ \hline Sample height (cm) & 123.6 & 122.5 \\ \hline A & 0.0645 & 0.0649 \\ \hline B & 20.211 & 35.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Initial parameters for bioreactor testing.  
The comparisons between the settlement calculated by the model in this article and the results of tests are shown in Figure 13. Comparing the calculation results of this article's model with those of [PERSON]'s model [24] and [PERSON]'s model [13], it was found that neither [PERSON]'s model nor [PERSON]'s model can reflect the differences in MSW primary compression under different temperature conditions. It has been proven that the MPCI model in this article is relatively reliable and that the calculation results of the model and tests results align well. There was a little deviation between the test results and the calculation results of the model; as shown in the figure, the deviations between test 1 and test 2 were 8.17% and 2.72% in the first layer, 1.54% and 5.3% in the second layer, 4.04% and 4.66% in the third layer, 2.47% and 1.61% in the fourth layer, and 1.15% and 3.21% in the top layer, and the overall deviation was less than 5%. The reason for the deviation is that when the volume of the MSW sample increased, the non-uniformity became more apparent compared to the small sample.
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.17. Telemedicine in Other Specialties  
Telemedicine is being increasingly adopted across various medical specialties in Poland. However, fields such as artificial intelligence, allergology, bariatric, dentistry, diabetes, hematology, laboratory medicine, maritime and tropical medicine, medical education, nephrology, neurosurgery, obesity, obstetrics/gyneecology, oncology, pharmacy, prenatal care, preventive medicine, radiology, rheumatology, surgery, telemedicine/eHealth, and digital twins remain underrepresented in academic literature. Initial investigations, often in the form of surveys, reviews, or pilot studies, have indicated the development of telemedicine applications. The COVID-19 pandemic has heightened interest, prompting research on its impact on service delivery and patient perceptions, particularly in bariatrics, diabetes, obesity, and prenatal care. Patient satisfaction surveys are common in nephrology, hematology, and dentistry. Clinical studies are exploring technologies, such as artificial intelligence in voice technology, mobile nutrition applications, and virtual patients in medical education. Specialized services include telemedical assistance in maritime and tropical medicine for mass casualties. Some specialties such as orthopedics, urology, and rheumatology integrate telemedicine with traditional care. In summary, telemedicine in Poland is expanding, with cardiology, emergency medicine, and psychiatry exhibiting mature research, whereas other fields remain in the pilot phase. The pandemic has stimulated interest in telemedical solutions for the continuity of care, with satisfaction surveys indicating a need for acceptance. Technological studies suggest ongoing innovation, although implementation lags behind that of more established fields.
# Evaluation of Three-Dimensional Environmental Hydraulic Modeling in Scour Hole
## 2 Methodology
### Numerical Study  
#### 2.2.1 Governing Equation  
Both models of Ansys Fluent and OpenFOAM use the RANS formulation in their codes, and are solved in the ensemble-averaged form. The governing equations of continuity and its RANS formula are as follows:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{3}\]  
\[\rho\left(\frac{\partial U_{i}}{\partial t}+\frac{\partial}{\partial x_{j}} \big{(}U_{i}U_{j}\big{)}\right)=-\frac{\partial P}{\partial x_{i}}+\frac{ \partial}{\partial x_{j}}\Big{(}2\mu S_{ji}-\rho\overline{u_{i}^{\prime}u_{j}^ {\prime}}\Big{)} \tag{4}\]  
In these equations, \(u\), \(U\), and \(u^{\prime}\) are the instantaneous, mean, and fluctuating velocities of the flow (m/s), respectively; \(t\) denotes the time in second (s); \(\rho\) is the density  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Q (m\({}^{3}\)/s)** & **\(h_{0}\) (m)** & **\(u\) (m/s)** & **\(d_{50}\) (m)** & **Re** \\ \hline
**exp.** & 0.035 & 0.144 & 0.405 & 0.0012 & 58,300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical experiment setup.  
Figure 1: Laboratory experiment setup sketch (**left**) and real picture (**right**) (modified from [18]).  
(kg/m\({}^{3}\)), \(P\) is the pressure (Pa), \(\mu\) is the molecular viscosity (kg/m\(\cdot\)s), and \(S_{ji}\) is the mean strain-rate tensor.
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 4 Experimental Setup
### Environment and Ground Rules
\]
18: Update reward \(r\leftarrow\text{gaussianReward}\)
19:elseif using Poisson model then
20: Apply Poisson reward adjustment: \[\text{poissonReward}= \begin{cases}\text{arrivetime}-(\text{arrivetime}\times \text{reward}\times v)&\text{if on time}\\ \text{arrivetime}-(\text{arrivetime}\times\text{reward}\times v)&\text{if too early}\\ \text{arrivetime}-(\text{arrivetime}\times\text{reward}\times v)&\text{if too late}\\ \end{cases} \]
21: Update reward \(r\leftarrow\text{poissonReward}\)
22:endif
23: Update Q-values: \[Q(s,a)\gets Q(s,a)+\kappa\left[r+\gamma\max_{s^{\prime}}Q(s^{\prime},a^{ \prime})-Q(s,a)\right]\]
24: Update the current state: \(s\gets s^{\prime}\). 25:until convergence
```  
**Algorithm 1** Unified model for transportation mode selection. Early and late arrivals incur penalties, whereas arriving on time neither incurs penalties nor earns rewards. This neutral treatment of on-time arrivals can be considered a reward in itself, as it ensures no negative impact is associated with punctuality. Showing whether a vehicle is on time or not will be different for each traffic group. In this way, both a realistic system and a fixed value are created in every size scenario because the total time to reach the destination in a very crowded environment is different from the time to reach it in less crowded traffic. In order to overcome this situation, each group is evaluated within itself, and the average of the arrival times of all vehicles is taken. All initial conditions and traffic configurations are described in detail to ensure the reproducibility of the experimental setup used in this study. The experiments were conductedusing SUMO 1.16 simulation software, and a campus-scale traffic network of 1 km \(\times\) 1 km was configured. The simulation network includes basic elements such as roads, pedestrian crossings, and traffic lights. In addition, the same area can be recreated using the Open Street Map (OSM) provided by SUMO to replicate the environment. To test different traffic densities, 100, 200, 300, and 400 vehicle/pedestrian configurations were used. The origin and destination points were randomly assigned among all valid nodes in the network. The traffic lights were configured according to SUMO's default algorithms, and each experiment lasted until the last vehicle completed its journey. The traffic density scenarios were low-density traffic (scenarios where the starting points were far apart), medium-density traffic (scenarios where the starting points partially overlapped), and high-density traffic (scenarios where the traffic lights caused congestion). Vehicle speed limits and congestion measurements were provided by the default settings of SUMO and the TraCI interface. In the future, it is planned to visualize the parameters used, such as starting points, traffic density distributions, and traffic light durations, and to document the simulation scenarios in detail.
## 2 Detector Working Principle  
There are two kinds of interactions between neutrons and nuclei, namely, scattering and absorption. In the scattering interaction, a neutron collides with a nucleus and transferssome portion or all its energy to the recoiling nucleus. In the absorption interaction, a nucleus absorbs neutrons and enters an excited state. Because of the high hydrogen content and large cross-section of (n, p) elastic scattering of the organic scintillator, the EJ200 plastic scintillator developed by Eljen Tech [16] was selected in this study. It had a scintillation yield of 10,000 photons per 1 MeV of deposited electron energy, a light attenuation length of 380 cm, rise and decay times of 0.9 and 2.1 ns, and a maximum emission wavelength of 425 nm. These characteristics made it suitable for the fast neutron detection.  
The SiPM is a solid-state photon-sensitive device based on the reverse-biased P-N diode. It features high quantum efficiency, low operating voltage, immunity to magnetic field interference, low cost, and easy integration into arrays. It is suitable for all applications where high measurement precision and quantification of low light/radiation levels are required. Owing to their better single-photon resolution capability, SiPMs have widely substituted for PMTs in scintillation photon readouts [17; 18].  
In this study, the fast neutron radiography detector consisted of an EJ200 array and a SiPM array. The schematic view of the detector is shown in Figure 1. Incident fast neutrons collided with hydrogen nuclei in the scintillators to produce recoil protons. Then, these protons deposited energy along their trajectory with the production of isotropic scintillation photons. Most of these photons were limited in the cuboid scintillator and transferred toward the SiPM array through the optical guide. Subsequently, these photons were distributed by the optical guide to SiPMs. Next, they were converted to electrical signals and recorded by the electronics.  
The EJ200 organic scintillator (Eljen Technology, Sweetwater, TX, USA) had a refractive index of 1.58. The K9 optical glass with a refractive index of 1.5 was selected as the optical guide placed between the EJ200 array and the SiPM array [19]. It had a low scattering cross-section for neutrons. The optical guide could reduce the total internal reflection of scintillating photons at the end face of EJ200. Meanwhile, it could enhance the transverse diffusion of outgoing scintillating photons to more SiPM pixels, which would ultimately improve the detection efficiency and position resolution of the detector for fast neutrons.
# A Review of Pool-Boiling Processes Based on Bubble-Dynamics Parameters
## 2 Bubble Nucleation
### Heterogeneous Nucleation
The value of nucleation cavities' density was expressed by calculating the minimum nucleation cavity density and the maximum nucleation cavity size:  
\[N_{\text{s}}=7.8125\times 10^{-29}\times(1-\text{cos}\theta)R_{c,min}^{-6}\left[1- \left(\frac{R_{c,min}}{R_{c,max}}\right)^{6}\right] \tag{8}\]  
[PERSON] and [PERSON] [36] synthesized the conservation of energy and presented the nucleation site density calculation equation involving the bubble detachment diameter, bubble departure frequency, and Jacob number as follows, where the Archimedean number \(Ar=\left(g/v_{L}^{2}\right)(\sigma/\rho_{L}g)^{3/2}\). \[N_{\text{s}}=\frac{6q}{\pi D_{d}^{3}\rho_{\text{o}}h_{\text{L}}f(1+A_{r})} \tag{9}\]  
From the perspective of enhanced boiling, boiling surfaces are often expected to have lower nucleation onset (ONB) and denser nucleation sites, thus generating more phase transitions and transferring more heat at low superheat. [PERSON]'s [25] criterion can be applied to a wide range of irregular surfaces and provides a valuable reference for surface cavity design. According to this criterion, the wall-contact angle of the liquid should be as large as possible, i.e., more hydrophobic. [PERSON] et al. [37] also noted that hydrophobic surfaces have lower boiling-onset temperatures and higher heat-transfer coefficients than hydrophilic surfaces under low superheat conditions. It should be noted, however, that while hydrophobic surfaces are more likely to nucleate, bubbles on hydrophilic surfaces are more likely to detach. [PERSON] et al. [38] found that hydrophobic surfaces have lower boiling-onset superheat and higher boiling heat-transfer coefficients than hydrophilic surfaces, but their critical heat flux (CHF) is lower, which may be caused by the excessive bubble size on hydrophobic surfaces, which results in no liquid wetting underneath the bubbles [39]. Therefore, a combination of hydrophobic and hydrophilic surfaces is considered a viable approach to surface design [40; 41]. It needs to be explained that the above criteria (Figures 4-6) can only be applied to ordinary rough surfaces. However, as shown in Figure 7, more and more complex surface structures are being used to enhance boiling heat transfer, such as metal foams [42], nanostructures [43], and porous media [44]. How to describe the disordered microstructure of these complex surfaces by mathematical logic is the urgent problem that still needs to be solved. Moreover, the force analysis of bubbles has an important effect on the disordered microstructure, so the research on force analysis of bubbles is reviewed in order to research the mechanisms of growth and detachment of bubbles after nucleation. Figure 7: Surfaces of three complex structures: (**a**) nanostructures [43], (**b**) porous media structure [44], and (**c**) copper metal foam structure [45].
## 1 Introduction
One study, at the professional level, compared activity profiles before and after the game format change and found the relative distance for all field players increased playing four quarters, but positional comparisons were similar before and after the rule change [18]. Examining the match in quarters, [PERSON] et al. [5] also saw the trend of defenders covering the highest absolute distance (5181 \(\pm\) 607 m) and the forwards the lowest absolute distance (4549 \(\pm\) 546 m), but when examining the relative distance, forwards were the highest (142.2 \(\pm\) 17.1 m\(\cdot\)min\({}^{-1}\)) and the defenders were the lowest (115.1 \(\pm\) 13.5 m\(\cdot\)min\({}^{-1}\)). Collegiate team organization, training, and play in the United States are distinctly different from international-level teams. In college, athletes typically play 1-2 games per week over a 10-11 week season. NCAA athletes are organized within three collegiate exclusive divisions, with Division I being the highest competitive level for collegiate play. Many international tournaments take place over 8-12 days, with 5-6 games played during the short time period [20; 21]. Practice schedules are also different. The NCAA limits practice time during the competitive playing season to 20 h per week [22]. In addition to practices and games, student-athletes must balance classes and other collegiate-related stresses. To our knowledge, only one study has examined activity profiles in collegiate female field hockey athletes and was conducted before moving to the four-quarter format [3]. Sixty-eight players from six different Division I universities were monitored for one regular season game. Similar to international players, forwards played less time than defenders and defenders covered less relative distance compared with both midfielders and forwards. Defenders also had significantly more distance in low-intensity running compared with forwards. The change in game format introduces more short-term recovery periods resulting from the break between quarters. This change can potentially lead to increased levels of high-intensity running. Understanding how this change affects activity profiles is important for coaches to structure their training appropriately. Activity profiles for collegiate field hockey athletes only exist for the game format of two halves. Therefore, the purpose of this study is to report activity profiles for Division I female field hockey athletes in the game format of four quarters. Additionally, this research will compare activity profiles in the two different game formats, including differences between positions. This research can provide a basis for further informed training at the collegiate level, including similarities and differences across player positions. We hypothesize that the change in rules will allow for increased high-intensity running and sprinting, in particular across forwards and midfielders, and the number of substitutions will increase.
## 3 Case Study  
### Participants  
To evaluate the effectiveness of the force tracking system, the present study analyzed the normal group and two patient groups diagnosed with subcortical vascular mild cognitive impairment (svMCI) or subcortical vascular dementia (SVaD) at Samsung Medical Center in Seoul, Republic of Korea. Eleven patients with svMCI (male = 6, female = 5; mean age = 74.5, SD = 5.6) and 13 patients with SVaD (male = 5, female = 8; mean age = 75.8, SD = 4.7) participated in this case study. All svMCI cases fulfilled the criteria suggested in [PERSON] et al. [16], and all SVaD cases met the criteria proposed by [PERSON] et al. [17]. Twelve normal participants (male = 4, female = 8; mean age = 71.6, SD = 7.3) were recruited as controls. The mean age and gender ratio of each group were approximately matched with each other as shown in Table 1 because force control capabilities may differ by age and gender [18; 19; 20]. This study was approved by the Institutional Review Board of Samsung Medical Center (IRB No. 2009-09-084).  
The primary lesions for svMCI and SVaD were located in the frontal and related subcortical areas. [PERSON] et al. [10; 14] have shown that svMCI and SVaD are associated with cortical thinning in the frontal regions that may cause MIDs. The cortical thinning largely overlaps between svMCI and SVaD; however, the severity is greater in SVaD since svMCI is regarded as the prod  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{3}{c}{**Gender**} & \multicolumn{3}{c}{**Age**} \\ \cline{2-6}  & **Male** & **Female** & **Percentage of Male** & **Mean** & **SD** \\ \hline Normal & 4 & 8 & 33\% & 71.6 & 7.3 \\ \hline svMCI & 6 & 5 & 55\% & 74.5 & 5.6 \\ \hline SVaD & 5 & 8 & 39\% & 75.8 & 4.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participant characteristics.  
Figure 2: Illustration of force control measures (IT: initiation time, DT: development time, ME: maintenance error, TT: termination time, TE: tracking error).
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 3 Results
### Performance and Validation of Models  
Figure 13, Tables 4 and 5 show the ROC curve, the AUC, and the classification accuracy of the used models. The RF model has achieved the highest performance (AUC = 0.982). The Kappa index reported by all models varied between 0.57 and 0.89. The RF model has achieved the highest predictive positive value (0.949), indicating a high probability of correctly predicting flood susceptibility. While the KNN, NB, and XGBoost models have achieved the values of 0.820, 0.730, and 0.948, respectively.  
Figure 12: Percentages of the FFS classes.  
Figure 13: ROC curve of the RF, KNN, NB, and XGBoost models validation.  
On other hand, the RF model has the largest negative predictive value (0.943), indicating that the model has a high probability of correctly categorizing non-flood susceptibility areas correctly. For the models KNN, NB, and XGBoost, this probability is 0.901, 0.934, and 0.930, respectively. The RF and XGBoost models have the highest sensitivity (0.949), indicating that 0.936% of the pixels were classified correctly as a flood. The pixels properly classified as flood are 0.924% and 0.962% for the KNN and NB models, respectively. Furthermore, the RF model likewise scored the best specificity (0.943), indicating that 0.94% of the non-flood areas were adequately classified as non-flood. The KNN, NB, and XGBoost models are 0.774, 0.605, and 0.943, respectively. These results indicate that, in the case of the KNN model, certain study area zones would be medium or highly susceptible to flooding. In contrast, in the instance of the RF, NB, and XGBoost models, these zones would be less prone to flooding. The findings indicate that the models used in this study adequately depict the positive connections between susceptibility maps and flood inventory points.