esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Results and Discussion
### Quantitative Data Analysis  
Based on the interaction parameters defined for tracking events in the context of accessibility in serious games, quantitative data were collected and stored in LRS. We exported data from Learning Locker and analysed interaction parameters important for determining the interaction mechanism settings for individual use, such as task completion time, the option to skip tasks, and the subjective assessment of interactions. The focus of results analysis is on overall descriptive trends for these parameters rather than on comparing specific participant categories or establishing statistical relationships. Therefore, inferential analyses were not necessary. Instead, we employed descriptive statistics to summarize key findings across all participants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Age Group** & \
\begin{tabular}{c} **f** \\ **(N\({}_{\text{a}}\) = 79)** \\ \end{tabular} & **\%** & **Disability Group** &
\begin{tabular}{c} **f** \\ **(N\({}_{\text{d}}\) = 29)** \\ \end{tabular} & **\%** \\ \hline Under 18 & 9 & 11.39 & Visual & 1 & 3.45 \\ \hline
18–24 & 37 & 46.84 & Auditory & 3 & 10.34 \\ \hline
25–34 & 12 & 15.19 & Physical & 5 & 17.24 \\ \hline
35–44 & 6 & 7.59 &
\begin{tabular}{c} Cognitive, learning, \\ neurological \\ \end{tabular} & 8 & 27.59 \\ \hline
45–54 & 7 & 8.86 & Multiple & 12 & 41.38 \\ \hline
55–64 & 5 & 6.33 & & & \\ \hline
65 and more & 3 & 3.80 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Distribution of participants by age and disability type.
# Analysis of Earth Pressure Variation for Partial Displacement of Retaining Wall
## 4 Conclusions  
A horizontal trap-door experiment was conducted to research the active earth pressure distribution under different displacement modes and widths. The influence of the soil arching effect on the distribution of earth pressure was studied and the relationship of displacement and earth pressure was also investigated. Some conclusions obtained are listed as follows:  
The horizontal soil arching effect was generated after the active displacement of the trap-door. The existence of soil arching greatly reduced the earth pressure behind the trap-door and enlarged it on the box wall to exceed the static earth pressure. For the trap-door widths of 20 cm, 30 cm, and 40 cm, there may be a secondary soil arching effect.  
The variation of earth pressure with displacement amount indicated a tendency of decreasing first, then increasing and finally tending to be stable. This variation was divided into three phases corresponding to the evolution of soil arching.  
The distribution of active earth pressure varied with displacement along the height of the trap-door, showing obvious non-linearity. The active earth pressure in RT mode was larger than the Rankine or Coulomb earth pressure while it was smaller in T and RB modes.  
Analysis of the load sharing ratio showed that the minimum was reached at a relatively small displacement, then was not significantly reduced as the displacement increased. The width of the moveable wall to reach the minimum was not the same in different displacement modes.  
It should be mentioned that this test did not consider the theory to calculate the earth pressure distribution caused by the partial displacement of the retaining wall, so further theory analysis may need to be induced to study the distribution of earth pressure and the soil arching effect.  
Conceptualization, H.Z.; writing--original draft preparation, M.L.; funding acquisition, P.Z., X.L., and X.X. writing--review and editing, Z.Z. and X.S. All authors have read and agreed to the published version of the manuscript.  
This paper was supported in part by the Key Research and Development Program of Shandong Province (No. 2019 GSF109045), in part by the Qilu Young Scholar Program of Shandong University (20199900017).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
Figure 21: Variation of load sharing ratio in different modes: (**a**) T mode, (**b**) RB mode, (**c**) RT mode.
## 3 Results and Discussion
k-\(\omega\), and SST k-\(\omega\) turbulence models (this study). intensity was investigated with this model. Five different calculations were made by keeping the turbulence-length scale constant and changing the inlet-turbulence intensity between 1% and 10%. The results are shown in Figure 6a, together with the experimental and laminar numerical solution results. The turbulent transition regions are shown in Table 2. In addition, the results of the experimental study of [PERSON] et al. [25] are provided in Figure 6b for comparison. The experimental conditions of this experimental study are also shown in Table 3.  
intensity increases, the transition to turbulence occurs at lower Re numbers. In the case where the inlet-turbulence intensity is 1%, since the calculations are made in the range of 0 < Re < 700,000 and the turbulence transition region is outside this range, the turbulence transition region could not be determined and is shown with a \"-\" in Table 2. In the case where the inlet-turbulence intensity is 10%, the flow occurs directly as turbulent, and this time, there is no laminar and turbulent transition region, so it is shown with a \"-\" in Table 2.  
As seen in Figure 6a, with the increase in the inlet-turbulence intensity, the transition to turbulence became easier, and the laminar region shortened. Some results in Figure 6a are summarized in Table 2, and it can be seen that as the turbulence intensity increases, the transition region to turbulence narrows. This situation was also determined in the experimental study of [PERSON] et al. [25]. When Figure 6a,b are examined, it can be easily said that the numerical results in this study are consistent with the experimental results. Although the turbulence length scale was kept constant in this study, it should not be forgotten that this consistency should be evaluated qualitatively since it was not kept constant in the experimental study of [PERSON] et al. [25]. Because, as stated in the studies of [PERSON] and [PERSON] [40] and [PERSON] and [PERSON] [41] where their studies were evaluated, the turbulence-length scale is also effective on the boundary layer, along with the turbulence intensity. [PERSON] et al. [42] stated a significant effect of turbulence-length scale on the onset and the end of the bypass transition. But, in their study that investigates boundary layers corresponding to the ERCOFTAC test case TA+ for fixed Tu = 3% and an LS range of 2.2 mm-33.3 mm, Cf distribution does not affect length-scale (LS) changes above the LS = 15.3 mm. By representing ERCOFTAC test cases T3A, T3B, and T3 AM for transitional integral parameters, [PERSON] and [PERSON] [43] found that the transition location for the Tu level higher than 4% is only a function of turbulence intensity (Tu) and unaffected by significant changes in the length scale. Also, the importance of wall-friction fluctuations has been emphasized by [PERSON] et al. [42], which has been considered by the ERCOFTAC test case T3A+. But there is no information about experimental uncertainty for the friction coefficient, and the possible effects of this type of fluctuation must be kept in mind.
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 3 Proposed Method
### Active Contour Model Based on Improved SPF and LIF  
[PERSON] et al. [31] constructed the LIF model, which can effectively process nonhomogeneous images through local image information. Unfortunately, the model is sensitive to the initial curve and noise [2]. To construct a model that can process nonhomogeneous images and reduce the dependence on the location of the initial contour, this subsection combines the new SPF function with the existing LIF model to form a new ACM based on local and global image information.  
By combining the new SPF function with the LIF model, the new level set evolution equation is defined as  
\[\frac{\partial\varphi}{\partial t}=\lambda(I-I^{LIF})(m_{1}-m_{2})\delta_{ \epsilon}(\varphi)+(1-\lambda)spf(I(x))\cdot\alpha, \tag{22}\]  
where \(\delta_{\epsilon}(\phi)\), defined in Equation (10), is the regularized Dirac delta function and \(\lambda\) is a new weight coefficient.  
Here, \(\lambda\) is a weight function that can be employed to dynamically adjust the ratio between the local and the global term in image segmentation. Namely, the image information term playing a crucial role in segmenting an image can be selected.  
Based on the local and global image information, the weight coefficient \(\lambda\) is defined as  
\[\lambda=\frac{A}{\max(|B|)}, \tag{23}\]where \(A\) is defined in Equation (21) and \(B\) is defined as  
\[B=m_{1}+m_{2}, \tag{24}\]  
where \(m_{1}\) and \(m_{2}\) are defined in Equation (16).  
It is noted that the selection of the weight parameter \(\lambda\) is important in controlling the influence of the local and the global terms. [PERSON] et al. [PERSON] et al. (2016) declared that the local term is critical to the initialization to some extent; a global term is incorporated into the local framework, thereby forming a hybrid ACM. Therefore, with the mutual assistance of the local force and the global force, the robustness to the initialization can be improved, and the global force is dominant if the evolution curve is away from the object. When the contour is placed near the object boundaries, the LIF model plays a dominant role, and fine details can be detected accurately. In contrast, the new SPF model plays a key role when the contour is located far from the object boundaries, and owing to the assistance of the SPF, a flexible initialization is allowed. It follows that the automatic adjustment between the LIF and SPF models in our ACM is very distinct. Furthermore, the objective of the dynamic adjustment is to determine an optimal result for image segmentation.  
In general, the new proposed SPFLIF-IS model not only solves the problem that the intensity-inhomogeneous images cannot be accurately segmented by using the global image information but also overcomes the primary shortcoming that the model based on the local image information is sensitive to noise and the initial contour.
## 4 Discussion
In our research, the unique Endo tip was used with both a side-firing spiral tip and a sealed tip to prevent the transmission of radiation to or through the apical foramen of the tooth. Furthermore, the Endo tip fits the shape and volume of root canals and radiates directly laterally to the walls of the root canal after root canal preparation. According to our investigation [17], Er:YAG laser tip close to the biofilm showed a significant biofilm reduction. However, laser intensity was not found to significantly affect biofilm reduction. Contrary to the effect of diode laser irradiation on _E. faecalis_, the bactericidal effect of diode lasers was significantly better at 2.5 W compared to 1.0 W, 1.5 W, and 2.0 W [55]. The emitted energy from various laser systems in dentistry can be delivered into the root canal system by an optical fiber (Nd:YAG, Er:YSGG, and diode) or by a hollow tube (CO2 and Er:YAG) that has the ability to have a bactericidal effect and provide additional cleansing and disinfecting of the root canal system following biomechanical instrumentation. Laser-activated irrigation was presented by [PERSON] et al. and [PERSON] et al. [44; 56]. The treatment involves delivering a laser beam through a tip into the root canal filled with liquid. The energy of the Er:YAG laser is highly absorptive in water at between 1-3 \(\upmu\)m penetration depth [19], resulting in explosive boiling, cavitation bubbles that create shock waves in proximity to canal walls during their collapses and generating shear flows capable of removing particles from the surface [57]. Additionally, due to the emitted shock waves in proximity to the canal walls, they encounter the root canal wall at supersonic speeds. With this energy, the technique may be more efficient in cleaning and disinfecting the root canal system.
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 2 An Improved YOLOv8n Algorithm: PSP-YOLO
### PConv Module  
Partial convolution (PConv) [14] is a novel convolutional structure designed to address the inefficiencies in floating-point operations per second (FLOPS) caused by frequent memory accesses in deep convolutional architectures. Additionally, PConv reduces feature redundancy caused by high similarity among channel features in networks with numerous convolutional layers, thereby enhancing spatial feature extraction efficiency. Unlike standard convolutions, which apply filters across all channels, PConv performs convolution only on a subset of the input channels, leaving the remaining channels unchanged.  
Figure 3: Structure diagram of SPD-Conv module with scale = 2.  
By setting a partial rate r, PConv can control the proportion of channels involved in the convolution. The computational cost for conventional convolutions is given by:  
\[\text{FLOPS}_{\text{Conv}}=\text{ h }\times\text{ w }\times\text{k}^{2}\times\text{c}^{2} \tag{2}\]  
where h is the height of the feature map, w is the width, k is the filter size, and c is the number of channels in the convolution. The computational cost for partial convolutions PConv is:  
\[\text{FLOPS}_{\text{PConv}}=\text{ h }\times\text{ w }\times\text{k}^{2}\times\text{c}_{ \text{P}}^{2} \tag{3}\]  
where cP represents the number of channels used in the partial convolution. As shown in Figure 4, if the partial rate r = \(\frac{\text{cP}}{\text{c}}=\frac{1}{4}\), this indicates that only 1/4 of the input channels participate in the convolution, thus theoretically reducing the computational cost of conventional convolutions to \(\frac{\text{FLOPS}_{\text{Conv}}}{\text{FLOPS}_{\text{Conv}}}=\frac{\text{c}_ {\text{P}}^{2}}{\text{c}^{2}}=\frac{1}{16}\). Thus, the use of partial convolutions not only decreases the number of floating-point operations (FLOPs), but also alleviates the demand on hardware resources. By incorporating the PConv module into the YOLOv8n algorithm and replacing some of the C2f modules in the feature fusion network, this paper effectively reduces computational and parameter complexity while improving the detection accuracy of the PSP-YOLO model, resulting in better recognition of dangerous objects under vehicles.
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 3 Methodology
### Complexity Analysis  
The proposed TDDRMF framework achieves workload prediction by combining matrix factorization, temporal regularization, and nuclear norm regularization. Below, we analyze the computational complexity of each component and compare TDDRMF with related baseline models.  
Matrix factorization updates the microservice matrix \(W\) and temporal feature matrix \(X\) iteratively, with a per-iteration complexity of \(O(n\cdot T\cdot r)\). Temporal regularization incorporates autoregressive and periodic constraints to enhance temporal dynamics modeling, with a complexity of \(O((M+N)(T-d)\cdot r^{2}\cdot T)\). Additionally, nuclear norm regularization involves the computation of the singular value decomposition (SVD) of \(W\), which has a complexity of \(O(n\cdot r^{2})\). Combining these components, the overall complexity of TDDRMF per iteration is  
\[O(k(n\cdot T\cdot r+(M+N)(T-d)\cdot r^{2}\cdot T+n\cdot r^{2})).\]  
Under the assumptions \(r\ll n\), \(T\) and \(M,N,d\) are constants, the complexity simplifies to  
\[O(k((T-d)\cdot r^{2}\cdot T)).\]  
To evaluate TDDRMF's computational efficiency, we compare its complexity with statistical models (e.g., ARIMA), ensemble models (e.g., Random Forest), neural network models (e.g., LSTM), and matrix factorization models (e.g., TAMF). The updated complexity comparison is shown in Table 1.  
The computational complexity of TDDRMF and TAMF mainly arises from the low-rank constraints in matrix factorization and temporal regularization. Its global optimization approach eliminates redundant calculations caused by sliding windows, achieving lower computational costs and higher efficiency for multi-sequence modeling. In contrast, ARIMA's complexity is determined by the independent fitting of each sequence and the lag order \(p\), with recursive predictions significantly increasing the training time. However, ARIMA performs well for single-sequence modeling. The complexity of Random Forest is influenced by the sliding window size \(p\), the number of windows \(T-p\), and the number of trees \(L\), making it suitable for short-term pattern extraction but computationally expensive for long sequences. The complexity of LSTM is dominated by the hidden state dimension \(r_{h}\) and the number of training iterations \(k\), which allows it to capture long-term dependencies effectively, but its high computational cost limits its application in large-scale data scenarios. Overall, TDDRMF demonstrates the best efficiency and complexity control for multi-sequence modeling, while LSTM excels in capturing nonlinear dynamics, and Random Forest and ARIMA are more suitable for short sequences or low-dimensional tasks.
## 5 Conclusions  
This study was carried out to compare the fruit quality parameters and heavy metal contents of strawberries grown by organic and conventional methods. Within the scope of the study, organic and conventional strawberry samples collected from different stations were examined by comparing fruit quality parameters (fruit firmness, fruit size, TSS content, pH, fruit width and length) and heavy metal (Pb, Cd, Cu, Zn) contents. According to our study findings, the fruits grown in organic agriculture had greater width, length, weight and TSS values, while fruits grown in conventional agriculture had greater firmness values. According to our study findings, for heavy metal parameters, while the Zn metal content of strawberry fruits grown with conventional agriculture was higher than in organic agriculture, the Cu metal content was found to be higher in organic agriculture in the spring season and in conventional agriculture in the autumn season. The heavy metal content of the soil is of great importance in areas where agriculture will take place, especially in areas where organic farming will be practiced. When the data of the soil samples obtained as a result of this study were compared with the heavy metal limit values allowed in the soil in Turkey and the world, it was observed that heavy metal accumulation in the soil did not exceed the acceptable limit value. When the data of the samples taken from strawberry fruits grown by organic and conventional methods were examined, it was determined that there was Zn and Cu accumulation, but when compared with the heavy metal limit values allowed in plants by the World Health Organization (WHO) and the United Nations Food and Agriculture Organization (FAO), the heavy metal accumulation did not exceed the acceptable limit value. According to these results, it can be said that the region and the strawberries grown there do not show pollution in terms of Zn, Cu, Cd and Pb heavy metals.  
Conceptualization, S.S. and M.H.C.; methodology, S.S. and M.H.C.; data curation, S.S. and M.H.C.; writing--original draft preparation, S.S. and M.H.C.; writing--review and editing, S.S.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Akdeniz University, Unit of Scientific Research Projects, Antalya, Turkey, Project No. FYL-2020-5366 (only for research funding).  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
We acknowledge the Unit of Scientific Research Projects, Akdeniz University, for funding this project (FYL-2020-5366).  
The authors declare no conflict of interest.
## 4 Case Study
### Modeling Prediction Analysis
#### 4.2.2 Cross Validation  
Considering the necessary adaptability to dynamical changes, we use another dataset of 36,000 for 6-hour working time zone and obtain a final of 31,304 data samples for further cross validation to illustrate the proposed method's effectiveness and generality. The learning results are shown in Figure 11.  
It is obvious that the proposed method performs well in both training and testing processes. The average error in cross validation is 1.021%, which decreases since the data amount is becoming larger. In other words, data volume is essential for the deep learning method to function properly. This is just the advantage we explore in this novel model for prediction with operational \"big data\". Especially, the model can be updated by upcoming new data for more accurate results.
## 5 Conclusions  
To measure the transient voltages in an AC power grid safely, a non-contact measuring method was developed. An integrated optical electric-field sensor based on the Pockels effect was developed, as well as the measuring system. These measuring systems were used to monitor the lightning transient voltages and measure the typical switching transient voltages.  
Figure 12: Distribution of rising time and half wave time. (**a**) Rising time; (**b**) half wave time.  
(1) The measuring system has a response time less than 6 nanoseconds and a relatively flat amplitude-frequency characteristic from 5 Hz to 100 MHz. Its performances meet the requirements for measuring transient voltages in an AC power grid.  
(2) In 2017, 307 lightning transient voltage waveforms were recorded on a 220 kV overhead transmission line. All of these transients were caused by induction strokes with damped oscillation waveforms illustrated. Their rising time varies from several microseconds to about 200 microseconds, of which the middle value is 34.3 \(\upmu\)s. The distribution of rising time mainly ranges from 10 \(\upmu\)s to 50 \(\upmu\)s. The half wave time varies from several tens of microseconds to about 350 microseconds, of which the middle value is 105.25 \(\upmu\)s. The distribution of half wave time mainly ranges from 40 \(\upmu\)s to 160 \(\upmu\)s. These features are much different from those of the standard lightning impulse voltage.  
(3) Three types of typical switching transient voltages, including switching on the no-load long transmission line, switching on the no-load power transformer, and switching off the no-load power transformer, were measured during the commissioning of 500 kV middle Tibet power grid. A 1.73 p.u. overvoltage was recorded during switching off the no-load power transformer because the breaker cut off the current near its peak value.  
(4) Based on the wave transmission theory, the lightning stroke position, the actual transmission velocity of the traveling wave can be estimated with an accurate time of the transient voltage.  
The new measuring technology based on optical electric-field sensor supply a more convenient field measuring method for transient voltages.  
Methodology, S.X.; equipment development, S.X., H.Y. ([PERSON]); field experiment, Y.Z., H.Y. ([PERSON]), H.Y. ([PERSON]), X.C., and S.X.; project administration, S.C.; writing--original draft preparation, S.X., C.Z.; writing--review and editing, R.H., Z.M.  
This research was funded by the science and technology project of SGCC, grant number 52199918000G.  
The authors wish to thank the anonymous reviewers, whose comments assisted in improving the clarity of the paper.  
The authors declare no conflict of interest.