esa-sceva/satcom-chunk-collection huggingface dataset:
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 5 Giardia intestinalis
### G. intestinalis Infections from Food and Drinking Water
An \"adjusted likelihood ratio\" statistical tool that examines the association between outbreak cases and food distribution was developed to improve the identification of food products that should be analyzed for _G. intestinalis_ cysts [26]. The detection of _G. intestinalis_ in food was improved by the use of immunogenic separation (IMS) to isolate and elute cysts from the food sample before DNA extraction, followed by PCR targeted on _gdh_, _tpi_, _bg_, and the 18S rRNA encoding gene. A standardized method for the detection and enumeration of _G. intestinalis_ cysts on berry fruits and fresh leafy green vegetables based on IMS became available in 2016. However, the cost of IMS beads limits the implementation of this method in developing countries [26].
## 3 Results  
### Effects of Ultrasonic Frequency on the CO\({}_{2}\) Ultimate Absorption Amount and AR  
#### 3.1.1 Effects of Ultrasonic Frequency on the CO\({}_{2}\) Ultimate Absorption Amount  
The effects of ultrasonic vibration on the CO\({}_{2}\) ultimate absorption amount are shown in Figure 2. It can be seen that under mechanical agitation, the CO\({}_{2}\) AA in the cement paste increased gradually. When the CO\({}_{2}\) absorption reached 5, 10, 15, and 20 s, the CO\({}_{2}\) AA were 0.77%, 1.35%, 1.87%, 2.15%, and 2.47% of the cement mass, respectively. After an absorption time of 35 s, the cement paste could not further absorb CO\({}_{2}\) due to thickening and lost fluidity. At this point, the CO\({}_{2}\) AA of the cement paste reached 2.64% of the ultimate absorption amount.  
Compared with mechanical agitation, the CO\({}_{2}\) AR of fresh cement paste is significantly increased under ultrasonic vibrating agitation. At 5, 10, 15, 20, and 25 s, the CO\({}_{2}\) AA of fresh cement pastes under the fixed ultrasonic frequency of 40 kHz were 0.85%, 1.41%, 2.07%, 2.42%, and 2.61% of cement mass, respectively. These are 10.4%, 4.4%, 10.7%, 12.6%,  
Figure 2: Effects of ultrasonic vibration on the CO\({}_{2}\) ultimate absorption amount.  
and 5.7% higher than those under mechanical agitation at the same absorption times. The CO\({}_{2}\) AA reached a maximum at 30 s, approximately 2.66% of the ultimate absorption amount. Compared with mechanical agitation, the CO\({}_{2}\) ultimate absorption amount of fresh cement pastes under ultrasonic vibrating agitation is significantly increased with the shortening of absorption time.  
With the reduction of ultrasonic frequency, the CO\({}_{2}\) ultimate absorption amount of cement paste is increased to some extent. At 5, 10, 15, and 20 s, the CO\({}_{2}\) AA of fresh cement pastes at the fixed ultrasonic frequency of 28 kHz were 0.94%, 1.65%, 2.2%, and 2.53% of the cement mass. These increased to 1.19%, 2.09%, 2.84%, and 3.12% by decreasing the ultrasonic frequency to 20 kHz. At an ultrasonic frequency of 28 kHz, the CO\({}_{2}\) AA of fresh cement pastes reached 2.73% of the ultimate absorption amount at 25 s. At an ultrasonic frequency of 20 kHz, the CO\({}_{2}\) AA of fresh cement pastes reached 3.17% of the ultimate absorption amount at 25 s.
## 4 Experiments and Results
### Prediction
#### 4.2.2 Machine Learning Models and Training Procedure  
We evaluated three machine learning algorithms for classification: Logistic Regression (LR) [36], Random Forest (RF) [36], and Support Vector Machine (SVM) [36]. These algorithms were selected because they provide insights into feature importance during prediction [37]. We employed nested cross-validation for model training and evaluation, using stratified 4-fold cross-validation in the inner loop and stratified 10-fold cross-validation in the outer loop. We use the inner loop to optimize hyperparameters, while the outer loop estimates the models' generalization error [38]. Table 3 shows the hyperparameter ranges used for each algorithm.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Classifier** & **Hyperparameters** & **Values’ Interval** \\ \hline \multirow{4}{*}{LR} & penalty & [None, ’1’, ’12’] \\  & C & [0.01, 0.1, 1, 10, 100] \\  & solver & [’liblinear’, ’bfbfs’, ’newton-cg’, ’sag’, ’saga’] \\  & max\_iter & [100, 200, 300] \\ \hline \multirow{3}{*}{SVM} & C & [0.01, 0.1, 1, 10, 100] \\  & kernel & [’linear’, ’rbfs’] \\ \cline{1-1}  & gamma & [0.01, 0.1, 1, 10, 100] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters optimized using the Grid-Search technique for classification.  
Figure 7: Class distribution in different \(\text{CWA}_{L}\) thresholds.
## 3 Experimental Results and Analysis  
### Macromechanical Response Characteristics  
Synchronized CT real-time scanning and AE monitoring were conducted during uniaxial compression tests on Group 3 sandstone. Time-dependent stress-strain responses, AE activity patterns, and fracture evolution processes were acquired in real-time (Figure 6). The images in Table 3 display 10 mm cubic subsamples extracted from the central region of sandstone specimens at different feature points.  
Progressive fracture evolution of sandstone (Figure 6, Table 3) progresses through several distinct phases: (I-II) Initial loading: Minimal structural alteration with sparse AE activity. (Stage III) Unstable fracture initiation: Marked by volume expansion threshold (Point 3). Macrocrack nucleation occurs with exponential AE signal amplification. Peak strength attainment (Point 4) terminates Stage III. Crack coalescence develops into pervasive macrocracks with AE signal surge. Catastrophic failure ensues through brittle collapse mechanism. Pre-threshold phase: Transition from distributed microdamage to localized microcracks. Post-threshold phase: Evolution from stochastic microcracks to coalesced fracture networks. The fracture process exhibits nonlinear dynamics with critical state  
Figure 5: Selection of CT scanning points: (**a**) Key scanning point distribution for Group 1 (4 R-1 specimens); (**b**) Group 2 (4 R-2 specimens); (**c**) Group 3 (4 R-3 specimens).  
transitions. Critical thresholds manifest abrupt damage transitions, evidenced by AE signal discontinuities and fracture propagation patterns.  
Figure 6: Stress-strain responses and AE activity patterns during fracture development of sandstone specimens: (**a**) Specim R-3-1; (**b**) Specim R-3-2; (**c**) Specim R-3-3; (**d**) Specim R-3-4.
## 3 Numerical Examples
### Cantilever Beam
#### 3.1.1 Sensitivity of Stress Solution to Shape Parameters  
In conventional RPIM, the stress solutions are affected by shape function parameters in MQ-RBF. [PERSON] and [PERSON] reported that the RPIM performs the best when \(q=1.03\) or \(q=0.98\)[31]. The stress solutions obtained from conventional RPIM and improved RPIM for different \(q\) (\(q=-0.6\), 0.5, 1.03 and 1.25) with different \(a_{c}\) (\(a_{c}=0.5\), 1.5, 3 and 5) were computed, respectively. Stress solutions at coordinate position (24 m, 6 m) are shown in Figure 7. Results show that the stress solutions obtained  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Parameter** & \(x\) **(m)** & \(y\) **(m)** & \(\sigma_{x}\) **(KPa)** & **Relative Error** \\ \hline Analytic Solution & - & & \(\pm 6.0\) & \(\pm 1000.0\) & - \\ \hline RPIM & \(a_{c}=6\), \(q=1.03\) & 24.0 & \(\pm 6.0\) & \(\pm 927.6\) & 7.2\% \\ Improved RPIM & \(a_{i}=3\) & & \(\pm 6.0\) & \(\pm 1000.0\) & 0.0 \\ \hline Element Free Galerkin (EFG) & & & & \(\pm 6.0\) & \(\pm 982.0\) & 1.8\% \\ Improved EFG & \(a_{i}=4\) & & \(\pm 6.0\) & \(\pm 1000.0\) & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The stress solutions obtained from different methods at coordinate position (24 m, 6 m).  
Figure 6: Model of cantilever beam and distribution of field nodes.  
from improved RPIM are insensitive to the shape parameters (both \(q\) and \(a_{c}\)). The results also show that improved RPIM-based stress solutions are agreed with the analytical results.
# Investigation of the Performance of Basalt Fiber Reinforced Asphalt Mixture
## 4 Conclusions  
1. Basalt fiber-reinforced asphalt mixture is superior to the ordinary asphalt mixture in high-temperature stability, crack resistance at low temperatures and water sensitivity.
2. Considering the road performance and economic benefits, the optimum dosage of basalt fiberisabout0.3%.MS, DS, the maximum bending strain and TSR of asphalt mixture with 0.3% basalt fiber are increased by 19.6%, 25.5%, 22.2% and 6.0%, respectively.
3. By comparing the test results of lignin fiber and polyester fiber, basalt fiber has superiority in enhancing the low-temperature crack resistance of the asphalt mixture.
4. Considering large-scale natural disasters, such as low temperatures, rain, snow and freezing, often occurring in recent years, higher requirements are placed on the crack resistance of asphalt mixtures. Basalt fiber is more suitable for pavement construction in Jiangxi Province.  
Conceptualization, H.Z.; methodology, B.G.; validation, H.Z., B.G. and R.X.; formal analysis, H.Z.; investigation, B.G.; resources, R.X.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z.; visualization, A.Z.; supervision, A.Z.; project administration, H.Z. and funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D Plan of Jiangxi Provincial Department of Science and Technology (20192 BBG70064), the program of China Scholarships Council (No.201906825007), the program of Traffic Innovation Management Consulting Research Project of Yunnan Province (No.2019304) and the China Postdoctoral Science Foundation (No.2019M653520).  
The authors declare no conflicts of interest.  
Figure 8: The road performance of various kinds of fiber-reinforced asphalt mixtures: (**a**) MS, (**b**) DS, (**c**) maximum bending strain and (**d**) TSR.
## 2 Literature Review
### Spatiotemporal Learning Data Visualization
#### 2.2.2 Mouse Movement Data Visualization  
Mouse movement data visualization often relies on line-based representations to depict various mouse features. For instance, [PERSON] and [PERSON] [PERSON] and [PERSON] (2005, 2010) used lines to illustrate average mouse trajectories and employed curve plots to show mouse movement velocity and acceleration. They analyzed participants' psychological characteristics by comparing the deviation of actual mouse movement trajectories with idealized straight-line paths. Another approach, called the trajectory heat map ([PERSON] et al., 2016), applies the color and intensity of the trajectory lines to indicate mouse dwell time and passage frequency. The MatMouse visualization toolbox developed by [PERSON] and [PERSON] [PERSON] and [PERSON] (2016) designs dots on trajectory lines to represent mouse clicks, with the size of the dots indicating mouse dwell time. Furthermore, [PERSON] et al. [PERSON] et al. (2016) employed three-dimensional visualization methods, which, based on two-dimensional trajectories, add a z-axis to illustrate \"potential energy\" derived mathematically from mouse movement velocity. It reflects the \"energy\" state during decision-making. Higher values may indicate greater decision uncertainty or competition. By visually analyzing mouse trajectories, educators can better understand how students behave and make decisions, helping to identify learning challenges.  
While current visualization methods for eye-tracking and mouse movement data have proven valuable for analyzing spatiotemporal learning behaviors, they also present several limitations. Eye-tracking visualizations often involve a trade-off between clarity and detail: AOI-based methods simplify spatial complexity but lose finer details, whereas point-based techniques provide richer visualizations that may lead to clutter or misinterpretation, especially on two-dimensional displays. Mouse movement visualizations, such as trajectory lines or heat maps, are effective at illustrating patterns like pathways, dwell time, or decision-making \"energy\"; however, they are often designed to analyze single data types and rarely integrate other forms of complementary data, such as eye-tracking data. This lack of integration limits the ability to support more comprehensive and flexible analyses across multiple modalities. These limitations highlight the need for a more integrated and scalable solution. To address these challenges, we propose MultiScaleAnalyzer, a hierarchical framework that enhances the exploration of spatiotemporal learning data by balancing clarity and detail, while enabling integration of multiple data types, as described in Section 3.
## 4 Conclusions  
In this study, a model to predict observed safety climate levels by construction site personnel working in the construction industry of Saudi Arabia was developed. Significant predictors of the safety climate in construction sites were identified. The prediction model was developed using the bootstrapped multiple ordinal logistic regression statistical technique. The model revealed five statistically significant predictors: supervision, guidance, and inspection; social security and health insurance; management's commitment to safety; management's safety justice; and coworker influence. These predictors were classified under the three components of safety climate determinants: safety support, safety commitment, and safety interaction. A sensitivity analysis and scenario application showed that the model is responsive to extreme higher and lower input values and reflects them in the predicted output. Furthermore, the developed model can generate 67% correct predictions using the revealed set of predictors. Applications of the prediction model include assisting construction industry decision makers, safety policy designers, government agencies, and stakeholders to estimate construction site safety climates and determine areas of improvement. Targeting improvements for low-scoring predictors can translate to safety performance improvements.  
Collecting a larger sample size and reapplying this study's methodology is a possible direction for future research. This approach could confirm the revealed set of predictors using another dataset. Other machine learning prediction or classification techniques could also be used in future studies to confirm the set of predictors and improve prediction accuracy. The prediction model developed in this study represents patterns within the study's dataset describing the construction industry of Saudi Arabia. Conducting a similar study in other temporal and spatial contexts would also be a worthy topic of future research.  
Conceptualization, A.A.M. and I.M.; methodology, A.A.M. and I.M.; software, A.A.M.; validation, A.A.M. and I.M.; formal analysis, A.A.M.; investigation, A.A.M. and I.M.; resources, A.A.M. and I.M.; data curation, A.A.M. and I.M.; writing--original draft preparation, A.A.M. and I.M.; writing--review and editing, A.A.M. and I.M.; visualization, A.A.M. and I.M.; supervision, A.A.M. and I.M.; project administration, A.A.M. and I.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Full ethical approval of this study was granted by the Research Ethical Committee of the Center of Excellence in Genomic Medicine Research, King Abdulaziz University, (HA-02-j003).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.  
**Acknowledgments:** The authors acknowledge and thank the respondents of the survey questionnaire in this research study.
# Optimizing a Reverse Supply Chain Network for Electronic Waste under Risk and Uncertain Factors
## 4 Proposed Approach  
This section demonstrates how fuzzy mixed integer programming (FMILP) is constructed to deal with risk and uncertain factors in the e-waste RSC. In the real world, most input parameters are normally decided through historical data and experts' knowledge, which is a key challenge in an RSC network design [30]. The reason is that for some practical applications, there is not adequate historical data and it is difficult for decision-makers to determine these parameters exactly in RSC operations. Thus, some studies adopted a stochastic program based on different scenarios to address the issue, which leads to heavy computation [23].  
The input data of the RSC for e-waste are considered as unknown parameters (i.e., the number of returns, fixed cost, transportation cost, disposal cost, risk cost, and the capability of centers) because RSC is recognized as a dynamic system with a lot of uncertain factors involved. In most existing research, these parameters are assumed as deterministic and known in advance. This is not true in real-life applications, which always affects the performance of the RSC network. Therefore, the proposed model aims to consider these parameters by triangular fuzzy numbers. To handle these uncertain parameters, an FMILP is adopted. There are two main steps for applying the solution approach. In the first step, the FMILP model is transformed into an auxiliary model [60]. The second step is to implement an interactive fuzzy approach to find the optimal solution.
## 2 Proposed Circuit Configuration and Operation Analysis
### Mode III (\(t_{2}<t<t_{3}\))  
The current \(i_{Tp}\) becomes negative at the beginning of this mode. Both \(i_{Tp}\) and \(i_{LPFC}\) flow through \(S_{2}\). The current \(i_{LPFC}\) keeps increasing while the current \(i_{Tp}\) keeps decreasing. As soon as the gate signal \(v_{GS2}\) turns from a high level to a low level, the circuit operation enters Mode IV.  
Mode IV (\(t_{3}<t<t_{4}\))  
Both the current \(i_{LPFC}\) and \(i_{Tp}\) divert from \(S_{2}\) to flow through the parasitic capacitance of \(S_{1}\) when \(S_{2}\) is turned off. The parasitic capacitance of \(S_{1}\) is discharged, and the voltage across \(S_{1}\), \(v_{DS1}\) decreases. Since the parasitic capacitance is quite small, it is completely discharged very rapidly. When energy on the parasitic capacitance of \(S_{1}\) is totally released, \(i_{LPFC}\) and \(i_{Tp}\) divert from the parasitic capacitance to flow through the intrinsic diode \(D_{S1}\). The voltage across \(S_{1}\), \(v_{DS1}\) is clamped at zero volts. Thus, the ZVS  
Figure 3: The theoretic waveforms of key components.  
operation can be achieved. During this mode, the voltage across \(L_{PFC}\) and the transformer primary winding can be respectively expressed as  
\[v_{LPFC}(t)=v_{rec}(t)-V_{Cbus}, \tag{4}\]  
\[v_{Tp}(t)=V_{Cbus}-V_{Cb1}, \tag{5}\]  
The current \(i_{LPFC}\) can be expressed as  
\[i_{LPFC}(t)=i_{LPFC}(t_{3})+\frac{v_{rec}(t)-V_{Cbus}}{L_{PFC}}(t-t_{3}), \tag{6}\]  
Regarding a boost-type PFC converter, the dc-link voltage should be designed to be higher than the rectified voltage; therefore, the inductor current \(i_{LPFC}\) starts to decrease. The magnetizing current \(i_{Lm}\) and the primary current can be expressed as  
\[i_{Lm}(t)=i_{Lm}(t_{3})+\frac{V_{Cbus}-V_{Cb1}}{L_{m}}(t-t_{3}), \tag{7}\]  
\[i_{Tp}(t)=i_{Lm}(t)+\frac{N_{S}}{N_{P}}I_{Lo}, \tag{8}\]  
Since the primary voltage is positive, both \(i_{Lm}\) and \(i_{Tp}\) increase. On the other hand, the voltage across the secondary winding is positive. Therefore, the secondary winding supplies current to flow through \(D_{1}\) and \(D_{4}\) to the output inductor. After a very short deadtime, the gate signal \(v_{CSI}\) turns from a low level to a high level. This mode ends when \(i_{Tp}\) becomes zero and changes polarity.  
E. Mode V (\(t_{4}<t<t_{5}\))  
In this mode, \(i_{Tp}\) is smaller than \(i_{LPFC}\). The current flowing through \(D_{SI}\) is equal to \(i_{LPFC}\) minus \(i_{Tp}\). When \(i_{Tp}\) becomes higher than \(i_{LPFC}\), \(S_{1}\) is turned on at zero volts, and the circuit enters Mode VI.  
F. Mode VI (\(t_{5}<t<t_{6}\))  
In Figure 2f, \(v_{CSI}\) remains at high level and \(i_{LPFC}\) keeps decreasing. When \(i_{LPFC}\) decreases to zero, the circuit enters Mode VII.  
G. Mode VII (\(t_{6}<t<t_{7}\))  
In Figure 2g, the trigger signal \(v_{CSI}\) remains at a high state. The dc-link capacitor \(C_{bus}\) keeps supplying energy via the transformer to the output. At the instant when \(v_{CSI}\) turns into a low state, this mode ends and the circuit operation enters Mode I of the next high-frequency cycle.
###### Abstract  
This study investigates the blast-resistant performance of a polyurea-coated suspension bridge girder under explosive loads. The Hunan Road Bridge of Shandong was used as a case study through combined test and numerical simulation methods. Two 3 kg TNT charges and one 5 kg TNT charge were used to conduct two single-blast tests and one repeated blast test on a 1:3 scaled segment of a box girder. The tests were labeled as G (box girder without polyurea), PCG (first blast on box girder coated with polyurea), and PCGR (second blast on box girder coated with polyurea). A 1.5 mm polyurea layer was uniformly applied to the top surface. Numerical simulations of the explosion response were performed and validated using LS-DYNA software. The results indicate that under 3 kg of TNT detonation directly above the top plate, sample G exhibited an elliptical perforation, whereas sample PCG only experienced minor local dents without penetration. After a second detonation of 5 kg of TNT above the box chambers, sample PCGR displayed a nearly circular perforation in the top plate, along with cracks near the supports of chambers 1 and 3. For the main girder of the suspension bridge, multiple detonation points caused severe damage, rendering it impassable. However, after polyurea coating, the blast resistance significantly improved, with only minor spalling of concrete on the top plate and no other notable damage, allowing for continued passage. The polyurea layer effectively reduced the vertical displacement of the girder, and this reduction plateaued with increasing coating thickness. Under a 500 kg TNT blast, the optimal polyurea thickness to enhance blast resistance was determined to be 9 mm.  
self-anchored suspension bridge; concrete box girder; explosion-resisting performance; protection with polyurea; repeated explosion; numerical simulation