esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Auxiliary transport vehicles are essential components of the underground mine auxiliary transportation system, primarily used for tasks such as personnel and material transportation. However, the underground environment is complex, and unstructured roads exhibit significant randomness. Traditional passive hydropneumatic suspension systems struggle to strike a balance between ride comfort and stability, resulting in insufficient adaptability of auxiliary transport vehicles in such challenging underground conditions. To address this issue, this paper proposes a multi-mode hydropneumatic suspension control strategy based on the identification of road surface grades in underground mines. The strategy dynamically adjusts the controller's parameters in real time according to the identified road surface grades, thereby enhancing vehicle adaptability in complex environments. First, the overall framework of the active suspension control system is constructed, and models of the hydropneumatic spring, vehicle dynamics, and road surface are developed. Then, a road surface grade identification method based on Long Short-Term Memory networks is proposed. Finally, a fuzzy-logic-based sliding mode controller is designed to dynamically map the road surface grade information to the controller's parameters. Three control objectives are set for different road grades, and the multi-objective optimization of the sliding mode's surface coefficients and fuzzy-logic-based rule parameters is performed using the Hiking Optimization Algorithm. This approach enables the adaptive adjustment of the suspension system under various road conditions. The simulations indicate that when contrasted with conventional inactive hydropneumatic suspensions, the proposed method reduces the sprung mass's acceleration by 21.2%, 18.86%, and 17.44% on B-, D-, and F-grade roads, respectively, at a speed of 10 km/h. This significant reduction in the vibrational response validates the potential application of the proposed method in underground mine environments.  
auxiliary transport vehicle; hydropneumatic suspension; vehicle vibrational response; road surface grade identification; fuzzy-logic-based sliding-mode control +
Footnote †: journal: Applied sciences  
Article
# Oxygen Isotopic Compositions of San Carlos Olivine Standard NMNH 111312-42
## 4 Discussion
### Comparison with Other Olivine Reference Materials  
Compared to -44, the larger size of -42 olivine grains offers practical advantages for in situ analysis by accommodating more analytical points per grain, reducing variability introduced by inter-grain differences, and minimizing standard sample waste. These larger grains also reduce errors introduced by inter-grain variability, further enhancing analytical reliability.  
In recent years, some new olivine standard samples for oxygen isotope analysis have been developed (Table 2). [PERSON] et al. [17] evaluated the potential as SIMS reference materials of five olivines from mantle peridotite xenoliths and dunite. The obtained results revealed that the oxygen isotopic values of these samples are homogeneous and suitable to be used as reference materials for in situ oxygen isotope microanalysis. The recommended \(\xi^{18}\)O value for these five samples ranged from (3.91 \(\pm\) 0.25)% to (5.30 \(\pm\) 0.13)%. [PERSON] et al. [18] developed two new olivine geostandard samples from the Jian forsterite jade deposit and examined the homogeneity of their oxygen isotope. The recommended \(\xi^{18}\)O values are (16.37 \(\pm\) 0.11)% and (18.29 \(\pm\) 0.14)%, respectively. Notably, these two types of olivine samples have extremely high magnesium contents (Fo > 99), making them potential reference materials only for SIMS oxygen isotope analysis of high-magnesium olivine.  
When considered alongside the recent advancements in olivine geostandards, the -42 olivine grains represent a valuable addition to the suite of available geostandards. These materials collectively provide researchers with a diverse and reliable set of tools for precise and accurate oxygen isotope analysis, advancing our understanding of geochemical processes across a wide range of geological environments.
## 10 Current Performance
### Experiments to Assess Performance  
Reference [31] describes aspects of the measurement protocols used for the RIID algorithm comparisons. One important task is to choose the number of repeat measurements per scenario, where a measurement scenario is defined by the source, geometry, shielding, and count time. There is a tradeoff between how many measurement scenarios can be assessed with how many repeat measurements. Options to augment real repeat measurements with synthetic (using MCNP-X for example, or simply using Poisson random numbers using estimated energy channel means) should be evaluated in performance assessments. Our view is that until simulated data is sufficiently realistic, the potential for algorithm improvements is not fully understood.  
Section 6 mentioned a measurement protocol that requires measurements of the source at several distances between the source and detector as a way to correct for gain shift. RIID algorithm performance assessments should provide a convenient repository of data for evaluating candidate measurement protocols.
###### Abstract  
Despite the fact that a 2-degree spectral sensitivity curve (SSC) is extensively used in scientific research and relevant applications, the choice between the 10-degree or the 2-degree photopic SSCs in practical applications for the calculation of scotopic/photopic ratios (_S/P_ ratios) depends on actual needs. We examined _S/P_ ratios for more than 300 light sources for correlated colour temperatures (CCTs) from 2000 K to 8000 K and blackbody radiant spectra from 10000 K to 45000 K using 2- and 10-degree SSCs. Results showed that the ratio of the _S/P_ values calculated using the 10-degree and 2-degree SSCs was approximately equal to 0.916. The average mesopic luminance difference increased from 0% to 5.7% at a photopic adaptation luminance from 0.005 to 5 cd/m\({}^{2}\). For most practical applications, the mesopic luminance values calculated using these two SSCs were different by several percentage units, yet these differences could be neglected. At extremely high CCTs over 10000 K, the mesopic luminance difference may approximate the maximum value of 16%. This work proposes the conversion coefficients for _S/P_ ratios and the transforming mesopic luminance values calculated for 2- and 10-degree SSC systems. These results may help researchers clarify differences between the _S/P_ ratios calculated using different SSCs.  
mesopic vision; luminous radiation efficacy; scotopic vision; mesopic luminance; photopic adaptation luminance +
Footnote †: journal: Applied
## 5 Discussion  
### Model Performance Advantages and Limitations  
The IBKA-CNN-Transformer model proposed in this study exhibits outstanding prediction performance, with coefficients of determination (R\({}^{2}\)) of 0.99 and 0.98 for the training and test sets, respectively, demonstrating its effectiveness in predicting weir life. This performance improvement is largely attributed to the hybrid structure of the model. The CNN module excels at capturing intrinsic patterns in geometric attributes through its powerful local feature extraction capabilities, while the Transformer module accurately models global features and long-range dependencies using the multi-head self-attention mechanism. Additionally, the enhanced IBKA algorithm optimizes the model's hyperparameters, improving its adaptability and generalization to complex nonlinear data. The introduction of SHAP analysis further enhances model interpretability by quantifying the contributions of key features to predictions, providing a clear basis for identifying the primary drivers of weir life.  
However, despite its strong performance, the model exhibits high computational complexity and significant training costs, particularly when handling large-scale datasets. Although the IBKA algorithm mitigates overfitting in deep-learning models with small sample sizes to some extent, the model still relies heavily on data volume and quality. Moreover, the model's reliance on geometric features and hydrological conditions may lead to performance degradation when key features are missing, which warrants careful consideration in practical applications.  
The model accounts for several critical factors influencing the stability and longevity of landslide dams, including hydrological conditions, material properties, and triggering factors such as rainfall, earthquakes, and extreme weather events. However, the potential impact of shock waves from seismic activity is not explicitly included in the model. These shock waves, especially from large-scale seismic events, can significantly affect dam stability and trigger landslides, representing a limitation of the current model. Additionally, while the model considers extreme weather as a triggering factor, the broader impacts of climate change, including long-term shifts in environmental conditions, have not been fully integrated. This remains another limitation that could be addressed in future versions of the model, particularly to better capture the cumulative effects of extreme weather patterns over time.
## 3 Combined Method for Occupancy Time Calculation
### Occupancy Time of Two Trains  
For two trains with partial repeated occupancy, substituting Equation (4) into Equation (5), we can obtain:  
\[OT_{ij}=OT_{i}+OT_{j}-(OT_{i}-(d_{j}-d_{i}))=OT_{j}+d_{j}-d_{i} \tag{6}\]  
According to Equation (1), \(OT_{j}=TT_{j}+I_{js}-TT_{s}\). Substituting into Equation (6), we can obtain:  
\[OT_{ij}=TT_{j}+I_{js}-TT_{s}+d_{j}-d_{i} \tag{7}\]  
Substituting \(TT_{j}=a_{j}-d_{j}\) into Equation (6), we can obtain:  
\[OT_{ij}=(a_{j}-d_{i})+I_{js}-TT_{s} \tag{8}\]  
Figure 5: Occupancy time of two trains with full repeated occupancy.  
By observing the form of Equation (8), if train \(i\) and train \(j\) are regarded as a combined train \(\overline{ij}\), as shown in Figure 6, taking \(d_{i}\) as the departure time and \(a_{j}\) as the arrival time, the content in the parentheses of Equation (8) is the section travel time \(TT_{\overline{ij}}\) of train \(\overline{ij}\).  
\[OT_{ij}=OT_{\overline{ij}}=TT_{\overline{ij}}+I_{j_{\text{s}}}-TT_{s} \tag{9}\]  
For two trains with full repeated occupancy, substituting \(RT_{ij}=OT_{j}\) into Equation (5), we can obtain:  
\[OT_{ij}=OT_{i}=TT_{i}+I_{is}-TT_{s} \tag{10}\]  
Therefore, we can regard train \(i\) and train \(j\) as a combined train \(\overline{ij}\), as shown in Figure 7, taking \(d_{i}\) as the departure time and \(a_{i}\) as the arrival time.  
According to the above analysis, two trains with a repeated occupation relationship can be regarded as a combined train whose departure time is the earliest departure time of two trains and whose arrival time is the latest arrival time of two trains. Then, the occupation time can be calculated according to Equation (1). This method does not need to calculate the repeated occupancy time between two trains, nor does it need to calculate the occupancy time of the two trains, which greatly simplifies the calculation process.  
Figure 6: Combined method for two trains with partial repeated occupancy.  
Figure 7: Combined method for two trains with full repeated occupancy.  
The occupancy time of the continuous train group \(L\) is:  
\[OT_{L}=TT_{L}+I_{Ls}-TT_{s} \tag{11}\]  
In this way, the calculation of multiple train occupancy time can be transformed into the calculation of one combined train, which greatly simplifies the calculation process. This method is also applicable to one single train or two trains.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 5 Verification of Frame Experiment  
### Model and Damage Scenario  
The experimental three-layer plane frame structure was made of Q235 steel, shown in Figure 10a. Its elastic modulus was 2.10 GPa, and its density was \(7.85\times 10^{3}\) kg/m\({}^{3}\). The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **OMP** & **IOMP** \\ \hline Damaged substructures & [3, 29, 15, 30, 48, 1, 6] & [3, 29, 15, 30, 48] \\ \hline Residual stiffness & [49.87\%, 60.44\%, 69.40\%, 79.06\%, 80.59\%, 93.01\%, 92.81\%] & [49.92\%, 60.50\%, 69.50\%, 79.09\%, 80.62\%] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identification results of space truss by OMP and IOMP method.  
Figure 9: Space truss and bars number.  
height and span of each layer were identical, which is 0.3 m. The cross-section dimension is 0.005 m \(\times\) 0.06 m. The frame contained nine substructures and 36 elements, with each substructure having four units. The finite element model is shown in Figure 10b.  
The experimental frame model damage was achieved by producing a 1 cm deep, 1 mm wide, and 1 cm spacing incision on substructure 2. Under this condition, the stiffness of substructure 2 decreased by 33%, indicating that the damage factor was 0.67. In addition, an incision of 1.5 cm depth, 1 mm width, and 1 cm spacing was made on substructure 9. Its stiffness decreased by 50%, showing that the damage factor was 0.5. During the experimental tests, the load and structural acceleration responses were measured. The required equipment included a modal force hammer, acceleration sensor, and data acquisition instrument.
# Water Pipeline Leakage Detection Based on Coherent \(\varphi\)-OTDR and Deep Learning Technology
## 3 Data Collection and Preprocessing
### Dataset Production  
The data collection platform in this paper simulates a water pipeline leakage environment. The sensing fiber cable is tightly installed to the water pipeline using nylon ribbon, and an opening is created on the water pipe to simulate a leak. To simulate long-distance detection scenarios, the fiber length is 4 km, and the pulse width is set to 100 ns, corresponding to a spatial resolution of 10 m. Figure 5a is the picture simulating the water pipeline and Figure 5b is the picture of a leakage point, which is a fixed-size hole with a diameter of about 5 mm that is located at the back of the water pipe with a distance of a few centimeters from the sensing cable.  
Data from operation both with and without leakage events in the water pipe were collected on the aforementioned experimental platform. The data were then divided into training and testing sets. The sample quantities for the two distinct event categories are shown in Table 1, with each individual sample containing 10,240 data points. Figure 6 illustrates five sets of the original phase signals for the water pipe under both non-leakage and leakage conditions. Notably, even during non-leakage periods, the water pipe exhibits low-amplitude, low-frequency vibrations, which are caused by water flow impacting the pipe walls and other environmental disturbances. In addition, since some leakage signals are quite similar to the case of non-leakage, it is difficult to distinguish leakage events through original phase signals.  
Figure 4: Phase response results at different voltages and frequencies.  
Figure 3: Phase demodulation results of vibration signals at different frequencies: (**a**) 100 Hz; (**b**) 150 Hz; (**c**) 200 Hz; (**d**) 250 Hz. Phase demodulation PSD plots of vibration signals at different frequencies: (**e**) 100 Hz; (**f**) 150 Hz; (**g**) 200 Hz; (**h**) 250 Hz.  
Figure 5: **(a) Simulating water pipeline; (b) leakage point.**  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **No Leakage** & **Leakage** \\ \hline Training set & 13,699 & 1951 \\ Test set & 1574 & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of samples per category.  
Figure 6: Phase signals of different events: (a–e) no leakage; (f–j) leakage.  
To extract as much effective information as possible from the collected data, original phase data were converted into Mel spectrograms. Mel spectrograms are commonly used in audio signal processing as feature representations. They decompose audio signals into energy distributions across different frequencies, providing richer information. Specifically, Mel spectrograms map linear spectra (typically obtained through Fast Fourier Transform) onto the Mel scale, which better aligns with human auditory perception.  
The data were sampled at a rate of 10 KHz, with an FFT window size of 1024 and a frame shift size of 256 and Mel filter number of 40. Figure 7 provides five sets of Mel spectrograms corresponding to both non-leakage and leakage conditions in the water pipe.
## 3 Results
### Modulation of STAT3 Activation  
Considering the well-known key role of the STAT3 cascade in the control of cell migration, cancer cell survival and apoptosis inhibition, especially through the activation by phosphorylation at tyrosine 705, namely pSTAT3(Tyr705) [37; 48], we studied the modulation of this cascade as a possible chemopre mechanism of \(\beta\)-caryophyllene towards CSC. The results of the Western blotting analysis highlighted that pSTAT3(Tyr705) was markedly upregulated by the CSC treatment, with almost a three-fold increase with respect to the control (Figures 17, S5 and S6).  
Figure 16: RTPCR analysis of cell migration factors BIRC5 (**A**), MMP2 (**B**), TPX2 (**C**), and IL-8 (**D**) induced by cigarette smoke condensate (CSC) and \(\beta\)-caryophyllene in MDA-MB-468 cancer cells after 24 h exposure. Data are displayed as mean \(\pm\) SE of at least three independent experiments with at least three technical replicates (\(n=9\)). * \(p<0.05\), ** \(p<0.01\) and *** \(p<0.001\), significant lowering of cell viability with respect to the control (ANOVA + [PERSON]’s multiple comparison post-hoc test). \({}^{\circ\circ\circ}p<0.001\), significant difference respect to CSC (Student’s \(t\)-test).  
Conversely, \(\beta\)-caryophyllene completely suppressed the activation of pSTAT3(Tyr705), restoring its basal levels. These results suggest that the modulation by \(\beta\)-caryophyllene of the pSTAT3(Tyr705) activation induced by CSC can be responsible for the modulation of cell migration and apoptosis and probably for the inhibition of the autophagic fate induced by the natural sesquiterpene. Therefore, it could represent a key mechanism accounting for its chemopre power.
## 1 Introduction
During the blast trials, it was observed that the V pattern had better fragmentation, probably due to in-flight collision between broken rock fragments [18]. Analogously, investigations found that firing patterns can significantly influence and govern both fragmentation and ground vibration results [19]. Soft computing approaches such as self-learning, adaptive recognition, and nonlinear dynamic processing can substantially aid in the resolution of intractable and perplexing geotechnical problems [20; 21; 22; 23; 24; 25; 26]. Many researchers have employed artificial intelligence-based algorithms such as ANN, FIS, GEP, Regression, XG Boost, Random Forest, AMC, and K-NN to solve and predict rock fragmentation and resultant ground vibration [27; 28; 29; 30]. Nevertheless, none of these models have delivered a unique formula for predicting fragmentation and ground vibration using joint angle and blast design parameters, so an attempt has been made by the authors in this direction. Two models--FIS, an artificial intelligence approach, and regression--were developed to forecast rock fragmentation using 415 blast design datasets in an Iranian mine and the research indicated that the FIS model performed well in predicting results [23]. Similarly, various methods showed that models can accurately forecast rock fragmentation based on input factors such as burden, spacing, and explosive quantity, among others [31]. Analogously, the Gene Expression Programming (GEP) model was used to predict Peak Particle Velocity (PPV) in Malaysian mines. A total of 102 datasets of blast design parameters such as burden-to-spacing ratio, stemming, hole-depth, a maximum charge per delay, and powder factor were fed into the trained model, and the GEP produced good R\({}^{2}\) and Random Mean Square Error (RMSE) metrics and predicted well [32]. On 93 blasts of data, algorithms such as SVM, XG Boost, Random Forest, and K-NN were employed, and the results showed that XG Boost predicted the closest PPV value among all of them [33; 34].
## 4 High-Performance Photodetector Based on Graphene
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Absorption** & **Responsivity** & **Bandwidth** & **Size** & **Operation Wavelength Range** \\ \hline InGaAs [68] & 0.68 A/W & 32 GHz & 1 μm & 1260 nm\(-\)1360 nm \\ InP [69] & 0.8 A/W & 40 GHz & 5 μm & 1240 nm\(-\)1650 nm \\ InP [70] & 0.5 A/W & 130 GHz & N. A. & 1310 nm and 1550 nm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the key parameters of the state-of-the-art photodetectors based on different materials. Figure 4: Graphene/silicon photodetectors based on PB effect. (**a**) Plasmonic enhanced graphene photodetector based on bowtie structure. Reprinted with permission from _ACS Photonics_ 2019, 6, 1, 154–161. Copyright 2019 American Chemical Society. (**b**) Coherent optical receiver based on four graphene photodetectors [58]. (**c**) Double-slot graphene photodetector with a high responsivity [59].
## 3 Experimental Technique
### Test Results and Data Analysis
#### 3.4.3 Analysis of the Variation in Temperature at Different Depths along a Vertical Plane  
Taking A, B, and C points as examples, the temperature data of points at different depths along a vertical plane were plotted as shown in Figure 7.  
Figure 5: Temperature curves of hydration (A-E test points).  
Figure 6: Temperature curves at the same depth. (**a**) 50 mm below the top surface; (**b**) 900 mm depth of foundation; (**c**) 50 mm above the bottom surface.  
Figure 7: Temperature curves in the same plane position. (**a**) Position of A points; (**b**) Position of B points; (**c**) Position of C points.  
Along a vertical plane, the temperature of the measuring points was markedly different with depth. Because of a good convection effect, the test points that were located on the surface had the lowest temperature. Due to the heat preservation effect, the temperature of the bottom surface was higher than at the top surface. The central concrete was in a typical scenario of heat of hydration in mass concrete; thus, the temperature here was the highest.