esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Responses of a Cable-Stayed Bridge with Consideration of Uniform Temperature Load
## 4 Thermal and Earthquake Loads  
It has been noticed that load combination does not consider the concurrent happening of temperature and earthquake loads in current bridge specifications. However, uniform temperature has significant effects on the static and dynamic responses of long-span bridges. Therefore, in order to take uniform temperature into account, thermal load is applied to the bridge before conducting the seismic analysis of the bridge. A bridge is completed in a certain period of a year, thus it will experience various temperature changes during its lifetime. These temperature changes will influence the response of the bridge. In this paper, the bridge is subjected to temperature changes \(\Delta T\) of \(\pm 20\)\({}^{\circ}\)C and \(\pm 35\)\({}^{\circ}\)C, based on the construction temperature and actual temperature occurring in the bridge site.  
The dynamic time history analysis is adopted for the earthquake load. In this study, three scaled earthquake records taken from the PEER ground motion database are used as shown in Table 2. The entire time history of earthquake records has been scaled to match the design response spectrum of Chinese criteria [32]. The scaling was done based on the mean squared error (MSE) of the difference between the spectral accelerations of the record and the target spectrum with period range of interest. As shown in Figure 3, three scaled records with lower MSEs in the period range from \(0.2T\) to \(T\) (\(T=5.4\) s) are selected. It is noted that uniform temperature has little effect on the transverse responses of the prototype bridge. Therefore, only the longitudinal and vertical components of the selected records are input simultaneously when performing nonlinear time history analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Record No.** & **Name** & **Time** & **Station** & **Magnitude** & **Scaling Factor** \\ \hline
1 & 6 & Imperial Valley & 1940 & EI Centro & 6.95 & 1.07 \\
2 & 15 & Kern County & 1952 & Taft Lincoln & 7.36 & 1.73 \\
3 & 68 & San Fernando & 1971 & LA-Holly wood & 6.61 & 1.38 \\ \hline \end{tabular}
\end{table}
Table 2: Earthquake records used in time history analysis.  
Figure 3: Scaling SRSS spectrum of the earthquake records.
# Characterization of Magnetic-Silica Magnetic Fluids by Laser Scattering
## 2 Materials and Methods
### Assessment of Physical Characteristics of Magnetic Fluid  
The study of the granulometric composition of magnetic fluids was performed with scanning electron microscopy (SEM) and transmission electron microscopy (TEM). JSM-5800 (JEOL Corp., Tokyo, Japan) and JEM-1400 (JEOL Corp., Tokyo, Japan) microscopes were used, respectively. The phase composition of dried magnetic fluid was analyzed with X-ray diffraction (XRD) by using a diffractometer D2 Phaser (Bruker, Billerica, Massachusetts, USA) and with reflection high-energy electron diffraction (RHEED) by using an electronograph EMR-102 (SELMI JSC, Sumy, Ukraine) with an automated registration system and software developed at Saint Petersburg Electrotechnical University \"LETI\" at an accelerating voltage of 75 kV. The software package for radiograph analysis PDXL-2 (Rigaku, Tokyo, Japan) and X-ray diffractogram database PDF-2 (International Center for Diffraction Data, 2011) were used for spectra processing. The magnetic properties were measured with vibrating sample magnetometry (VSM) by using a 7400-S Series instrument (LakeShore Cryotronics, Inc., Westerville, Ohio, USA) at room temperature. A comparative analysis of the particle size distribution was conducted with the method of dynamic light scattering (DLS) with the help of a Photocor Mini device (Photocor, Ltd., Moscow, Russia). To study sizes and shapes of individual nanoparticles, a method based on the classical dynamic light scattering, depolarized dynamic light scattering (DDLS), was proposed.
## 4 Optimization Scheduling Model
### Constraints
#### 4.2.2 Procurement Constraints  
Taking into account the system's purchasing capacity for electricity, gas, and gray hydrogen, as well as safety considerations, the procurement constraints are as follows:  
\[\left\{\begin{array}{l}0\leq p^{buy}(t)\leq P_{\rm buymax}\\ 0\leq G^{buy}(t)\leq G_{\rm buymax}\\ 0\leq Q^{buy}(t)\leq Q_{\rm buymax}\\ \end{array}\right. \tag{20}\]  
where \(P_{\rm buymax}\), \(G_{\rm buymax}\), \(Q_{\rm buymax}\) represent the upper limits of purchased electricity, gas, and hydrogen at time \(t\), respectively.  
Equipment Ramp-up Constraint.  
To ensure the safety of the equipment and the smooth operation of the system, the following limitations are imposed on the Ramp-up capability of the equipment:  
\[\left\{\begin{array}{l}P_{i}^{\rm min}\leq P_{i}(t)\leq P_{i}^{\rm max}\\ \Delta P_{i}^{d}\leq\Delta P_{i}(t)-\Delta P_{i}(t-1)\leq\Delta P_{i}^{u}\\ \end{array}\right. \tag{21}\]  
where \(i\) represents different energy-consuming devices, \(P_{i}^{\rm min}\) and \(P_{i}^{\rm max}\) represent the lower and upper limits of device output, \(\Delta P_{i}^{u}\) and \(\Delta P_{i}^{d}\) represent the climbing upper and lower limits of device output, respectively.  
Energy Storage Constraint.  
The energy storage devices in this study encompass electricity, heat, cold, and hydrogen. Their energy charging and discharging processes are analogous. The hydrogen storage device details are provided following the Section 2.1.2, while other energy storage devices are uniformly represented as follows:  
\[\left\{\begin{array}{l}S_{\rm i}^{\rm min}\leq S_{i}(t)\leq S_{\rm i}^{\rm max }\\ 0\leq S_{\rm i}^{\rm c}(t)\leq S_{\rm i}^{\rm c,max}X_{cha}\\ 0\leq S_{\rm i}^{\rm d}(t)\leq S_{\rm i}^{\rm d,max}X_{dis}\\ 0<X_{\rm cha}+X_{\rm dis}<1\\ S_{i}(0)=S_{i}(24)\\ \end{array}\right. \tag{22}\]  
where \(i=1,2,3\) represents energy storage, heating, and cooling devices, respectively; \(S_{i}(t)\) represents the energy storage capacity of the energy storage device at \(t\) time; \(S_{\rm i}^{\rm c,max}\) and \(S_{\rm i}^{\rm d,max}\) represent the maximum charging and discharging power of the energy storage device, respectively; \(X_{\rm cha}\) and \(X_{\rm dis}\) represents the charging and discharging state of the energy storage; \(S_{i}(0)\) and \(S_{i}(24)\) represent the initial and final energy storage states of the energy storage device within one scheduling cycle, respectively.
## 4 Conclusions  
The present study represents a proof-of-concept study to investigate the possibility of coupling NIR spectroscopy and chemometric classifiers with the aim of detecting adulterated rice samples. In order to achieve this goal, two different strategies were exploited; one, based on a discriminant classifier (PLS-DA), and one employing a class-modelling technique (SIMCA). The two approaches provided different results; in particular, SIMCA appeared unable to solve the investigated problem. On the other hand, PLS-DA analysis is a suitable approach for the purpose of the work. In fact, while this approach provided not completely satisfactory results in discriminating authentic and adulterated samples, when considering both 5% and 10% counterfeits (with a total correct classification rate of 63.3%), on the other hand, when the analysis was restricted to the 10%-adulterated samples only, a very good accuracy was achieved. In fact, 82.6% of the authentic samples and 82.4% of the adulterated objects were correctly classified (corresponding to 4 authentic and 3 adulterated misclassified samples). These results indicate that the high within-class variability (due to the different origins of the samples in terms of factory, region, and season) can have an impact on the possibility of detecting low levels of adulteration; at the same time, they also suggest that the proposed approach could be useful for detecting samples adulterated at 10% or more.  
In conclusion, we assert that the present preliminary study demonstrates that the combination of NIR spectroscopy and PLS-DA can represent an effective, rapid and non-destructive tool for the determination of adulteration in jasmine rice.  
Conceptualization, Q.C.N. and A.B.; methodology, Q.C.N. and A.B.; software, F.M. and A.B.; validation, D.L.N.D., Q.C.N., F.M. and A.B.; formal analysis, Q.C.N., F.M. and A.B.; investigation, D.L.N.D. and Q.C.N.; resources, D.L.N.D.; data curation, A.B.; writing--original draft preparation, Q.C.N. and A.B.; writing--review and editing, Q.C.N., F.M. and A.B.; visualization, A.B.; supervision, D.L.N.D. and Q.C.N.; project administration, D.L.N.D.; funding acquisition, D.L.N.D. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Vietnam National University Ho Chi Minh City under grant number C2019-20-23. We would like to thank Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for the support of time and facilities for this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Preprocessing** & **PCs** & **Sensitivity (\%)** & **Specificity (\%) vs. 5\% + 10\% Adulterated** & **Efficiency (\%)** \\ \hline MC & 4 & 98.0 & 4.4 & 20.7 \\ SNV (+MC) & 6 & 95.9 & 5.5 & 23.0 \\
1 st derivative (+MC) & 5 & 93.9 & 6.6 & 24.9 \\
2 nd derivative (+MC) & 8 & 98.0 & 11.0 & 32.8 \\ \hline \end{tabular}
\end{table}
Table 3: Soft independent modeling of class analogies (SIMCA) modeling of the authentic class (49 training samples): results of cross-validation for model selection.
# Experimental Study on Permeability Evolution of Sandstone during Triaxial Compression Damage
###### Abstract  
In order to investigate the mechanical properties and permeability characteristics of sandstone during damage evolution under hydromechanical condition, a series of coupled hydromechanical triaxial tests on sandstone specimens were conducted based on the Rock Top 50 HT full-stress multi-field coupling triaxial test system. Variations in permeability as a function of confining pressure, seepage pressure gradient, and volumetric strain during damage evolution were obtained. The results show that: (1) When the confining pressure is constant and the specimen is gradually changed from a dry to a saturated state, the failure mode of sandstone changes from shear failure to single-slope shear failure. (2) There are four distinctive stages in the permeability evolution of sandstone: gradual decrease, steady development, gradual increase, and rapid growth. These stages correspond to the complete stress-strain curve under the respective working conditions. (3) Employing the Weibull distribution formula, this study investigates the evolution of fracture damage under varying working conditions and determines the permeability evolution relationships associated with damage variables. This exploration reveals an intrinsic link between permeability and damage variables. These findings enhance our understanding of the interplay between stress, deformation, permeability, and damage evolution in seepage-stress coupled sandstone. The results contribute valuable insights to the field of rock mechanics and hold implications for diverse geotechnical and engineering applications.  
rock mechanics; permeability; triaxial test; damage evolution
## 3 Bottom up Meets Top down-The Emerging Use of Google Earth by Grass Root Initiatives
### Bottom-up Example: Quarry and Barge Loading Facility on Texada Island
#### 3.1.4 Discussion  
The Texada Island example illustrates the application of \"Interoperability and Mashups\", where Google Earth allows layers with disparate themes and from multiple sources to be easily integrated and visually overlaid showing functional relationships [2]. It also illustrates the benefit of \"representativeness\" where virtual globes allow \"freedom to view places from any angle or height and from any number of views [...] instead of the more conventional limited selection of static views determined by the creator of the visualizations\" [1]. While formal user-response research to the visualization was not part of this example, anecdotal evidence indicates that government regulators had a strong affective (emotional) response to the visualization, expressed as surprise upon seeing the proximity of the proposed barge loading facility to the sensitive ecological features [39]. While this could be hypothesized as an example of \"cognitive dissonance\" in public decision-making [41], the lack of a formal evaluation precludes such suppositions. The Texada Island example thus points to a need for further research on actual decision-making outcomes resulting from the application of landscape visualization in virtual globes. Such research would ideally be inter-disciplinary as it crosses the boundaries of geospatial, social and political sciences.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 4 Collision Avoidance System
### Evaluation Function
#### 4.4.1 Attainability Subfunction \(f_{1}(X_{pi},S_{i})\)  
The first demand of the CAS is to reach the target point. If the course \(\psi\) keeps pointing to the target point, which is called the navigation angle, the vessel can reach the target point along the shortest path. Hence, the difference angle \(\theta_{pi}\) between the predicted course \(\psi_{pi}\) and the navigation angle can be used to evaluate the attainability of \(X_{pi}\), as shown in Figure 10.  
The attainability subfunction is  
\[f_{1}(X_{pi},S_{i})=\pi-\theta_{pi} \tag{15}\]
## 5 Conclusions and Future Research  
The uncertain events involved in projects make it challenging to achieve the project objectives without performing a risk and opportunity analysis and determining the contingency reserve. In this paper, type-1 fuzzy arithmetic, interval type-2 fuzzy sets, and the principle of justifiable granularity are combined to improve the project contingency reserve determination. The new method, called interval type-2 fuzzy risk analysis model (IT2 FRAM), is introduced in order to develop, optimize, and aggregate the membership functions for the probability and impact of risk and opportunity linguistic terms (e.g., _very low_). IT2 FRAM is an extension of the fuzzy arithmetic-based risk analysis model proposed by [PERSON] et al. [1], which addresses the limitations of traditional techniques of project contingency determination methods. Interval type-2 fuzzy sets are employed to capture more uncertainties, provide better knowledge representation, and consider several experts' opinions. The principle of justifiable granularity is employed to optimize interval type-2 fuzzy sets by maximizing the performance index of two criteria: coverage and specificity. IT2 FRAM also provides an alternative to other methods for the elicitation of membership functions, such as fuzzy clustering and the analytical hierarchy process (AHP), which cannot be effectively applied to form the membership functions of risk analysis linguistic terms. A software tool, Fuzzy Risk Analyzer(r) (FRA(r)), was introduced to illustrate the implementation of IT2 FRAM using a hypothetical case study.  
The contributions of this paper are in addressing the following challenges associated with previous methods of determining project contingency reserve: (1) considering the opinions of several SMEs to develop the membership functions of linguistic terms for the probability and impact of events, (2) decreasing the effect of outlier opinions in developing the membership functions of linguistic terms, and (3) aggregating non-linear membership functions into trapezoidal membership functions.  
Future research will focus on the validation of IT2 FRAM using real project data and comparing the results with traditional contingency determination methods. Since it is assumed in the proposed method that WBS components and risk and opportunity events are independent, IT2 FRAM will be extended to consider the interdependencies among them.  
Conceptualization, S.H.F.; methodology, S.H.F. and V.S.; software, S.H.F.; writing--original draft preparation, S.H.F. and V.S.; writing--review and editing, S.H.F., V.S., and A.R.F.; supervision, A.R.F.; project administration, A.R.F.; funding acquisition, A.R.F. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Industrial Research Chair in Strategic Construction Modeling and Delivery (NSERC IRCPJ 428226-15), which is held by [PERSON]. The authors gratefully acknowledge the financial support provided by industry partners and NSERC through the Chair.  
The authors declare no conflict of interest.
## 2 Principle
The noise sources of the heterodyne coherent angular measurement system primarily comprise the thermal noise \(Pht\) generated by the detector device, the shot noise \(P_{sh}\) generated by the local oscillation laser beam, and the laser relative intensity noise \(P_{RIN}\) generated by the laser. The power level of each noise can be expressed as Equation (20). In this equation, \(k\) denotes the Boltzmann constant, \(T\) denotes the Kelvin temperature, \(B\) denotes the detection bandwidth, \(R_{L}\) denotes the load resistor resistance, \(\
u\) denotes the optical frequency, and \(R\) denotes the laser suppression ratio parameter. \[\left\{ \begin{aligned} P_{th}&=\frac{\Phi KTB}{R_{L} }\\ P_{sh}&=\frac{2\Phi_{d}^{2}EP_{LO}}{h}\\ P_{RIN}&=R\left(\frac{\Phi\epsilon}{h\
u}\right)^{2}B(P_{ LO})^{2}\end{aligned} \right. \tag{20}\]Combined with the beat frequency signal power expression in Equation (5), the SNR is obtained to have the following expression [24]. \[SNR=\frac{2\left(\frac{\xi R_{0}}{Hv}\right)^{2}P_{LO}P_{S}}{P_{th}+P_{sh}+P_{RIN}}= \frac{\xi P_{S}}{Bh\
u}\left[1+\frac{2 kThv}{\xi q_{e}^{2}P_{LO}R_{L}}+\frac{ \xi RP_{LO}}{2h\
u}\right]^{-1} \tag{21}\]  
Combined with the weak light detection background, when the signal laser beam power \(P_{S}\) is 20 nW, the signal-to-noise ratio \(SNR\) with the local oscillation laser beam power \(P_{LO}\) variation graph is as shown in Figure 2. When the \(P_{LO}\) is 10 mW, the \(SNR\) attains its optimal value. In addition, the most suitable \(P_{LO}\) needs to be further selected in combination with the device's performance to ensure optimal functionality within the actual system.
## 5 Clustering Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Population** & **Hospital** & **Restaurant** & **Residential** & **Working** & **Hotel** & **Shopping** & **School** & **Bank** & **Bus Stop** \\ \hline Moran’s 11  & 0.7638 & 0.3552 & 0.4505 & 0.4968 & 0.3120 & 0.5617 & 0.3529 & 0.4091 & 0.4898 & 0.4373 \\ z-value & 13.7708 & 4.5965 & 6.4550 & 7.6702 & 4.0048 & 9.1815 & 4.3148 & 6.6139 & 7.2004 & 6.6956 \\ p-value & 0.0010 & 0.0010 & 0.0010 & 0.0010 & 0.0020 & 0.0010 & 0.0020 & 0.0010 & 0.0010 & 0.0010 \\ \hline Adjusted Moran’s 12  & 0.4606 & 0.3440 & 0.4021 & 0.3840 & 0.3356 & 0.4665 & 0.2821 & 0.4103 & 0.4637 & 0.3752 \\ z-value & 6.6114 & 4.4703 & 5.5677 & 4.8051 & 4.3744 & 7.4033 & 2.9959 & 7.3466 & 6.9072 & 4.9410 \\ p-value & 0.0010 & 0.0030 & 0.0010 & 0.0010 & 0.0010 & 0.0010 & 0.0050 & 0.0010 & 0.0010 & 0.0010 \\ \hline Coefficient 3  & 0.1518 & 0.4646 & \(-\)0.3457 & \(-\)0.3882 & \(-\)0.1068 & 0.3972 & \(-\)0.2014 & \(-\)0.4447 & 0.1632 & 0.3661 \\ z-value & 1.3756 & 3.0234 & \(-\)1.7097 & \(-\)2.1938 & \(-\)0.6245 & 2.2191 & \(-\)0.9108 & \(-\)0.3203 & 1.0100 & 2.0907 \\ p-value & 0.1689 & 0.0025 & 0.0873 & 0.0283 & 0.5323 & 0.0265 & 0.3624 & 0.0025 & 0.3125 & 0.0366 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Spatial autocorrelation analysis results of environmental variables.
## 3 Data Processing  
### TLS--Processing of Measurements  
Laser scans were registered in RealWorks v12.0 software by Trimble using the \"Auto-Register using Planes\" mode, which uses the ICP algorithm for automatic detection of plane surfaces in the overlap of individual scans and then searches for identical surfaces and calculates their outliers after transformation. After registration, a total mean error of 2.05 mm was achieved. The total mean georeferencing error of the 7 GCPs reached a value of 7.2 mm. Further processing in the Trimble RealWorks system consisted of segmentation, cleaning, and sampling (in steps of 2 mm), to eliminate unnecessary points and reduce the total volume of data to approximately 200.8 million points, which was then cropped to the size of 58 million points used in this study (Figure 8).  
Figure 8: Point cloud from TLS colored using the reflection intensity (**left**), and the resulting mesh from Metashape (**right**).
## 3 Results  
Table 2 shows the participants' task duration as well as the mean and max core temperature as recorded from the ICTS and other wearable sensors. The physical task duration varied from 8 to 47 min and the average mean temperatures recorded by the ICTS, SS, and Zephyr were 37.36 \({}^{\circ}\)C, 37.60 \({}^{\circ}\)C, and 37.30 \({}^{\circ}\)C, respectively. Additionally, the average max temperatures recorded by the ICTS, SS, and Zephyr were 37.39 \({}^{\circ}\)C, 37.74 \({}^{\circ}\)C, and 37.50 \({}^{\circ}\)C, respectively.  
Table 3 is an excerpt from Table 2. It shows the number of participants that were within the clinically acceptable limits and those that were above the limits for the mean and maximum temperature recorded by the SS and Zephyr. Table 4 shows the results from the Bland Altman plots. The results of the comparison between these devices are presented in the following subsections.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**ID** & **Intensity** & **BMI** & \begin{tabular}{c} **Avoid** \\ **Heat** \\ \end{tabular} & \begin{tabular}{c} **Duration** \\ **(mins)** \\ \end{tabular} & \begin{tabular}{c} **Mean\_ICTS** \\ \end{tabular} & \begin{tabular}{c} **Max\_ICTS** \\ \end{tabular} & \begin{tabular}{c} **Mean\_SS** \\ \end{tabular} & \begin{tabular}{c} **Max\_SS** \\ \end{tabular} & \
\begin{tabular}{c} **Mean\_Zephyr** \\ \end{tabular} &
\begin{tabular}{c} **Max\_Zephyr** \\ \end{tabular} \\ \hline
1 & Vigorous & Healthy & sometimes & 37 & 37.13 & 37.39 & 37.43 & 37.74 & 37.50 \\
2 & moderate & Healthy & No & 47 & 37.19 & 37.53 & 37.55 & 38.03 & 37.35 & 37.80 \\
3 & moderate & Healthy & no & 14 & 37.28 & 37.33 & 37.74 & 38.22 & 37.39 & 37.50 \\
4 & Low & overweight & sometimes & 16 & 38.06 & 39.63 & 38.16 & 38.59 & 37.31 & 37.70 \\
5 & moderate & underweight & no & 28 & 38.02 & 38.07 & 37.58 & 38.14 & 37.43 & 37.90 \\
6 & Vigorous & overweight & sometimes & 9 & 36.60 & 36.78 & 37.41 & 37.68 & 37.12 & 37.20 \\
7 & Low & Healthy & sometimes & 23 & 37.57 & 37.70 & 37.42 & 37.78 & 37.29 & 37.50 \\
8 & Vigorous & Healthy & sometimes & 8 & 37.51 & 37.58 & 37.68 & 37.88 & 37.22 & 37.22 \\
9 & Low & underweight & sometimes & 43 & 37.14 & 37.26 & 37.37 & 37.58 & 37.24 & 37.40 \\
10 & Low & overweight & sometimes & 10 & 37.12 & 37.19 & 37.68 & 38.07 & 37.26 & 37.40 \\  & & Average & 37.36 & 37.39 & 37.60 & 37.74 & 37.30 & 37.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Duration, mean, and max CT as collected from the CT devices.