esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
## 1 Introduction
However, the proposed oblique-slip fault was limited to a strike-slip movement with a fault dip angle of 90 degrees. [PERSON] et al. [17; 18] investigated different failure modes of pipelines under the action of oblique reverse faults by means of a three-dimensional multiple nonlinear finite element model and fitted equations for predicting the range of the local buckling failure of buried pipelines. The failure characteristics and failure range of the local buckling of a buried pipeline were systematically analyzed, and the influence of the fault dip angle, displacement, diameter-thickness ratio, burial depth, and internal pressure on the local buckling failure range of the pipeline was discussed in detail. Apart from the numerical studies, [PERSON] et al. [19; 20] and [PERSON] et al. [21] also conducted experimental studies on the impact of strike-slip faults on buried polyethylene pipelines. Based on centrifugal modeling, this study investigated the influence of fault types and strike slip fault angles on the mechanical behavior of pipelines, as well as the effects of the embedment depth and pipe diameter. The present paper extends the work presented in [22], considering buried steel pipelines crossing the fault plane at various angles. Furthermore, this paper examines the mechanical behavior of buried steel pipelines under different fault types, as well as the internal pressure and the wall thickness. In this study, the pipeline-soil interaction model is established under fault action through ABAQUS software, version 6.13., the force and deformation characteristics of the pipeline under fault action are combined, and the strain-based design criterion is selected to analyze the response of the pipeline. The vertical displacement, axial compressive strain, and axial tensile strain of the pipeline under the action of a strike-slip reverse fault are analyzed, and the influence of the fault angle, fault type, internal pressure, and wall thickness on the performance of the pipeline are also studied. The results of this investigation can determine the fault displacement during pipeline failure and provide some reference for pipeline design.
## 2 Material Testing
At temperatures below the glass transition temperature, the material is hard and brittle, whereas, at temperatures above the glass transition temperature, the material is more flexible and rubbery. The DMA tests were carried out using TA Instruments Q800 Dynamic Mechanical Analyzer (TA Instruments, New Castle, DE, USA). The beam samples (60 \(\times\) 10 \(\times\) 4 mm) were clamped at one end (single cantilever mode) and subjected to constant oscillating deformations over a temperature range of \(-\)100 to 100 \({}^{\circ}\)C for four different excitation frequencies: 1, 2, 5, and 10 Hz. The excitation amplitude was set to 20 \(\upmu\)m, and the heating rate to 3 \({}^{\circ}\)C/min. The plots of the storage modulus, loss modulus, and the tangent of the phase angle over a wide range of temperatures for four different excitation frequencies are shown in Figure 2.  
Figure 2: _Cont._  
The glass transition temperature, usually denoted as \(T_{g}\), which is one of the most significant parameters used to characterize mechanical behaviour of polymers, was measured by the peak value of tangent of the phase angle. As indicated in Figure 2, the exact values of \(T_{g}\) were determined for each of the analyzed excitation frequencies. It can be observed that \(T_{g}\) rises with the increasing excitation frequency, which is actually typical for viscoelastic materials due to molecular relaxation. In the glassy region (i.e., at temperatures \(T<T_{g}\)), the analyzed polymer is hard and brittle, so that its storage modulus is at its highest value, while the loss factor is generally low. In the transition region (i.e., at temperatures \(T\cong T_{g}\)), the analyzed polymer softens, as a result of which the material goes through its most rapid rate of change in stiffness and exhibits the highest level of loss factor. In the rubbery region (i.e., at temperatures \(T>T_{g}\)) the analyzed material is flexible, and consequently its stiffness and loss factor are at lower, but still satisfactory levels. The results obtained from the DMA tests confirmed that the dynamic properties of the analyzed polymer are influenced by both temperature changes and the frequency rate of deformation. Nevertheless, as presented in Figure 3, in typical atmospheric temperature range (\(-20\) to \(40\)\({}^{\circ}\)C), and for predominant earthquake frequencies (\(1\div 10\) Hz), its dynamic mechanical properties vary only slightly with changes in temperature and excitation frequency. As a consequence, the mechanical behaviour of the analyzed polymer is more stable and easier to predict. Most importantly, relatively high value of the loss factor confirms its high damping properties, which are crucial for the materials used for seismic isolation bearings. Figure 2: Dynamic mechanical analysis (DMA) results for various excitation frequencies: (**a**) 1 Hz, (**b**) 2 Hz, (**c**) 5 Hz, and (**d**) 10 Hz.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 2 Related Works
### Improving Transmission Performance
The proposedrouting protocol can effectively improve the throughput. Because AODV+ protocol only considered how to connect the destination node in the Internet or infrastructure network, [PERSON] and [PERSON] proposed a Modified AODV+ (M-AODV+) [26] to achieve reliable transmission from the mobile source to the destination in VANETs. This protocol involved three parts: communication channel selection, gateway discovery, and infrastructure to infrastructure communication routing. For load balancing, [PERSON] and [PERSON] presented a node-disjoint AODV (NDj-AODV) [27] routing protocol that identified multiple node-disjoint routes to the destination. The proposed scheme could be used for load balancing, QoS- (Quality of Service) based routing, and more specifically, for performing energy aware routing. Regarding QoS, [PERSON] and [PERSON] proposed a routing protocol: AODV with QoS (QAODV) [28]. In QAODV, two additional fields were added to the message of AODV routing protocol in order to improve the performance in terms of PDR, PLR, and average E2 ED. Many valuable schemes in AODV are proposed to improve the transmission performance, including the energy efficiency, reducing the network congestion, improving the throughput, and load balancing. Just as mentioned above, there are many researches on enhancing the security and improving the performance in AODV. Unfortunately, joint works of the above two aspects are seldom researched. Moreover, we argue that the practical secure-routing-protocol is a tradeoff between security, energy consumption, and transmission performance. Therefore, we will propose a Source Anonymity-based Lightweight Secure AODV Protocol (SAL-SAODV) in the following sections in order to meet the security requirements of a fog-base MANET.
## 2 Design and Modelling  
The new metasurface antenna design is constructed by etching the split ring resonator of a circular disk, as shown in Figure 1b,e. The simple circular disk design and complementary split ring resonator (CSRR) metasurface design are presented in Figure 1a,d. The ground plane is kept the same for both designs, as shown in Figure 1c,f. The ground plane is diffracted. The optimized width of the ground plane is 8 mm. The ring shape is also optimized with an outer ring of 8 mm and an inner ring of 6 mm. The length and width of the substrate are, respectively, 40 and 25 mm. The copper material is used in the resonator and ground plane. The electric length of the proposed design is 0.5\(\lambda\)\(\times\) 0.35\(\lambda\)\(\times\) 0.02\(\lambda\). The substrate is made of FR-4 material with a thickness of 1.6 mm. The design dimensions of the presented work are represented in Table 1.  
Figure 1: Proposed 2-port MIMO antenna. (**a**) Circular disk MIMO antenna design. (**b**) CSRR metamaterial-based MIMO antenna design. (**c**) Ground plane with the defected ground structure. The diameters D = 10 mm, D\({}_{1}\) = 8 mm, and D\({}_{2}\) = 6 mm. Length = 35 mm, width = 25 mm, and DGW = 8 mm. (**d**) Prototype top view of circular disk MIMO antenna. (**e**) Prototype top view of CSRR-based MIMO antenna. (**f**) Bottom view of prototype.  
The primary design aim was to use inexpensive materials in the MIMO antenna's construction. Therefore, the suggested design makes use of FR4 material. This material was chosen because it is easily accessible and has robust physical qualities that can resist the milling process. For the milling operation, high-velocity drill bits were manually scraped away from the exposed copper surface. Compared to the minimal resolution sizes of 0.1 mm and gaps of 0.6 mm necessary to manufacture the antenna, the milling machine's precision was much superior. The milling operation was accomplished in under an hour as the antenna had simple geometries, and the bulk of the board was the ground plane, which did not need etching. Connecting the coaxial connections to the feed points on the antenna structure followed the milling of the MIMO antenna board. Coaxial connectors are the interface via which the antenna may be linked to third-party testing instruments. The antenna's ground plane was bent to accommodate a coaxial line interface for a simple connection. The ground plane surface currents were assured to behave as predicted by the simulation by soldering at many points rather than only along the coaxial wire.  
The electrical equivalent model of the presented design is represented in Figure 2. The capacitance represents the gap between the ring's two terminals, and the structure's wounded shape is represented by the two parallel connected indicators [34].
## 7 Conclusions  
In this research, an experiment involving blasting was carried out using a shock chamber system, along with analysis to assess how well various combinations of STF based stemming materials and plug devices performed. The study resulted in the following findings:  
1. The blockage performance improved when STF stemming material was mixed with sand, showing a 7.5% enhancement compared to using only sand for stemming;
2. The application of a plug device to the STF stemming model further enhanced the blockage performance in the blast hole. Through blasting experiments and numerical analysis, the twist-type plug designed in this study demonstrated the highest blockage effect and frictional resistance in the blast hole, achieving a blockage performance exceeding by 130% that of common sand stemming;
3. STF-based stemming materials are cost-effective and can be easily utilized at blasting sites. These materials lack internal voids and exhibit superior blockage performance in the blast hole. Their compression resistances are significantly better than those of existing unconsolidated or solidified materials. STFs are incompressible fluids that do not undergo volume changes due to external forces; however, they experience a rapid increase in viscosity upon impact. Generally, for open-pit blasting, STF-based stemming materials can be used either in the form of pre-injected packaged products or directly injected into the blast holes. Employing blast stemming in combination with a plug device can lead to enhanced blasting outcomes;
4. STF-based stemming materials have the potential to minimize environmental impact by reducing noise and vibration from the ground, they improve rock fragmentation, and they require fewer explosives in engineering practice. Flexibility and efficiency are enhanced in a variety of blasting scenarios, especially in open-pit mining and tunneling operations, by the capacity to pre-inject STF-based products or inject them straight into blast holes.  
Conceptualization, Y.-H.K. and K.B.; methodology, J.-G.K.; software, Y.-H.K.; validation, K.B., Y.-H.K. and J.-G.K.; formal analysis, Y.-H.K., H.-J.P. and G.K.; investigation, K.B. and H.-J.P.; resources, Y.-H.K. and G.K.; data curation, J.-G.K.; writing--original draft preparation, Y.-H.K. and K.B.; writing--review and editing, K.B. and H.-J.P.; visualization, J.-G.K.; supervision, Y.-H.K. and G.K.; project administration, Y.-H.K. and G.K.; funding acquisition, J.-G.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (Grant Number NRF2022 RII1A2053440).  
Numerical model and mechanical model data are available and can be provided upon request. Most of the data be directly obtained from the Korea Institute of Civil Engineering and Building Technology (KICT) on request.  
The authors declare no conflicts of interest.
# Design and Construction of an LED-Based Excitation Source for Lock-In Thermography
## 3 Experimental Specifications  
### Thermal Characterization  
Infrared thermography is used to estimate the maximum junction temperature of the LEDs, which are the most temperature-sensitive elements of the device. Having operated the lamp continuously for 90 min with \(I_{\text{LED}}=0.9\) A, a thermographic image of the LED board is recorded (Figure 6). The infrared thermography system used is based on a cooled indium antimonide focal plane array snapshot detector to detect radiation between 2.0 um and 5.7 um (Infratec ImageIR 8380S). In full frame mode, a resolution of \(640\times 512\) px is possible. As the optics of the LEDs are assumed to have an emission coefficient larger than 0.9, no emissivity correction is necessary.  
For a proper estimate of the junction temperature, the thermal resistance between junction and primary optic is taken into account. The maximum surface temperature  
Figure 5: Illustration of the different operating modes of the excitation source. The voltage and current values are normalized. The arrows indicate digital commands to change the duty cycle and LED current.  
at point 1 is 82.9\(\,\)\({}^{\circ}\)C, while it is 78.9\(\,\)\({}^{\circ}\)C at point 2. The temperature rise in the junction is assumed to be up to 10% higher than the hottest surface temperature. Under these assumptions, the junction temperatures at points 1 and 2 are estimated to be 91.2\(\,\)\({}^{\circ}\)C and 86.8\(\,\)\({}^{\circ}\)C, respectively. Both values lie well below the specified maximum junction temperature of 145\(\,\)\({}^{\circ}\)C [24].  
The thermal resistance (junction to ambient) is defined as the temperature difference, \(\Delta T\), to ambient temperature divided by the magnitude of the applied power, \(P\), i.e.,  
\[R_{\text{th}}=\frac{\Delta T}{P}\,. \tag{1}\]  
To obtain the thermal resistance, it is necessary to estimate the power step, \(P\), per LED. For the thermographic image, as shown in Figure 6, the LEDs are operated with \(I_{\text{LED}}=0.9\,\)A, the average forward voltage is \(U=3.03\,\)V for each LED. According to the datasheet, the typical emitted optical power at 0.9 A is \(P_{\text{optical}}=1.39\,\)W. Thus, the heating power, \(P_{\text{heat}}\), amounts for each LED to  
\[P_{\text{heat}}=U\cdot I_{\text{LED}}-P_{\text{optical}}=3.03\,\text{V}\cdot 0.9\,\text{A}-1.39\,\text{W}=1.34\,\text{W}\,. \tag{2}\]  
At an ambient temperature of 20\(\,\)\({}^{\circ}\)C, the junction-to-ambient thermal resistances amount to  
\[R_{\text{th,1}}=\frac{71.2\,\text{K}}{1.34\,\text{W}}\approx 53\,\text{K}/ \text{W}\quad\text{and}\quad R_{\text{th,2}}=\frac{66.8\,\text{K}}{1.34\, \text{W}}\approx 50\,\text{K}/\text{W} \tag{3}\]  
for points 1 and 2, respectively.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 2 Research Content and Method
### Current Status of Wastewater Treatment Plants
#### 2.1.3 Pilot Test  
To determine the concentration change by coagulation-sedimentation, we injected the coagulator using Alum at regular intervals of 10, 20, 30, 40, and 50 mg/L. After fixing the pH to 7, we conducted rapid spinning (67 rpm) for one minute, slow spinning (31 rpm) for 10 minutes, and then sedimentation for 30 minutes. The supernatant was then sampled and analyzed. The PFCs removal efficiency by ozone oxidation was determined during an ozone contact test. The ozone contact test was performed by determining the ozone dose by potassium iodide titration and injecting the ozone to the water in the Ozone Demand Flask in rising concentrations of 5, 10, 15, 20, 25, and 30 mg O\({}_{3}\)/L. The volume of the Demand Flask was 1.5 L, and after injecting ozone, the Demand Flask was sufficiently shaken for 20 minutes to maximize the ozone contract efficiency before carrying out the analysis. By measuring the effective chlorine of the hypochlorous acid sodium solution in the inflow water to the treatment plant, the corresponding chlorine dose was calculated and injected. The injection doses of chlorine were 5, 10, 15, and 20 mg/L at regular intervals, based on the guidelines for the sewage treatment facilities. For the activated carbon adsorption of PFCs, we placed clearly washed and dried granular activated carbon into a column with an internal diameter of 20 mm and length of 350 mm. The influent wastewater was injected into this column using a controlled volume pump, and the treated sample was then analyzed. For the test conditions, the empty bed contact time (EBCT) was varied to 5, 10, and 15 minutes.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 4 Conclusions and Future Work  
We have presented and evaluated the transmission scheduling for emerging sparse LEO satellite services suitable for IoT. This is the first published study that addresses the critical uplink availability issue with sparse LEO constellation. A detailed probabilistic energy consumption model was developed used to evaluate our on-line learning scheme for predicting transmission periods. Our learning proposal is inexpensive computationally, learns in small increments and in a modest number of training epochs, and is interpretable, unlike most modern machine learning approaches. For three common scenarios, we have observed up to 20-fold reduction in power and battery requirements of the transmissions attributable to this learning approach. Since the whole scheme is suitable for the smallest embedded microcontrollers, we have demonstrated its implementation on Raspberry Pi Pico that manages to pack all sensed data within the least expensive dataplan.  
While the intended application is the water-level monitoring in remote locations, the proposed scheme is practical for other IoT applications, as it incurs modest computing  
Figure 16: Average modem power versus discount factor \(\lambda\) for bucketed noise and moderate preference model 2, using \(e_{pass}=0.5\), \(r_{attempt}=\frac{1}{24}\) hr\({}^{-1}\), and \(t_{pass}=25\) min.  
Figure 15: Average time to attempt transmission versus discount factor \(\lambda\) for bucketed noise and moderate preference model 2.  
costs. The results demonstrate the ability of the proposal to facilitate energy-efficient data collection over prolonged periods of time.  
The whole analysis was done by simulations to reduce the burden in cost, time and also to combat location-dependence, since RF noise in the city differs from the intended remote locations; while we demonstrate the ability of our proposed scheme to adapt appropriately, obtain interpretable learning and explore the impact of variables on the performance of the algorithm, they are nonetheless simulated results.  
Complete code and data for modeling, learning and energy consumption evaluation is made available by GitHub (as reported below in \"Data Availability\" statement). The code and data can be readily used for further exploration of sparse LEO connectivity for IoT in general.
## 2 PSs Siting and Timing: Technical Background and Problem Formulation
### Geographic Information System (GIS)  
GISs are used for working with maps and geographic information. They are used to create and manage maps, compile geographic data, analyze mapped information, and share and discover geographic information, and data are positively managed in a database. GIS software provides an infrastructure for making maps and geographic information available throughout an organization, across a community, or on the Web. Usually, several extensions can also be added separately to increase the functionality of GIS software. Several extensions provide added functionality, including 3D Analyst, Spatial Analyst, Network Analyst, Survey Analyst, Tracking Analyst, and Geostatistical Analyst. GISs have emerged as an essential tool for urban and resource planning and management in the last decades. Their capacity to store, retrieve, analyze, model, and map large areas with huge volumes of spatial data has led to an extraordinary proliferation of applications. Geographic information systems are now used for land use planning [20], utility management, ecosystem modeling, landscape assessment and planning, transportation and infrastructure planning [21], market analysis, visual impact analysis [22], facilities management, tax assessment, real estate analysis, and many other applications. For example, electric utilities can use GIS software to manage assets and outages, serve customers, facilitate field operations, and create situational awareness. They can benefit from location-based planning for accounting, network operations, and countless more departments where the power of location can advance the network.
## 2 Modeling of Subsystem
### Probability Density Function Fitting
#### 2.3.2 Weibull Distribution  
The accumulative distribution function of the two-parameter Weibull distribution is shown in Equation (9) and has two parameters, \(m\) and \(\eta\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group Number** & \(\mathbf{t_{i}}\)- & \(\mathbf{t_{i}}\) & \(\mathbf{t_{i+}}\) & \(\mathbf{n_{i}}\) & \(\mathbf{f_{i}}\) \\ \hline
1 & 0 & \(t_{\text{m}}/2k\) & \(t_{\text{m}}/k\) & \(n_{1}\) & \(f_{1}\) \\
2 & \(t_{\text{m}}/k\) & \(3t_{\text{m}}/2k\) & \(2t_{\text{m}}/k\) & \(n_{2}\) & \(f_{2}\) \\... &... &... &... &... &... \\ k & (\(k\)-1)-\(t_{\text{m}}/k\) & (\(2k\)-1)-\(t_{\text{m}}/2k\) & \(t_{\text{m}}\) & \(t_{\text{k}}\) & \(f_{\text{k}}\) \\ \hline \hline \end{tabular} Note: \(t_{\text{m}}\): the maximal time between failures; \(t_{i}\): the left end of the \(i\)th time interval; \(t_{i}\): the right end of the \(i\)th time interval; \(t_{i}\): the median of the \(i\)th time interval; \(n_{i}\): the fault number of the \(i\)th time interval; \(f_{i}\): the probability density of the \(i\)th time interval, which can be calculated by Equation (6). In Equation (6), \(\Delta t\) is the time interval length.  
\end{table}
Table 1: Reliability data acquiring and grouping.  
Figure 1: Typical probability density curves.  
\[F(t_{i})=1-\exp[-(t_{i}/\eta)^{m}] \tag{9}\]  
Taking the logarithm twice on both sides of Equation (9) and supposing \(y=\ln\ln(1/(1-F(t_{i})))\) and \(x=\ln t_{i}\), the linear equation can be created. If the sample data meet the Weibull distribution, \(y=\ln\ln(1/(1-F(t_{i})))\) and \(\ln t_{i}\) are linear and the observed value of parameters \(m\) and \(\eta\) can be calculated by Equation (10).  
\[\left\{\begin{array}{c}\hat{m}=b\\ \hat{\eta}=\exp\bigl{(}-\frac{g}{g}\bigr{)}\end{array}\right. \tag{10}\]