esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Exact Analytical Solutions
### Temperature Distribution \(\theta(\eta)\)  
Using Equation (17) and applying a new variable \(\varepsilon=-e^{-\alpha\eta}\) in Equation (11),  
\[\varepsilon\theta_{\varepsilon\varepsilon}(\varepsilon)+(p-q\varepsilon) \theta_{\varepsilon}(\varepsilon)+2q\theta(\varepsilon)=0 \tag{20}\]  
with BCs as  
\[\theta(0)=0\,\ \theta(-1)=1, \tag{21}\]  
where \(p=1-\frac{n}{\alpha}\Big{[}S+\frac{2}{\alpha^{2}}(1-\phi_{1})^{2.5}(1-\phi_{2 })^{2.5}\Big{]}\) and \(q=\frac{2n}{\alpha^{3}}(1-\phi_{1})^{2.5}(1-\phi_{2})^{2.5}\), where \(n=\frac{C_{4}}{\left(\frac{\
abla S}{\
abla T}+N_{R}\right)}\).  
To solve Equation (20), we deploy the Laplace transformation to obtain  
\[S(q-S)\Theta_{S}(S)+[3q+S(p-2)]\Theta(S)=0 \tag{22}\]  
Here, \(\Theta(S)=L[\theta(\varepsilon)]\). Integrating Equation (22) gives  
\[\Theta(S)=\frac{C(S-q)^{(p+1)}}{S^{3}} \tag{23}\]  
In order to obtain the solution of Equation (20), we apply the inverse Laplace transformation and use the convolution property to acquire  
\[\theta(t)=\frac{C}{2\Gamma[-1-p]}\int\limits_{0}^{t}\frac{(t-w)^{2}}{w^{p+2}} exp(qw)dw,\ \text{here}\ p<-1 \tag{24}\]  
where \(C\) is integrating constant can be determined by using the BCs \(\theta(-1)=1\) in Equation (24) to obtain,  
\[C=\frac{2\Gamma[-1-p]}{\int\limits_{0}^{-1}\frac{(1+w)^{2}}{w^{p+2}}exp(qw)dw} \tag{25}\]Therefore, Equation (24) becomes  
\[\theta(\varepsilon)=-\frac{\int\limits_{0}^{\varepsilon}\frac{(\varepsilon-w)^{2} }{w^{p+2}}exp(qw)dw}{\int\limits_{-1}^{0}\frac{(1+w)^{2}}{w^{p+2}}exp(qw)dw} \tag{26}\]  
Operating the integration of Equation (26) gives the final expression for \(\theta(t)\)  
\[\theta(\varepsilon)=\frac{q^{2}\varepsilon^{2}\Gamma[-p-1,0,-q\varepsilon]+2q \varepsilon\,\Gamma[-p,0,-q\varepsilon]+\Gamma[-p+1,0,-q\varepsilon]}{q^{2} \Gamma[-p-1,0,q]-2q\Gamma[-p,0,q]+\Gamma[-p+1,0,q]} \tag{27}\]  
In terms of the similarity variable \(\eta\), Equation (27) becomes:  
\[\theta(\eta)=\frac{q^{2}\varepsilon^{-2\alpha\eta}\,\Gamma[-p-1,0,qe^{-\alpha \eta}]-2 qe^{-\alpha\eta}\,\Gamma[-p,0,qe^{-\alpha\eta}]+\Gamma[-p+1,0,qe^{- \alpha\eta}]}{q^{2}\Gamma[-p-1,0,q]-2q\Gamma[-p,0,q]+\Gamma[-p+1,0,q]} \tag{28}\]  
Differentiating Equation (28), we obtain  
\[\theta_{\eta}(0)=\frac{2\alpha q\{\Gamma[-p,0,q]-q\Gamma[-p-1,0,q]\}}{q^{2} \Gamma[-p-1,0,q]-2q\Gamma[-p,0,q]+\Gamma[-p+1,0,q]} \tag{29}\]  
Therefore, from Equation (16,) Nusselt number becomes  
\[Nu_{x}=-\Bigg{(}\frac{k_{Inf}}{k_{f}}-k_{Inf}\text{Pr}N_{R}\Bigg{)}\xi_{1}x \frac{2\alpha q\{\Gamma[-p,0,q]-q\Gamma[-p-1,0,q]\}}{q^{2}\Gamma[-p-1,0,q]-2q \Gamma[-p,0,q]+\Gamma[-p+1,0,q]} \tag{30}\]  
In the next section, we analyze these results.
# An Approximation Algorithm for a Variant of Dominating Set Problem
## 3 Preliminaries  
Firstly, we introduce some notations. Given a graph \(G=(V,E)\), for the sake of discussion, we assume that \(V=\{v_{1},v_{2},\ldots,v_{n}\}\). For a vertex \(v_{i}\in V\), let \(N(v_{i})=\{v_{j}\in V|v_{i}v_{j}\in E\}\) denote the open neighborhood of \(v_{i}\). Denote the degree of \(v_{i}\) in graph by \(\delta(v_{i})\), which is the number of vertices in the neighborhood \(N(v_{i})\), i.e., \(\delta(v_{i})=|N(v_{i})|\). Let \(\Delta\) denote the maximum degree of all vertices in \(G\). Denote the maximum degree of \(v_{i}\) in \(N(v_{i})\) by \(\delta^{(1)}(v_{i}):=\max\limits_{v_{j}\in N(v_{i})}\delta(v_{j})\), and the maximum degree of \(v_{i}\) in the second neighborhood by \(\delta^{(2)}(v_{i}):=\max\limits_{v_{j}\in N(v_{i})}\delta^{(1)}(v_{j})\).  
Secondly, we give the linear programming relaxation for the total dominating set problem. Given an undirected graph \(G=(V,E)\), \(T\subseteq V\) is a total dominating set of \(G\) if each vertex \(v\in V\) satisfies \(N(v_{i})\bigcap T\
eq\emptyset\). For each vertex \(v_{i}\in V\), we assign a corresponding binary variable \(x_{i}\). If \(x_{i}\) is set to 1 if and only if \(v_{i}\) is a member of the total dominating set \(T\), i.e., \(v_{i}\in T\). Hence, \(T\) is called a total dominating set of \(G\) if, and only if, it satisfies \(\sum\limits_{v_{j}\in N(v_{i})}x_{j}\geq 1\) for every vertex \(v_{i}\in V\). Denote the adjacency matrix for \(G\) by \(N\). The MTDS problem can be formulated as the following integer linear programming (\(ILP_{MTDS}\)):  
\[\begin{array}{ll}\textbf{minimize}&\sum\limits_{i=1}^{n}x_{i}\\ \textbf{s.t.}&N\cdot\overline{x}\geq\overline{1}\\ &\overline{x}\in\{0,1\}^{n},\end{array}\]  
where the first constraint indicates that if the vertex \(v_{i}\) satisfies \(\sum\limits_{v_{j}\in N(v_{i})}x_{j}\geq 1\), then the vertex \(v_{i}\) is a member of the total dominating set.  
By relaxing the constraints, we can obtain the linear programming relaxation (\(LP_{MTDS}\)) of \(ILP_{MTDS}\):  
\[\begin{array}{ll}\textbf{minimize}&\sum\limits_{i=1}^{n}x_{i}\\ \textbf{s.t.}&N\cdot\overline{x}\geq\overline{1}\\ &\overline{x}\geq\overline{0}.\end{array}\]  
The dual programming (\(LDP_{MTDS}\)) of \(LP_{MTDS}\) is  
\[\begin{array}{ll}\textbf{maximize}&\sum\limits_{i=1}^{n}y_{i}\\ \textbf{s.t.}&N\cdot\overline{y}\leq\overline{1}\\ &\overline{y}\geq\overline{0},\end{array}\]where the first constraint indicates that if we introduce a positive value \(y_{i}\) for every vertex \(v_{i}\), then \(\sum y_{i}\) of all vertices in \(N(v_{i})\) of every vertex \(v_{i}\) is at most 1. This means that the sum of the corresponding \(x_{i}\) in \(N(v_{i})\) of every vertex \(v_{i}\) is at least 1.
# Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks
## 7 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Offered Load (bits/node)** & **500** & **1000** & **1500** & **2000** & **2500** \\ \hline
**Packet Success** & IEEE 802.15.4e Standard & 0.618656 & 0.390007 & 0.280493 & 0.196157 & 0.133287 \\
**Probability** & Proposed protocol & 0.765746 & 0.689023 & 0.617008 & 0.540302 & 0.503295 \\ \hline
**Throughput** & IEEE 802.15.4e Standard & 344.1575 & 331.1715 & 282.2049 & 246.1682 & 210.906 \\
**(bits/s)** & Proposed protocol & 475.9602 & 541.2176 & 542.543 & 567.9403 & 558.6206 \\ \hline
**Delay (s)** & IEEE 802.15.4e Standard & 0.11381 & 0.13283 & 0.138705 & 0.152111 & 0.17582 \\  & Proposed protocol & 0.08935 & 0.1003028 & 0.10966 & 0.115764 & 0.1124364 \\ \hline
**Energy Consumption** & IEEE 802.15.4e Standard & \(3.11\times 10^{-5}\) & \(4.9\times 10^{-5}\) & \(7.39\times 10^{-5}\) & \(11.3\times 10^{-5}\) & \(16.7\times 10^{-5}\) \\
**(joule)** & Proposed protocol & \(2.5\times 10^{-5}\) & \(3.23\times 10^{-5}\) & \(4.31\times 10^{-5}\) & \(4.43\times 10^{-5}\) & \(5.28\times 10^{-5}\) \\ \hline
**Reliability** & IEEE 802.15.4e Standard & 0.782367 & 0.415697 & 0.283299 & 0.195581 & 0.145791 \\  & Proposed protocol & 0.940286 & 0.937278 & 0.927357 & 0.922573 & 0.889301 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summarization of the obtained result of our protocol with the IEEE 802.15.4e standard. Figure 15: Validation of the reliability for different offered loads.
# Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems
## 3 MTM Analysis and Discussion
### Design Methodology
#### 3.1.2 Interdigital Capacitor Effects  
Since the introduction of the IDC structure to the proposed antenna could cause a significant performance change, the authors applied a study to evaluate the antenna S\({}_{11}\) and gain spectra. Therefore, the proposed IDC iteration number was changed from the first to the fourth degree. The effects of that on S\({}_{11}\) and gain spectra are shown in Figure 6. We realized that the proposed antenna bandwidth of the second mode at 3.8 GHz is significantly affected. Another mode was found to be generated at 5.8 GHz due to the proposed IDC introduction as seen in Figure 6b. An approximate expression for the interdigital capacitance is given by [1].  
\[C=\frac{\varepsilon_{r}+1}{W^{\prime}}I\left[(N-3)A_{1}+A_{2}\right] \tag{1}\]  
where \(C\) is the capacitance per unit length (\(l\)) with respect to the finger width (\(W\)), \(A_{1}\) (the interior) and \(A_{2}\) (the two exteriors) are the capacitances per unit length of the fingers, and \(N\) is the number of fingers and can be expressed in microns. For infinite substrate thickness (or no ground plane), \(A_{1}=4.409\times 10^{-6}\) pF/mm and \(A_{2}=9.92\times 10^{-6}\) pF/mm.  
Figure 6: Obtained antenna performance variation with changing IDC iteration: (**a**) S\({}_{11}\) and (**b**) gain spectra.  
Now, the antenna gain spectra over the entire frequency band of interest are discussed with respect to the same IDC iteration variation as shown in Figure 6b. It was found that the proposed antenna gain is enhanced significantly by increasing the proposed IDC iteration increase. Such a fact is attributed to the effects of the proposed IDC on the inherent inductive behavior of the proposed monopole [15]. Thus, the introduction of the proposed IDC minimizes the stored energy by the induction part in the monopole [16].
## 2 Marine-Based Bioactive Compounds
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Target Substance** & **Extraction Technique** & **Application** & **Reference** \\ \hline _Dunaliella salina_ & Carotenoids & supercritical carbon & Food colorant & [14; 15] \\ (microalgae) & & dioxide (sccO\({}_{2}\)) & & \\ \hline _Phaeodactylum_ & & Solvent extraction & Cellular uptake capacity & \\ _tricornutum_ & & (ethanol for 1 h at room temperature) & & and bioavailability & [16] \\ (microalgae) & & temperature) & & \\ \hline _Phaeodactylum_ & & Super-critical CO\({}_{2}\) & & \\ _tricornutum_ & & extraction (ethanol & \\ (microalgae) & & & cosolvent) & \\ \hline _Sargassum muticum_ & & Lipid-based & Solvent extraction & \\ (macroalgae) & & compounds & (ethanol, 20 min, room temperature) & Fish freezer burn control & [11] \\ \hline Tuna skin (_Thumus_ & & & \\ _obesus_) & Fish gelatine & Alkali and acid extraction & Gelling agent Food preservative & [18] \\ \hline Crustaceans & Chitin derivatives & Alkali and acid extraction & Gelling agent & [19] \\ \hline _Thraustochytrium_ sp. & & Ultrasound assisted & \\ (microalgae) & & & extraction & \\ \hline Fish oil & Omega-3 fatty acids & Solvent extraction (chloroform/methanol) & Food fortification & [21] \\ \hline _Laminaria digitata_ & & & Ultrasound assisted & Anti-oxidative potential in & \\ (macroalgae) & & laminarin & extraction combined & pork meat & [22; 23; 24] \\ \hline _Gracilaria_ sp. & & & \\ (macroalgae) & Phycobilproteins & Ionic liquid extraction (cholinium chloride) & Food colorant and antioxidant activity in milk & [25; 26] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of marine-based compounds including their sources, extraction method, and possible application in food-based materials.
## 3 Numerical Examples
### 2D-Riemann Problem  
This section uses the classic 2D Riemann problem to test the resolution of the scheme and its capacity to capture shock waves and discontinuities. Three examples of different shock wave structures and multi-scale structures are used [45].  
Figure 13: Double Mach Reflection problem: Density contours with 43 equally spaced isolines ranging from 1.887 to 20.9, obtained using the LF, LBFS, and URLBFS schemes. The color gradient represents the corresponding density values, with blue indicating the minimum density value. The simulation was conducted at t = 0.2 with a resolution of \(960\times 240\). Panels (**a–c**) show results using the WENO5 reconstruction method with LF, LBFS, and URLBFS schemes, respectively. Panels (**d–f**) display corresponding results with the WENO7 reconstruction method.
# Teleolocation--Remotely Operated Legged Robots
## 1 Introduction
### Haptic Feedback in Teleoperation  
A key component of a successful teleoperated system is a seamless user interface with the ultimate goal to achieve a high degree of telepresence [3]. Teleoperator feedback modes can influence the overall effectiveness. Typical traditional feedback modes include visual, 2D and 3D, and auditory [17]. The haptic sensorimotor pathway has been leveraged to provide the operator with additional sensory information in the forms of both tactile and kinesthetic feedback [18]. [PERSON] et al. presented a method to provide four types of haptic feedback--compliance, friction, fine-roughness and shape with a single force feedback device [19]. Haptic feedback can present cues to the operator as well as intelligent, task-based force feedback called virtual fixtures. Virtual fixtures can enhance task execution and shift task burdens from the operator to semiautonomous feedback. Such systems have great promise in the domains of vision-based assistance in surgical robotic procedures [14; 20], joint-limit cue delivery [21], underwater robotic operations [12], integrating auxiliary sensors [22; 23], and assembly and disassembly training [24].
## 5 Conclusions  
NPS and M-LKS appeared as reliable and complementary measures for assessing disease severity and treatment response in patients with CRSwNP undergoing dupilumab therapy. Both scores showed significant and sustained reductions, closely correlating with improvements in functional outcomes such as PNIF and SSIT. NP shrinkage plays a central role in improving olfactory function, likely due to increased olfactory cleft airflow but also to the reduced inflammation of the olfactory cleft mucosa. The correlation between M-LKS, NPS, PROMs, SSIT, and PNIF highlights the importance of a multimodal approach to evaluate CRSwNP response during biologic therapy. While NPS excels in monitoring rapid changes in polyp size, M-LKS captures broader inflammation-related disease features, providing a more comprehensive overview. The lack of significant differences between these two scores indicates that improvements in one parameter closely align with changes in the other. Future studies are solicited to further delineate their complementary roles to refine CRSwNP management strategies.  
Conceptualization, G.O.; methodology, G.R.; software, B.S. and P.M.; validation, G.R., G.O. and T.S.; formal analysis, B.S. and P.M.; investigation, G.R., T.S., G.O., S.Z., N.T. and A.F.; data curation, G.R., G.O., T.S., S.Z., N.T. and A.F.; writing--original draft preparation, G.R.,G.O., T.S., S.Z. and N.T.; writing--review and editing, G.R., G.O. and T.S.; visualization, G.R., G.O., T.S., S.Z., N.T., A.F. and P.N.; supervision, G.O. and P.N.; project administration, G.R. and G.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted in accordance with the Declaration of Helsinki and approved by Hospitals' ethical committees (Padova University Hospital: 5304/AO/22).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The datasets generated and analyzed during the current study are available on reasonable request.  
The authors are thankful to [PERSON] for proofreading the manuscript.  
The authors declare no conflicts of interests.  
The following abbreviations are used in this manuscript:  
CRSwNP Chronic rhinosinusitis with nasal polyps  
NPS Nasal polyps score  
M-LKS Modified Lund-Kennedy endoscopic score  
PROMs Patient-reported outcomes  
CROMs Clinician-reported outcomes  
PNIF Peak nasal inspiratory flow  
CRS Chronic rhinosinusitis  
QoL Quality of life  
INCS Intra-nasal corticosteroids  
OCSVAS Oral corticosteroidsVisual analog scale  
VAS-NO Visual analog scale for nasal obstruction  
SSIT Sniffin'Sticks test  
NP Nasal polyps
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 4 Laser Crosslink Payload
### PAT Algorithm  
For highly precise beam pointing and tracking during laser crosslinking, the LCT estimates the beam spot positions on the detectors and corrects the offset using the FSM. However, micro-vibrations, such as random jitters induced by orbital perturbations and RW control, might increase pointing errors even when a laser crosslink is established. During the FPS, when the QC is available, the FSM is operated to reject jitters at a frequency faster than 200 Hz to avoid resonance with the systems, and the operating frequency is approximately 10 times higher than the natural frequency of the systems. Considering the jitter characteristics of the RWs, the sampling rates of CAM and OC were set to 100 Hz and 2000 Hz, respectively. A PI controller was adopted in the FSM control algorithm to mitigate steady-state errors. Figure 13 shows the power spectral density (PSD) and residual jitters. With FSM operations, the jitter response is significantly reduced below the FSM control frequency domain. Nanosatellite body-pointing error profiles were applied to the FSM control simulations. Finally, the total pointing error during the FSM operations was reduced to less than 1 \(\upmu\)rad (0.2 arcsec), as presented in Figure 13c.  
Figure 12: Diagram of optical path and electrical interface for the laser communication payload.  
Figure 13: _Cont._
# Towards DC Energy Efficient Homes
## 3 Factors for DC Installations
### Installation of DC at Home  
Most devices in homes today operate in DC, and, with the current trends in technologies, more and more appliances will need to run in DC [74; 75]. A DC power supply for these devices directly increases the efficiency at home because it needs fewer conversions, which is on average 14% more efficient [76]. In addition, the heat losses produced by these conversions are reduced because they are usually carried out internally in the devices. Thus, DC-powered appliances favor a greater simplicity and a smaller number of elements. Therefore, DC has the ability to improve the resiliency, reliability, sustainability, energy efficiency, and safety of homes.  
Because of the advantages of DC and the growing trends of its application, not only in the different household devices, but also in different fields (presented in Section 2 (see EV,  
Figure 6: World shares of residential PV net capacity addition per year. (Data collected from [69] on 26 May 2021).  
PV or LED)), it is necessary to consider a change in the home distribution system [77; 78; 79]. Figure 7 shows a design of a possible DC distribution that could occur in homes in the next few years.  
It is not surprising that, due to these virtues (among others), certain clusters and trends emerge in order to standardize and promote the development of DC, such as Emerge Alliance or PoE. The EMerge Alliance is an association with different affiliated organizations, such as ABB, IEEE, IEC, Cisco, and Bosch, that develops standards for the implementation of DC. Among these standards, the standard that establishes the use of 380 V DC for distribution in buildings and 24 V DC for occupied spaces stands out [74]. PoE is a technology that has been developed with current trends in technology that incorporates power supply into a LAN infrastructure. Current voltages are established according to the IEEE 802.3 standard with values between 42.5 and 59 V DC [80].  
There have also been countless projects and studies on DC that extol their advantages, such as the Bosch DC microgrid or the Pulse projects. The Bosch DC microgrid is a project created by Bosch, in North Carolina in 2017, to reduce energy consumption at a local fitness center and an emergency shelter through a DC energy management system. As a result, the system increased its efficiency by 13% and reduced its consumption by more than 55,000 kWh/year [81]. Pulse is a project that was completed in 2018, carried out at the Technical University of Delft, in order to achieve an energy neutral building using an intelligent system with DC, solar and storage [82].
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
###### Abstract  
The realization of advanced optical fiber probes demands the integration of materials and structures on optical fibers with micro- and nanoscale definition. Although researchers often choose complex nanofabrication tools to implement their designs, the migration from proof-of-principle devices to mass production lab-on-fiber devices requires the development of sustainable and reliable technology for cost-effective production. To make it possible, continuous efforts are devoted to applying bottom-up nanofabrication based on self-assembly to decorate the optical fiber with highly ordered photonic structures. The main challenges still pertain to \"order\" attainment and the limited number of implementable geometries. In this review, we try to shed light on the importance of self-assembled ordered patterns for lab-on-fiber technology. After a brief presentation of the light manipulation possibilities concerned with ordered structures, and of the new prospects offered by aperiodically ordered structures, we briefly recall how the bottom-up approach can be applied to create ordered patterns on the optical fiber. Then, we present un-attempted methodologies, which can enlarge the set of achievable structures, and can potentially improve the yielding rate in finely ordered self-assembled optical fiber probes by eliminating undesired defects and increasing the order by post-processing treatments. Finally, we discuss the available tools to quantify the degree of order in the obtained photonic structures, by suggesting the use of key performance figures of merit in order to systematically evaluate to what extent the pattern is really \"ordered\". We hope such a collection of articles and discussion herein could inspire new directions and hint at best practices to fully exploit the benefits inherent to self-organization phenomena leading to ordered systems.  
Keywords:self-assembly; nanosphere lithography; lab-on-fiber; optical fiber sensors; photonic crystals; nanofabrication; nanopatterning; honeycomb structure; periodic nanostructures; biosensors +
Footnote †: journal: Applied Sci. 2021, 11, 3254. [[https://doi.org/10.3390/app11073254](https://doi.org/10.3390/app11073254)]([https://doi.org/10.3390/app11073254](https://doi.org/10.3390/app11073254))
## 3 Research Results  
This section shows the five steps from the acquisition of borehole data to seismic liquefaction assessment, the development of an improved IDW algorithm, and 3D seismic liquefaction hazard mapping. This workflow was proposed so that different application systems could achieve consistent aims by inputting, processing, and outputting highly interoperable data formats (Figure 5), and Figure 6 shows the conceptual diagram in 3D mapping of estimation results using voxel.  
1. Achieve borehole data, unify contents and format. (Microsoft Excel (Microsoft, Redmond, WA, USA));
2. Perform seismic liquefaction hazard assessment by using refined borehole data (MATLAB (MathWorks, Natick, MA, USA));  
Figure 4: Proposed approaches for 3D search neighborhoods: (**a**) Spheroid-based approach; (**b**) bounding box-based approach.  
3. Apply improved IDW interpolation. However, when the estimated result consists of 'NoData', Perform IDW interpolation repeatedly on the analyzed result (Rhino/Grasshopper (Robert McNeel & Associate, Seattle, WA, USA));
4. Perform 3D seismic liquefaction hazard mapping based on the estimated results (ArcGIS Pro (Esri, Redlands, CA, USA));
5. Publish 3D web application by using mapping results (ArcGIS Online/WebApp, Builder (Esri, Redlands, CA, USA)).  
Figure 5: Workflow of borehole data-based seismic liquefaction hazard assessment and mapping.  
Figure 6: Conceptual diagram of improved IDW results mapping.