esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Probabilistic Reliability Analysis of Slopes Based on a Multidimensional Parallelepiped Model
## 1 Introduction
[18] proposed a new topology optimization method based on a multidimensional parallelepiped model for non-probabilistic reliability, which was used to take into account the interval uncertainty of parameter correlation, and provided an effective method for solving topology optimization problems under non-probabilistic uncertainty. The above study verified the feasibility of the multidimensional parallelepiped model for non-probabilistic reliability analysis. However, there is no current research work on the use of such a model for slope stability analysis, and the feasibility of using this model for slope engineering is yet to be investigated due to the complexity of geotechnical parameters. This paper proposes a non-probabilistic reliability analysis method of slopes based on a multidimensional parallelepiped model. First, the multidimensional parallelepiped model is constructed based on limited sample data of geotechnical parameters. Next, the performance function of the slope is constructed by using Latin hypercube sampling and response surface methodology. Then, the limit state equation of the slope can be standardized by using the multidimensional parallelepiped model. Finally, the non-probabilistic reliability index of the slope is solved using a global optimal solution method. The effects of different multidimensional parallelepiped models, correlation, and variability of shear strength parameters on the non-probabilistic reliability index of the slope are investigated. The novelty of the proposed method lies in the fact that a multidimensional parallelepiped model is introduced into the non-probabilistic reliability analysis of slopes for the first time, which solves the problem that the correlation between geotechnical parameters cannot be considered by traditional interval models. Thus, it has important practical engineering significance.
## 2 Ten Case Studies in Extended Realities for Cultural Heritage Preservation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Case Study \#** & **Application Scenario** & **Application Type** & **Scenario of Use** & \multicolumn{1}{c}{**HCD and Evaluation**} \\  & & Entertainment and educational smartphone app, 3D interactive virtual environment of the Louvre Museum & Self-guided tour through cultural heritage events, costumes, sightbreseing & Design Thinking process, persona description, medium-/high-fidelity prototyping, expert review \\ \hline
2: Cham Culture AR & In-Home/School & AR smartphone/table app with QR cards & Viewing animations of rituals and costumes from ancient stone carvings & Persona description, SUS survey, prototyping \\ \hline
3: Memories of Kellie & In-Home/School & Desktop PC app, 3D interactive virtual environment & Narrative & Persona description, UXUI testing, Agile methods \\ \hline
4: Fort Ontario & In Situ (Museum/ On-Location) & AR smartphone app & Self-guided tour through a CH open air museum & CW with users, usability survey, think-aloud, observation \\ \hline
5: Hanging Gardens of Babylon & Online Platforms & VR and AR educational tool & A/B test of historical learning (AR vs. VR) & Experimental design, memory retention test \\ \hline
6: Weigh House Estonia & In Situ (Museum/ On-Location) & AR smartphone app & On-site visualization of reconstructed building & Theme-based content analysis, EMOTIVE framework, UXUI testing \\ \hline
7: Fountain of the Lions & In Situ (Museum/ On-Location) & 3D computer graphics museum installation & On-site interactive exhibit & 3D cognitive walkthrough, 3D heuristic evaluation \\ \hline
8: CHISel & Online Platforms & 3D collaborative annotation and restoration platform & Expert-only multi-user tool under development & CW with experts, think-aloud protocol, observational data \\ \hline
9: PLUGGY Platform & Online Platforms & Web-based social media curation platform & Create/share CH XR exhibitions & Triangulated HCD framework, SUS, NPS, EMOTIVE, prototyping, expert reviews \\ \hline
10: Picasso AR & Hybrid Pre-/ Post-Visit and On-Location & AR-AI location-based app & Chat with virtual [PERSON], hybrid tourism UX & Lean MV validation, persona interviews, pitch deck, prototype \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the ten XR cultural heritage case studies: application scenarios and HCD methods. in creating these physgital use cases, requiring various configurations of teams of domain experts to collaborate in cross-disciplinary development team meetings. Together they have to define the user experience and the diverse technologies and cultural heritage assets which are going to be incorporated and how they are going to be experienced by the intended users. A framework for Phygital Cultural Heritage Tourism App Development, proposed to facilitate the collaboration process between the various CH XR domain experts, has been developed and is provided in the Section 4.
# Superhydrophobic Surfaces Enabled by Femtosecond Fiber Laser-Written Nanostructures
## 3 Results and Discussion
### Effect of Laser Irradiation on Surface Morphology  
During line-by-line processing (lines separated by the pitch), the laser both moves and removes material from the surface via metal melting and evaporation (ablation), thus forming periodic structures (re-solidification). To fabricate metals with hydrophobic properties, the surfaces must be made into one of the three structures: microstructures, nanostructures, or hierarchical structures [29]. Hierarchical structures are nanostructures formed on microstructures. Studies have shown that structures with higher aspect ratios resist water condensation better, exhibiting greater hydrophobicity [30].  
SEM images were obtained for analysis of the structures of the fs laser-processed surfaces. Figure 3 displays the hierarchical structures, estimated to have an aspect ratio of 3, at different magnifications with a resolution from microscale to nanoscale. Coral-like surface structures with microcavities were formed at random orientations. These randomly oriented microcavities consisted of small grooves with dimensions in the range of 5-10 \(\upmu\)m, which created an ultrafine porous structure. Further studies demonstrated that these structures were reproducible and were well-controlled by adjusting the laser parameters.  
Although these structures were oriented randomly, when they formed a homogeneous pattern (Figure 3), the metal surface was found to be hydrophobic; when they formed a heterogeneous pattern (Figure 4), the surface was found to be hydrophilic. A possible explanation for this phenomenon is  
Figure 3: The fabrication of homogeneous hierarchical structures on a metal surface that resulted in hydrophobic traits, presented with different resolutions of 100 \(\upmu\)m (left) and 10 \(\upmu\)m (right) scale bars.  
Figure 2: Water droplets rolled to the edge of the sample, exhibiting uniform superhydrophobic properties across a \(3\times 3\) cm structured metal surface.  
that suboptimal parameters generated non-uniform thermal conductivity, causing uneven ablation and re-solidification throughout the surface.
## 5 Simulation Results
### Acceleration-Level Algorithm Results  
#### 5.2.1 Trajectory Tracking Results  
Figure 11 shows the dynamic response of the system controlled through the acceleration-level algorithm with the reference joint acceleration profiles computed by Equation (24). All the disturbances and possible variations in the inertial parameters presented in Section 4 are active, as in the case of the velocity-level algorithm.  
Figure 12 shows the dynamic response of the system subject to all the modeled disturbances and variations in the inertial parameters when controlled by the closed-loop acceleration-level algorithm. The proportional and derivative gains are set to \(K_{P}=100\) and \(K_{D}=20\).  
It can be noticed that the acceleration-level algorithm ensures good accuracy in tracking the desired trajectory even in the presence of sensors noise: the maximum error norm  
Figure 11: System response in the case of wind disturbances and sensors noise. The system is controlled by an open-loop acceleration-level inverse kinematics algorithm (\(K_{P}=0\)). The inverse kinematics is solved using the maximum positive variation in all the inertial parameters, see Table 5. (**left**) Task-space error 2-norm, \(\|e(t)\|\), as a function of time. From the instant \(t=T_{path}\), indicated with the black vertical line, the end-effector tries to remain still at the trajectory final point. (**right**) Graphical output of the simulator in the vertical (x–z) plane; the blue star represents the UAM CoG.  
Figure 12: System response in the case of wind disturbances and sensors noise. The system is controlled by a closed-loop acceleration-level inverse kinematics algorithm (\(K_{P}=100\), \(K_{D}=20\)). The inverse kinematics is solved using the maximum positive variation in all the inertial parameters, see Table 5. (**left**) Task-space error 2-norm, \(\|e(t)\|\) (in blue), and force on the end—effector, \(F_{EE}\) (in orange), as a function of time. From the instant \(t=T_{path}\), indicated with the black vertical line, the end-effector tries to remain still at the trajectory final point. (**right**) Graphical output of the simulator in the vertical (x–z) plane; the blue star represents the UAM CoG.  
is always less than 4 mm. Moreover, the actual end-effector trajectory is significantly smoother with respect to that of the velocity-level algorithm.  
Finally, in Figure 13, the performance of the algorithm in controlling a UAM with a three-DOF arm is shown. In this case, the error is slightly larger but still acceptable in real-world application scenarios.
# Optical Fibres for Condition Monitoring of Railway Infrastructure--Encouraging Data Source or
## 2 Optical Fibres--A Brief Overview
### Distributed Fibre Optical Sensing (DFOS)
The basic technology of all DOFS can be found in the development of the optical time-domain reflectometry (OTDR) proposed by [PERSON] and [PERSON] [49; 50] for the first time in the late 1970s, demonstrating its ability for detecting malfunctions and faults in optical fibres. Nowadays, it is mainly used for monitoring the health of communication links and newly installed optical fibres. A traditional OTDR system uses an incoherent light source ending in a signal with a smooth trace. The progress in both laser technology and science, as well as the shift from multimode to single-mode fibres, led to lasers with narrower linewidths which have been applied to traditional OTDR systems. The exploration of coherent detection extended the range of OTDR [51; 52] and is realised by the optical mixing of the backscattered light and reference light. [PERSON] [53] proved that light sources with better coherence could lead to a degradation of OTDR measurements. Instead of a smoothed line, a rather jagged signal shape is obtained. What initially was considered as a nuisance [54; 55], was later proven to be the phase response of the Rayleigh backscattered light and, in contrast to other methods, coherent backscatter cannot be averaged out [56] because they arise from random effects. As the phase of coherent incident light is consistent for all the dipoles within a resolution cell, the same applies to scattered light. As a result, the scattered light consists of the summation of electric fields that have a stable and random phase relationship. It was [PERSON] and [PERSON] [57], and later [PERSON] and [PERSON] [58], who discovered the applicability of Rayleigh backscatter for distributed disturbance measurements. The practical demonstration of the sensitivity of backscatter intensity in COTDR followed in 1994 [59]. Although the sensing properties from a C-OTDR technique have some downsides compared to OFDR or \(\phi\)-OTDR (decreased spatial resolution and gauge length), they also have substantial benefits such as a wide range and easy applicability and thus have been used for many different applications,including linear asset protection, border monitoring, train traffic management and detecting natural hazards. [PERSON] et al. and [PERSON] et al. (2016); [PERSON] et al. (2016); [PERSON] et al. (2016) set up a scalable system architecture that enables border monitoring, a high security perimeter and linear asset protection. [PERSON] et al. (2016); [PERSON] et al. (2016) developed a rail traffic management system utilizing C-OTDR. The fibre was buried near the railway track at a depth of 50-100 cm. The system was able to detect the trains with an accuracy of up to 1.10 m. In 2016, [PERSON] et al. (2016) proposed DAS as a monitoring technology to protect railway tracks against natural hazards. DFOS in railways is not intended to replace proven and established measurement technologies (at the moment), but rather to provide additional information where it is needed.
###### Abstract  
This study aimed to examine the reliability, validity, and usefulness of the smartphone-based application, My Jump 2, against Optojump in recreationally active adults. Participants (18 women, 28.9 \(\pm\) 5.6 years, and 26 men, 30.1 \(\pm\) 10.6 years) completed squat jumps (SJ), counter-movement jumps (CMJ), and CMJ with arm swing (CMJAS) on Optojump and were simultaneously recorded using My Jump 2. To evaluate concurrent validity, jump height, calculated from flight time attained from each device, was compared for each jump type. Test-retest reliability was determined by replicating data analysis of My Jump 2 recordings on two occasions separated by two weeks. High test-retest reliability (Intraclass correlation coefficient (ICC) > 0.93) was observed for all measures in both male and female athletes. Very large correlations were observed between the My Jump 2 app and Optojump for SJ (\(r\) = 0.95, \(p\) = 0.001), CMJ (\(r\) = 0.98, \(p\) = 0.001), and CMJAS (\(r\) = 0.98, \(p\) = 0.001) in male athletes. Similar results were obtained for female recreational athletes for all jumps (\(r\) > 0.94, \(p\) = 0.001). The study results suggest that My Jump 2 is a valid, reliable, and useful tool for measuring vertical jump in recreationally active adults. Therefore, due to its simplicity and practicality, it can be used by practitioners, coaches, and recreationally-active adults to measure vertical jump performance with a simple test as SJ, CMJ, and CMJAS.  
measurement; healthy athletes; jump performance; smartphones; My Jump 2; reliability; validity +
Footnote †: journal: Applied Sciences
## 3 Results
### Stability Test of MNDVI Estimation for \(f\)PAR
#### 3.3.1 Accuracy Assessment Based on Different Varieties  
The measurement of canopy reflectance and \(f\)PAR could not be completed due to insufficient emergence of T3 and T5 varieties in some plots and the inability to measure IS-F and IS-G in all plots on 11 April and 18 April due to irrigation. However, the measurements from all other plots were included in the accuracy validation. Figure 3 shows the relationship between the estimated values calculated using Equation (3) and the ground truth measured values, where the horizontal axis represents the estimated values and the vertical axis represents the measured values.  
It can be seen that, with the exception of variety T10, the other varieties have a good correlation and a low RMSE. The variety T6 has the highest coefficient of determination of 0.918, while variety T1 has the lowest RMSE of 0.074. This indicates that MNDVI can maintain high stability among different varieties, and in actual production processes in regions such as the North China Plain, it is difficult to obtain extensive sowing data. MNDVI has great potential for application in yield estimation in these research areas.
## 2 Design and Implementation
### Antenna Design-I with Performance Evaluation
The reported literature based on slotted patch antenna has observed that lower and higher frequency bands are affected when slot elements are embedded on the antenna geometry because of disturbance in surface current directions on the patch and the ground plane. Figure 4b shows the plot of received antenna power from wireless video camera. Here, we have considered video camera as RF wireless device, which can transmit a power of 242 mW towards the antenna while maximum received antenna power of 29 mW is achieved at the desired frequency of operation. The received power of the antenna is measured using video camera (Canon High-definition (HD) RF GHz wireless, Ota, Tokyo, Japan), power meter (N8262A P-series), and vector network analyzer (VNA) (Keysight Technologies, 8722 ES, Santa Rosa, CA, United States) hardware set-ups. The simulated and measured antenna power are made good correlation with each other. The antenna efficiency variation with respect to frequency is illustrated in Figure 4c. The maximum antenna efficiency of 70% is observed within ultra-wide band of performance. The most important tilt angle of receiving antenna is plotting in Figure 4d. The title angle shows an equal radiated power from \(-90^{\circ}\) to \(90^{\circ}\) in both direction (top and bottom). The measurement is done by using Keysight Technologies' vector network analyzer (VNA) (Keysight Technologies, 8722 ES, Santa Rosa, CA, USA) with proper calibration. Tilt angle is measured by using small anechoic chamber under the environmental conditions.
## 1 Introduction  
During the past few decades, researchers and developers have shown much interest in developing UIoT applications such as deep-sea exploration, divers' system monitoring, early warning generation, naval network surveillance, etc. As shown in Figure 1, the UIoT network consists of heterogeneous devices such as underwater sensor nodes (UW-SNodes), underwater cluster heads (UW-CHs), remotely operated underwater vehicles (ROVs), unmanned underwater vehicles (UUVs), autonomous underwater vehicles (AUVs), etc. The UIoT devices can be fixed or mobile, moving from one location to another to gather information and transmit that information via digitally linked devices in water bodies such as the gateway or buoy in surface water. In addition, other devices like moving gateways, satellites, base stations, etc., are utilized to expand the communication range of UIoT applications.  
In the recent survey produced by the United States National Oceanic and Atmospheric Administration (NOAA), 97% of the earth's surface is covered with water [1]. The UIoT environment is coped with smart sensing underwater devices that are installed with heterogeneous functionalities. Many researchers have proposed different methodologies to design and develop various UIoT applications in the last few years. However, the challenges and limitations are still concerns for the UIoT environment based on the application, channel types and channel characteristics. Channel types define the type of medium used in UIoT environments such as RF, acoustic, optical (VLC: visible light communication or IR: infrared) and MI, and channel characteristics represent the technical factors that affect the medium used in UIoT environments, such as propagation speed, turbulence, pressure, node mobility, etc. [2] Security attacks and privacy issues are the other key challenges in the current UIoT system [3].  
This research aims at providing a survey of the state-of-the-art research, communication technologies, challenges, security attacks and privacy issues and provides the mitigation methodology to overcome the challenges and security attacks in the current UIoT system. Furthermore, this research will help the researchers and developers to build new UIoT applications by considering the best channel type with security and privacy models. The key contributions of this paper are briefed under research goals in Table 1.  
The layout of this paper is delivered as follows: Section 2 represents the prior study insights and recently used communication technologies of the UIoT system. Section 3 describes the technical challenges, security attacks and privacy issues of UIoT system. Section 4 provides the available methods to overcome the challenges, security attacks and privacy issues of the UIoT system. Section 5 highlights the findings, future work and directions of UIoT system, and Section 6 concludes the paper.  
Figure 1: UIoT Architecture.
# Structure-Property Relation of Trimethyl Ammonium Ionic Liquids for Battery Applications
## 2 Materials and Methods
### Fitting of the Transport Properties  
Transport properties of the pure ionic liquids and the mixtures with lithium salts were fitted with the Vogel-Fulcher-Tammann-(Hesse) (VFT) Equation (3), which is commonly applied to describe the transport properties of fragile glass-forming materials in dependence on temperature [24]. The VFT equation is frequently used and describes the temperature-dependence of the ionic liquid transport properties in a broad temperature range more accurately than the Arrhenius Equation (4) [25; 26].  
\[Y=Y_{0}\exp\left(\frac{B_{Y}}{T-T_{0,Y}}\right) \tag{3}\]  
The variable \(Y\) being the fitted transport property, e.g., the dynamic viscosity \(\eta\), the specific conductivity \(\kappa\), the molar conductivity \(\Lambda_{\text{M}}\) or the self-diffusion coefficients \(D_{\text{S}}(i)\) (with \(i\) being ionic liquid cation (\({}^{1}\)H), anion (\({}^{19}\)F) or lithium cation (\({}^{7}\)Li)). \(Y_{0}\), \(B_{Y}\) and \(T_{0,Y}\) (the so-called Vogel temperature) are material dependent fitting parameters. The parameter \(B_{Y}\) obtains positive values for \(Y=\eta\) and negative values for \(Y=\kappa\), \(\Lambda_{\text{M}}\), \(D_{\text{S}}(i)\). [PERSON]'s strength parameter \(\delta_{Y}\) is the absolute value of \(B_{Y}\) divided by \(T_{0,Y}\) and is a measure of the liquid fragility of glass formers, and thus describes the rate of change in a transport property upon change in temperature. \(\delta_{Y}\) is often referred to as \(D\) in the literature, which is avoided here in order to prevent confusion with the self-diffusion coefficients \(D_{\text{S}}(i)\)[27]. [PERSON]'s strength parameter is related to the kinetic fragility index \(m\), an alternative measure for liquid fragility, by the relation \(m=16+590\,\delta_{Y}{}^{-1}\)[27]. Liquids classified as 'fragile' show fast changes in the transport properties and narrow glass transition temperature range, while'strong' glass forming liquids have nearly constant activation energy of the transport process and broad temperature intervals of the glass transition. Notwithstanding the more accurate description of the \(T\)-dependence of the ionic liquid transport properties by the VFT Equation (2) in a broad temperature range, the Arrhenius Equation (4) can be used to determine the activation energy \(E_{\text{a,Y}}\) of a transport process in a narrower temperature range, where linear fits are feasible and the activation energy can be regarded as constant [28].  
\[\ln(Y)=\ln(Y_{0})-\frac{E_{\text{a,Y}}}{RT} \tag{4}\]  
where \(E_{\text{a,Y}}\) is the activation energy of the transport quantity \(Y\) within the considered temperature interval. For \(Y=\eta\), positive values of \(E_{\text{a,Y}}\) are obtained, while \(Y=\kappa\), \(\Lambda_{\text{M}}\), \(D_{\text{S}}(i)\) yields negative values.