esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, a gradient ratio filtration test device capable of applying normal stress was designed and applied to perform a series of filtration tests on various nonwoven geotextiles and fine-grained soil combinations. The head loss through soil geotextile samples, as well as the variation with applied normal stress and time, were investigated to assess the compatibility of fine-grained soils and nonwoven geotextiles in filtration applications. Based on the gradient ratio filtration test results, the following conclusions can be drawn:  
1. Nonwoven geotextile filters with different filtration opening sizes, porosities, and thicknesses showed different compatibilities with fine-grained soil samples. Thick needle-punched nonwoven geotextile had a relatively smaller filtration opening size, and the permeability of the soil-geotextile system decreased by 1-2 orders of magnitude after 48 h of filtration, resulting in poor filtration performance.  
Figure 12: A schematic interpretation of the gradient ratio filtration test results.  
2. The filtration performance of geotextile filters responded significantly to applied normal compressive stresses. As normal compressive stress increased, \(GR\) values also increased but the permeability decreased in soil-geotextile combinations.
3. Filtering fine-grained soils by needle-punched nonwoven geotextiles was primarily manifested as clogging, in which most of the fine particles were entrapped inside geotextile pores and retained on the fibers. The mass of fine particles per unit area was found to be significantly related to normal stress magnitude, soil characteristics, and nonwoven geotextile thickness, i.e., fine particle retention was greater when a thicker geotextile filters the internally unstable soil under higher normal compressive stress.
4. Although the nonwoven geotextile clogged in fine-grained soil filtration, the soil-geotextile composite layer was relatively thin, and the head loss occurring across the composite layer would have little effect on the overall system's seepage flow. Therefore, the lower permeability of soil-geotextile composites than protected soil was acceptable. Furthermore, the anticlogging of geotextile filters should be built into the permeability requirements. The current clogging criterion for nonwoven geotextiles filtering fine-grained soils is relatively conservative.  
Conceptualization, C.D., C.X. and Y.Y.; methodology, C.D., C.X., Y.Y. and J.W.; investigation, C.D.; writing--original draft preparation, C.D.; writing--reviewing and editing, C.D., C.X. and Y.Y.; supervision, C.X. and Y.Y.; project administration, C.X.; and funding acquisition, C.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 42172297).  
Not applicable.  
The authors greatly appreciate the comments from the reviewers, whose comments helped to improve the quality of the paper.  
The authors declare no potential conflict of interest.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 1 Introduction
### Contributions  
The main contributions of this work can be summarized as follows:  
* We provide a novel methodology for structuring the data produced by the digital twin into an event log suitable for process mining activities. This methodology includes a sequence-based formalization that facilitates the conversion of raw device logs into event logs, enabling the discovery of normal behavioral patterns and anomalies indicative of cyberattacks targeting ICS devices.  
* We present a realistic case study that replicates an industrial process through the use of digital twins to create a controlled and monitored environment for safely executing and analyzing cyberattacks without impacting real-world systems. By integrating process mining techniques, we generate event logs from these simulated attacks, allowing for systematic anomaly detection.
* We conduct an experimental assessment of our proposed approach. The results show that our method effectively detects anomalies associated with cyber-physical attacks while maintaining a high detection rate in spite of attempts to mask attacks with benign data, thereby validating its robustness and effectiveness in a realistic industrial setting.
# Helicopter Takeoff and Landing Point Location in Cities for Emergency Services
## 4 Optimization of Selection Based on the Greedy Algorithm  
### Rescue Radius Selection  
The international rescue service radius for helicopters is 50 km. Considering the current shortage of helicopters in China, this indicator can be appropriately enlarged. For example, Japan currently implements a 100 km forward detection and rescue service radius [22]. However, due to the considerable uncertainty and limitations of terrain and environment in low-altitude rescue, in order to maximize the survival rate of rescues and reduce the severity of the injuries, the rescue scope of helicopters should be combined with the actual situation and cover densely populated areas as much as possible.  
According to the golden rescue time, for different diseases, geological disasters and traffic accidents, medical treatment should be sought as soon as possible. The medical community generally recommends 30 min. Regarding factors such as the approval of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Longitude/\({}^{\circ}\)** & **Latitude/\({}^{\circ}\)** \\ \hline
1 & 119.0928 & 32.2183 \\
2 & 119.0301 & 31.3250 \\
3 & 119.0066 & 32.2029 \\
4 & 118.9946 & 32.2005 \\
5 & 118.9172 & 32.0416 \\
6 & 118.8749 & 31.8023 \\
7 & 118.7983 & 32.0703 \\
8 & 118.7896 & 32.0699 \\
9 & 118.7872 & 32.0827 \\
10 & 118.7852 & 32.0711 \\
11 & 118.7601 & 32.2026 \\
12 & 118.7191 & 32.0662 \\
13 & 118.4501 & 32.0317 \\
14 & 118.3416 & 31.3601 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Spot center coordinates (partial).  
helicopter temporary route and the preparation of the helicopter crew, the above pre-rescue time was set at 15 min. Under the constraints of rescue time, the maximum rescue radius should not exceed 50 km (estimated at a level flight speed of 203 km/h under the maximum weight of Mi-8).  
In order to accurately obtain the relationship between the medical coverage and the rescue radius, the geographic coordinates of the preliminary screening results were extracted by using GIS technology, and the ground surface was divided into small squares of 1 km\({}^{2}\). If the distance between the coordinates and the center of the square is lower than the rescue radius, it is considered that the area is within rescue range, and this square is within the coverage. We calculated the percentage of the squares within the coverage, and coverage rate was acquired. On this basis, this paper calculates the relationship between the rescue radius of the take-off and landing point and the coverage rate and obtains the approximate variation law of the two, as shown in Figure 5.  
Taking the above factors into consideration, when the rescue radius is selected as 28 km, a coverage rate of 96.87% can be achieved, and the helicopter pre-rescue time can be greatly reduced, which in turn, buys valuable time for follow-up rescue work.
## 1 Introduction
### Acceptance Phases for Long-Term Technology Acceptance
#### 1.4.1 Expectation Phase  
In the Expectation phase, individuals have no personal knowledge of, or experience interacting with, the technology [59]. Individuals learn about the technology through word of mouth or research and begin forming an attitude towards it before encountering it [52; 58]. Prior studies found that potential users begin forming expectations by asking questions such as, \"How does it work?\" and \"Why does it work?\" to seek an understanding of the uses, controls, and functionality of the technology [55]. Initial concerns or uncertainty about usage and privacy can arise. People who are more hesitant about this technology may seek reinforcement to persuade them to move forward to the next phase of using the technology for themselves [56]. Individuals often rationalize using the technology and begin to form attitudes and emotions toward it [9; 55]. This phase directly connects to people's understanding of and preparation for using the technology.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acceptance Phase** & **Timeline** \\ \hline Expectation & Two weeks prior to the introduction of the technology. \\ Encounter & Introduce the technology. \\ Adoption & Two weeks after the introduction of the technology. \\ Adaptation & One month after the introduction of the technology. \\ Integration & Two months after the introduction of the technology. \\ Identification & Six months after the introduction of the technology. \\ Non Use & Can occur at any time. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Approximate phases timeline according to a phased framework [9].
## 5 Experiment and Results
### AR Visualization Service Test
The LOD defined for the test object of the experiment, which was the main gate of Sejong University, included nine information fields. These fields were Name, ID (identification), Type, Establish (date of establishment), History (explanation concerning the name or history of the place), Location (geographic coordinates), Urban (land use), Time (time of opening), and President (name of the president of the institution). As the implementation shows, this study created an interface that is personalized based on the environment and context of the user. This interface adjusts the information properties (primarily, LOD and text size) using context data, and this is a significant innovation compared to other studies.
# Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
## 2 Second-Order Systems
### Underdamped Behavior (\(0<\zeta<1\))  
In this behavior, the poles (roots of the denominator) of Equation (1) are complex conjugated and located in the left-half of the \(s\) plane. Under such conditions, the system underdamped response \(C_{U}(s)\) in the Laplace domain can be characterized as follows:  
\[C_{U}(s)=\frac{\omega_{n}^{2}}{s(s+\zeta\omega_{n})^{2}+\omega_{n}^{2}(1-\zeta ^{2})}. \tag{2}\]  
Applying partial fraction operations and the inverse Laplace transform, it is obtained the temporal response that describe the underdamped behavior \(c_{U}(t)\) as it is indicated in Equation (3):  
\[c_{U}(t)=1-\frac{e^{-\zeta\omega_{n}t}}{\sqrt{1-\zeta^{2}}}\sin(\omega_{n} \sqrt{1-\zeta^{2}}+\tan^{-1}(\frac{\sqrt{1-\zeta^{2}}}{\zeta})). \tag{3}\]  
If \(\zeta=0\), a special case is presented in which the temporal system response is oscillatory. The output of these behaviors is visualized in Figure 2 for the cases of \(\zeta=0\), \(\zeta=0.2\), \(\zeta=0.5\) and \(\zeta=0.707\). Under the underdamped behavior, the system response starts with high acceleration. Therefore, the response produces an overshoot that surpasses the value of one. The size of the overshoot inversely depends on the value of \(\zeta\).
## 3 Simulation
### Simulation Scenarios  
Derived from various speeds, loading configurations, and rail irregularity profiles, Table 1 provides an overview of the simulations conducted for both undamaged and damaged scenarios.  
The baseline scenarios comprise crossing speeds ranging from 40 to 120 km/h and four distinct rail irregularity profiles. The loading schemes used are empty, half-loaded, full-loaded. For simulations of the baseline scenarios, a combination of perfect wheels and new wheels (defined in Section 3.3) is used. The baseline scenarios consist of 55 simulations  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Baseline Scenarios**} & \multicolumn{2}{c}{**Damaged Scenarios**} \\ \hline \multicolumn{2}{c}{Perfect wheel} & \multicolumn{2}{c}{New wheel} & \multicolumn{2}{c}{Polygonal wheel} \\ \hline Vehicle & \multicolumn{2}{c}{five freight wagons of the Laagrs type} \\ \hline Number of & 3 & 1 (full load) & 1 (full load) \\ loading schemes & & & \\ \hline Unevenness profile & 4 & 1 & 3 \\ \hline Speed range & 40–120 km/h & 80 km/h & 80 km/h \\ \hline Noise Ratio & 5\% & 5\% & 5\% \\ \hline \multirow{3}{*}{Location} & \multicolumn{2}{c}{1 st wagon} & \multirow{3}{*}{1 st wagon} \\  & & 3 rd wagon & \\  & & 5 th wagon & \\ \hline \multirow{3}{*}{Harmonic orders (\(\theta\))} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & H6-8 \\  & & & H12-14 \\ \cline{1-1}  & & & H17-18 \\ \hline Amplitude ranges & \multirow{3}{*}{-} & \multirow{3}{*}{0.030–0.040 mm} & A1 [0.2–0.4] mm \\ (W) & & & A2 [0.6–0.8] mm \\ \hline Total analyses & 55 & 30 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Damaged and undamaged scenarios.  
Figure 6: Example of a generated track irregularity profiles.  
with perfect wheels and 30 simulations with new wheel profiles. The simulations with new wheel profiles are carried out at different locations, in the first, third, and fifth wagons. For the damaged scenarios, a total of 30 scenarios of polygonal wheels were generated with combinations of harmonic orders and defect amplitudes. For these simulations, a single defective wheel located on the first wagon on the right front wheel, and fully loaded wagons operating at a speed of 80 km/h are considered.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Gateway Discovery Process
#### 3.3.1 Receive Signal Strength (RSS\({}_{\text{NV}}\))  
This is the RSS of the NV, measured in decibel milliwatts (dBm) with respect to its connected eNB. The computation of the \(RSS_{NV}\) is similar to the way that the \(RSS_{IV}\) is computed. However, in this case, the \(RSS\) is measured by the NV, not the IV. Refer to Equation (2) for the \(RSS_{NV}\) computation.
# Non-Stationary Stochastic Global Optimization Algorithms
## 4 Results  
### Selection Scheme Formalization  
A Selection Scheme, is a method of selecting a group of individuals from a population [28]. Some studies of these schemes can be found in [29; 30; 31]. Many schemes define an individual selection mechanism \(\mathtt{s}1\colon\Omega^{\lambda}\to\Omega\), and selects a group of individuals by repeatedly applying \(\mathtt{s}1\). In this paper, we study the uniform, fitness proportional, tournament ([32]), roulette, and ranking selection schemes:  
1. A uniform scheme (Uniform1\(\colon\Omega^{\lambda}\to\Omega\)) gives to each candidate solution \(i=1,2,\ldots,\lambda\), the same selection probability \(p(x_{i})=\frac{1}{\lambda}\).
2. A fitness proportional scheme (Proportional1\(\colon\Omega^{\lambda}\to\Omega\)) gives to each candidate solution \(i=1,2,\ldots,\lambda\), a selection's probability \(p(x_{i})\) such that \(p(x_{i})<p(x_{j})\) if \(f\big{(}x_{j}\big{)}\lhd f(x_{i})\) and \(p(x_{i})=p(x_{j})\) if \(f(x_{i})=f(x_{j})\).
3. A tournament scheme (Tournament1\({}^{m}\colon\Omega^{\lambda}\to\Omega\)) of size \(m\) chooses \(m\) individuals using a Uniform scheme and selects an individual from these using a Proportional1 scheme, Tournament1\({}^{m}=\)Proportional1\(\circ\)Uniform\({}^{m}\).
4. A roulette scheme (Roulette1\(\colon\Omega^{\lambda}\to\Omega\)) is a fitness proportional one where \(p(x_{i})=\frac{rate(x_{i})}{\sum_{j=1}^{k}rate(x_{i})}\) with \(rate(x_{i})<rate(x_{j})\) if \(f\big{(}x_{j}\big{)}\lhd f(x_{i})\) and \(rate(x_{i})=rate(x_{j})\) if \(f(x_{i})=f\big{(}x_{j}\big{)}\). If \(f(x_{i})\geq 0\) for all \(i=1,2,\ldots,\lambda\) and maximizing then \(rate(x_{i})\) can be set to \(f(x_{i})\).
5. A ranking scheme (Ranking1\(\colon\Omega^{\lambda}\to\Omega\)) is a roulette one with \(rate(x_{i})=1+|\{x_{k}\colon f(x_{i})\lhd f(x_{k})\}|\).
6. A stud scheme (Stud1\(\colon\Omega^{\lambda}\to\Omega\)) chooses the best candidate and can be characterized for the next kernel \(K_{R_{p,q+\lambda}}=\pi_{\{1\}}\circ s_{\lambda,\lambda-1}\)
7. An Over Selection scheme as defined in [33] (OSelection1\(\colon\Omega^{\lambda}\to\Omega\)) is a roulette one with \(p(x_{i})=0.8/(\lambda*0.68)\) if \(ranking(x_{i})\lhd\lambda*0.68\) and \(p(x_{i})=0.2/(\lambda*0.32)\) if \(ranking(x_{i})\rhd\lambda*0.32\) where \(ranking(x_{i})\) is defined as \(ranking(x_{i})=1+|\{x_{k}\colon f(x_{i})\lhd f(x_{k})\}|\).  
**Proposition 5**.: _If \(\mathrm{S1}\colon\Omega^{\lambda}\to\Omega\) is a selection scheme with kernel \(K_{\mathrm{S1}}\) then \(\mathrm{S}\colon\Omega^{\lambda}\to\Omega^{\mu}\) has kernel \(K_{\mathrm{S}}=\oplus_{i=1}^{\mu}K_{\mathrm{S1}}\)._  
**Corollary 1**.: _If \(\mathrm{S1}\) is based on a probability function then \(K_{\mathrm{S}}\) is a kernel._  
**Corollary 2**.: _The Uniform, Proportional, Tournament, Roulette, Ranking, Stud and OSelection selection schemes have Markov kernels._
## 3 Results and Discussion
### Pressure Effect of VFeScZ (Z = Sb, As, P)
However, for type-I VFeScAs alloy we found that a band gap width increases to 0.175 eV at first and then decreases to 0 gradually because of a small peak appearing in the spin-up gap under zero pressure. And with the increase of pressure, the Fermi surface falls into the semiconductor gap because of the high-energy shift of the whole spin-up band. It is the reason for the anomalous gap phenomenon of VFeScAs. Figure 8: Density of states of type-I (**a**) VFeScSb, (**b**) VFeScAs, and (**c**) VFeScP at various pressures. Pressure effect causes a novel electronic structural transition from semiconductor or half-metal to SGS in Heusler alloy VFeScZ (Z = Sb, As, P). The system with less s or p electrons of the Z atom has a relatively small pressure phase transition point. If the pressure is applied beyond the transition point, all of these alloys will eventually be transformed into ordinary non-magnetic alloys. The pressure dependence of the band gap width of the VFeScZ alloy provides an opportunity to tune the electronic properties by means of external pressure. By gradually applying pressure, a narrow gap in a semiconductor can be converted to zero, gradually. It can be an efficient way to get an \"optimal\" SGS material. In addition, the pressure effect on the magnetism of the VFeScZ alloy is also discussed. In VFeScZ alloy, see Figure 10 for detail, with the increase of pressure from 0 to transition point 132 GPa, 58 GPa, and 32 GPa for VFeScSb, VFeScAs, and VFeScP, respectively, the magnetic moments of V and Sc atoms decrease gradually, while those of Fe and Z atoms increase gradually. But, the total magnetic moments of 3 \(\mu_{\text{B}}\) almost remained unchanged.
## 4 Results
### Fluid Composition  
The results of the FiCs analyses were jointly examined due to the common features of the fluid remnants trapped in the laumontite and sidewall rock samples. The obtained mass spectra revealed the presence of non-hydrocarbon and hydrocarbon species within the liberated volatiles (Figure 12). The compound groups formed clusters on the spectra, enabling the recognition of individual components and/or molecular fragments present in certain compounds. Predominantly, two different fluid compositions characterised the mass spectra of the fluid residues along the investigated core section (Figure 12), with the volatile non-hydrocarbon species being generally dominated by H\({}_{2}\)O and CO\({}_{2}\) (Figure 12a,b). In the investigated mass range, the hydrocarbon fraction contained compounds with a maximum of seven carbon atoms and with a dominance of CH\({}_{3}\)+ fragments, with maximum part representing methane. In addition, aromatic hydrocarbon species were detected between the described clusters, highlighting the presence of benzene and toluene in the trapped oils (Figure 12c). The fluid remnants exclusively characterised by non-hydrocarbon or hydrocarbon species complied with two extreme cases; however, their mixture was more common in the remained fluids, demonstrating the co-occurrence of the volatile species (Figure 12b).  
Figure 11: Representative fracture network model. (**a**) Fractal dimension (\(D\)) log. Red arrows denote the intervals with high fracture density. (**b**) Representative vertical section of the total fracture network around the A-1 well. (**c**) Communicating part of the same fracture network.  
In both sample sets, the normalised contributions of hydrocarbon and non-hydrocarbon species were found to vary, especially in the sidewall rock samples. The variation had a fluctuating pattern, indicating an abundance of methane and water (Figure 13).  
Figure 12: Mass spectra of the fluid remnants characterising (**a**) non-hydrocarbon and (**b**,**c**) hydrocarbon species.  
Figure 13: Fluid inclusion chemost stratigraphic log of the A-1 well.  
There was also a definite appearance of H\({}_{2}\)O isolate intervals with a dominance of aqueous fluids. Within these regions, benzene, together with higher-carbon-numbered hydrocarbons, also occurred, though hydrocarbons existed independently too, indicating their predominance. Focusing on the laumontite samples, the hydrocarbon-bearing fluids clearly established a dominant phase, with a minor contribution from aqueous fluids. The maximum readings of benzene were a close match between the bulk rock and laumontite samples between 2061 and 2067 m, as well as 2072 and 2077 m, but the declared sections were assigned a maximum difference of 1 m (Figure 13).
# Digital Twin and Internet of Things--Current Standards Landscape
## 3 Standards for Digital Twins and the Internet of Things
### Digital Twin Definition Language  
The Digital Twin Definition Language (DTDL) [42] is developed by Microsoft and used in different products of their Azure services. Although not developed by an SDO, the DTDL is mentioned here because being already used in many commercial services offered by Microsoft like IoT Hub, IoT Central, and Azure Digital Twins. As the name suggests, the DTDL only covers the aspect of resource description and does not address resource discovery and resource access. There are two versions: Version 1 and Version 2. If not otherwise mentioned, we refer to Version 2 in this paper.  
In the DTDL, resources are called interfaces and can contain a set of telemetry, properties, commands, relationship, and components as shown in Figure 4. For identification of resources and their elements DTDL uses a special form of URIs called Digital Twin Modeling Identifier (DTMI) of the form <_scheme_>:<_path_>;<_version_>. Telemetry describes data emitted by a resource in the form of a data stream. It corresponds to a combination of events and property subscriptions in our general terms. Commands correspond to functions that can be invoked with optional input and output parameters. Components are a similar concept as the submodels in the AAS providing a way to structure functionality in re-usable blocks. DTDL uses a custom type schema called Digital Twin Schema Definition Language, which is compatible with popular serialization formats, including JSON, Avro, and Protobuf. It supports complex interlinking of resources through explicit modeling of relationships including min/max cardinality. DTDL also supports semantic annotation for the type and unit of properties and telemetries. Unfortunately, only predefined semantic annotations are supported, i.e., extensibility is not given. JSON and RDF are supported in serialization formats. DTDL does neither support geo-spatial, temporal, nor historical data. Although in Version 1 a schema for geo-spatial data is defined (based on GeoJSON), it is not mentioned in Version 2 and therefore remains unclear if it still applies.