esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization of Ionogels  
FT-IR spectra for wet and dried gel samples were collected using an attenuated total reflectance (ATR)/FT-IR spectrometer (ABB MB 3000, Canada) over the range of 4000-600 cm\({}^{-1}\) with a scan of 60 scans/s and resolution of 16 cm\({}^{-1}\). The gel samples were placed over a zinc selenide (ZnS) surface. The angle of incidence of the infrared beam onto to the surface was 45\({}^{\circ}\). The standard calibration of the instrument was done  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Codes** & **TEOS:H\({}_{2}\)O:H\({}_{3}\)PO\({}_{4}\)** & **IL** & **Gelation Time (min) a** \\ \hline IL 0 & 0 & 195 \\ IL 0.1 & 0.1 & 185 \\ IL 0.5 & 0.5 & 170 \\ IL 1.0 & 1:2:0.5 & 1.0 \\ IL 3.0 & 3.0 & 155 \\ IL 5.0 & 5.0 & 140 \\ IL 10.0 & 10.0 & 120 \\ \hline \hline \end{tabular}  
* obtained by monitoring sol–gel transition at room temperature (RT).  
\end{table}
Table 1: Formulations of the sol–gel-based silica ionogels.  
by measuring the spectra of polystyrene film over the crystal surface as a calibration standard. Thermogravimetric studies were performed using TG analyzer Model Q50 (TA Instruments) with a heating rate of 20 \({}^{\circ}\)C/min under N\({}_{2}\) atmosphere from 30 to 800 \({}^{\circ}\)C. The formation of a siloxane network structure in the prepared gel was characterized by using \({}^{29}\)Si solid-state NMR spectra, Bruker Avance 400 spectrometer, Germany, at a frequency of 79.49 MHz for \({}^{29}\)Si cross-polarization magic-angle-spinning (CP/MAS) NMR. The particle size measurements for the ground gel powder were performed using a Yobin Horiba, LB-550, particle size analyzer by making a dilute dispersion of silica particles in deionized water. The sol-gel transition was monitored using rheological experiments in a cone-plate geometry (CP 25-1) using an Anton Paar Physica rheometer (MCR301) with oscillatory tests. The measurements were performed at a constant frequency (1 Hz) and constant strain of 1% at 25 \({}^{\circ}\)C with time variation. X-ray diffraction patterns for the prepared gels were analyzed using an X-pert PRO X-ray diffractometer under Cu K\(\alpha\) radiation (\(\lambda\) = 1.54 A) under a voltage of 40 kV and a current density of 40 mA, scanning over a range of 3\({}^{\circ}\)-80\({}^{\circ}\) at a rate of 4\({}^{\circ}\)/min. Small-angle X-ray scattering (SAXS) measurements of gel samples were performed using a SAXSpace instrument from [PERSON] with the following specifications: collimation: line collimation; time: 30 s \(\times\) 40 frames (20 min); analysis: SAXStreat and SAXSQuant software; detector: Mythen 2 (1D detector); temperature: 28 \({}^{\circ}\)C; holder: solid sample holder; q range of 0.1-10 nm\({}^{-1}\) under Cu K\(\alpha\) radiation (\(\lambda\) = 0.071 nm). The structural morphology of the prepared silica gels was obtained using scanning electron microscopy (JEOL, JEM-7500F, Japan). TEM images were examined using a transmission electron microscope (JEOL, JEM-1200 EX, Japan).
# Study on the Aging Behavior of Asphalt Binder Exposed to Different Environmental Factors
## 3 Results
### Analysis of Outdoor Exposure on the Chemical Composition of Asphalt Binder
#### 3.3.1 Effect of Outdoor Exposure on the Structure Composition of Different Asphalt Binder
(**a**) Unaged; (**b**) TFOT aging; (**c**) O-aging; (**d**) O+UV-aging; (**e**) All-aging; (**f**) PAV-aging. Segmental integration was conducted according to the four attributed hydrogens of H\({}_{\rm A}\), H\({}_{\rm\alpha}\), H\({}_{\rm\beta}\), and H\({}_{\rm\gamma}\) in the spectra. The content of the four attributed hydrogens, H\({}_{\rm A}\), H\({}_{\rm\alpha}\), H\({}_{\rm\beta}\), and H\({}_{\rm\gamma}\), was obtained after normalization, as shown in Table 5.  
The \({}^{1}\)H-NMR spectra and the hydrogen content analysis results in Table 5 reveal that among the four attributed hydrogens, H\({}_{\rm\beta}\) exhibits the highest concentration, accounting for more than 50% of the total. This indicates that hydrocarbon elements in SBS-modified asphalt binder still predominantly manifest as carbon chains. The H\({}_{\rm A}\) content in SBS-modified asphalt binder experienced a relatively modest increase following short-term aging, signifying a rise in the presence of aromatic ring structures. Upon asphalt binder exposure, the pattern of hydrogen distribution mirrored that of virgin asphalt binder. The H\({}_{\rm A}\) content in the asphalt binder increased post-exposure, suggesting an increase in the aromatic ring structure after binder aging. The distribution of hydrogen elements in O-aged asphalt binder closely resembled that of O+UV-aged asphalt binder, with all-weather aging exerting a more pronounced impact on SBS-modified asphalt binder compared to virgin asphalt binder.
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 2 Theory of Clay Deformation under Cyclic Loading
### The Constant-Order Fractional Abel Dashpot  
Clay is characterized by its elasticity, viscoelasticity and plasticity over the course of the entire creep process as shown in Figure 3. The elasticity and plasticity of clay can be described by the hook body and the Newtonian dashpot, respectively, while the viscoelasticity of clay can be described by a constant-order fractional Abel dashpot, as shown in Figure 6.  
The constitutive relationship of the Abel dashpot is given as [45]  
\[\sigma(t)=\eta D^{\alpha}\epsilon(t) \tag{11}\]  
where \(\eta\) is the viscosity coefficient, \(0\leq\alpha\leq 1\), and \(D^{\alpha}\) indicates fractional differentiation.  
When \(\alpha=1\), the Abel dashpot in Equation (11) is a Newtonian dashpot representing an ideal fluid; and the element becomes a spring representing an ideal solid; when \(0<\alpha<1\), the Abel dashpot can be considered a comprehensive component describing the nonlinear strain processes of the material between the spring and the Newtonian dashpot.  
Considering \(\sigma(t)=\sigma_{0}\) (constant stress) in Equation (11), and taking the initial time \(t=0\), by applying the fractional integral to both sides of Equation (11), the equation expression of the Abel dashpot is obtained thus:  
\[\epsilon(t)=\frac{\sigma_{0}}{\eta}\frac{t^{\alpha}}{\Gamma(1+\alpha)} \tag{12}\]  
where \(\sigma_{0}\) is the normal level of stress.  
Figure 6: The basic mechanical elements.  
By substituting \(\sigma_{0}=20\) kPa and \(\eta=40\) kPa-h\({}^{\alpha}\) into Equation (12), a series of different \(\alpha\) creep curves are generated, as shown in Figure 7. Under the condition of equal duration, the greater the order, the greater the creep strain. However, creep strain is not susceptible to the order in the initial stage; with increase in loading duration, the differences among different creep strain curves caused by the order become more distinct.
## 4 The Arrowhead-EP of a Real Use Case
### The Use Case Described with the Arrowhead-EP Model  
Figure 8 illustrates the Arrowhead-EP model of the entire EP adopted by the stakeholders involved in the life cycle management of the SBS. The SBS vendor (StkH-1) leads the value chain and is involved in every phase of the EP, across the entire life cycle of the product, from early design to manufacturing and final retirement. In addition, the vendor of the IoT integration framework (StkH-2) leads the digitalisation of this appliance. The telecommunication provider (StkH-3) is a service provider, while the app developer (StkH-4), the installation technician (StkH-5), and the final user (StkH-6) utilise the services and functionalities offered by the SBS at different levels depending on their roles.  
StkH-1 designs and develops the electromechanical parts of the boiler and creates the requirements to be provided to StkH-2 (connection c1), which will develop the embedded control unit (ECU) that allows the boiler's digitalisation. StkH-1 is also involved in the _Deployment and Commissioning_ of the SBS and in its remote monitoring during _Operations and Management_. StkH-1 exploits the information collected from the SBS fleet (c25) to ensure safe operation, plan maintenance activities, improve the product, and guarantee its evolution. Eventually, StkH-1 is involved in the professional training of maintenance operators and the production of documentation for the other partners of the value chain, C22 and C26, and the final users.  
The IoT platform vendor (StkH-2) designs and develops the SBS ECU, the IoT framework running on the edge in the ECU, and its counterpart, the IoT integration platform that manages the entire fleet of SBSs. The IoT framework and integration platform provide services and APIs that allow the development of the application's business logic and integration at the enterprise level. Thus, the IT department of the smart boiler manufacturer (StkH-1) can integrate the fleet of smart boilers in its organisational processes, providing remote control, preventive and predictive maintenance, and collecting precious information about the product life cycle.  
The application developer (StkH4) adopts these services and APIs to develop final user applications intended for the maintenance operator and the consumer.  
This example demonstrates that the Arrowhead-EP model is a good solution to easily and flexibly describe the complexity of the management of an SoS product life cycle. The Arrowhead-EP model illustrates the dynamics of the EP, the relations and the synergies  
Figure 8: Arrowhead-EP (AH-EP) description of the Smart Boiler use case using a graph based on the Arrowhead-EP ontology.  
between the stakeholders, and their involvement in the EPPs of different EPs, even when some of them have to be considered \"black boxes\", because the stakeholder does not adopt the Arrowhead-EP due to the absence of an agreement between the stakeholders or due to intellectual property issues (see, e.g., the EP of StkH-3 and part of the EP of StkH-1).
## 5 Field Application and Effect Evaluation
### Analysis of Mine Pressure Appearance
#### 5.2.2 Surrounding Rock Deformation Analysis  
Starting 200 m ahead of the working face, two measuring stations began to monitor the amount of roadway displacement. Station a and station b respectively. Summarizing the data recorded at each time, a curve was plotted for the displacement of the roadway as a function of the distance from the working face, as shown in Figure 18.  
The advanced bearing pressure cannot be detected when the measurement point is far from the working face. When the working face moves forward, these measuring points are gradually affected by the advanced bearing pressure, and then the surrounding rock deformations gradually increase. The 3606 track roadway's surrounding rock deformation and relative deformation rate are both minor, according to the surrounding rock displacement observation. This scheme successfully controlled the deformation of the roof and two ribs.
## 5 Conclusions and Future Works  
In this paper, a numerical model is proposed to simulate the dynamic motion of a structural cable. In the numerical model, SPH is used. First, the numerical model was validated with for the case of which static solutions were available. Then, another case was used to validate the model under dynamic motion. Although, there are many problems available in the literature in validating the model, such approaches are limited in showing the real capability of the proposed numerical method. For example, the results of experiments in the literature were presented only for a limited time, or the displacement of only one point on the cable was presented. For these reasons, novel experiments were conducted. The main contribution of the experiments is to present the displacement of the whole cable profile by giving the displacement history of intermediate points on the cable. In addition, an extended time history analysis is provided to investigate the dynamic motion of the cable which does not end for a long time after the excitation vanishes.  
In the numerical model, a proper damping parameter is proposed. Damping becomes effective when the excitations are removed. Therefore, the results while the excitation is still applied are very similar, independent of the damping. However, oscillations observed in the experiments after the excitation ends cannot be captured in the numerical model without a proper damping parameter. After the addition of the damping parameter, results closer to the experimental data were obtained. To the best of the authors' knowledge, this is the first study that investigates the damping effect of the cable.  
Although validations show that SPH, with a proper damping parameter, can be used to model the dynamic motion of the cable satisfactorily, there are some limitations of the numerical model. The limits of the damping parameter should be investigated in detail. Since more computational study should be performed and new experiments should be carried out to investigate the effect of damping parameters appropriately, it is left for a future study. In addition, the current version of the proposed numerical model can simulate the single-segment cables. Another future work will be focused on the application of the numerical model for multi-segment cables which includes additional intermediate roller supports or pulleys resulting in a frictional force to be modeled.  
Investigation, A.E.D. and A.D.; Methodology, A.D.; Software, A.E.D.; Validation, A.E.D. and A.D.; Writing--original draft, A.E.D. and A.D. Revision, A.E.D. and A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 15: Displacement of P1 under Type1 excitation for experiment (black) and numerical solution without damping (blue) and with damping (\(\omega=0.04\)) (red).
## 3 Results and Discussion
### Pressure Distribution of Different MPPs  
In the 6 m\({}^{3}\cdot\)min\({}^{-1}\) SSC, the water film pressure distributions at leading flank under rated discharge pressure were calculated under \(\delta\) = 0\({}^{\circ}\) and \(\varphi_{\text{gr}}\) = 14.9\({}^{\circ}\). Under this gate-rotor rotating angle, the property that the instantaneous meshing line cross different columns of MCT can be well demonstrated and the pressure of the compression chamber is 325300 Pa. As shown in Figure 11, the calculating data are plotted to a 3D surface for every MPP. The 3D surface is projected to the Y\({}_{0}\)OZ\({}_{0}\) plane and a 2D pressure distribution of the tooth flank is available.  
For SSLT, the peak value of the water film pressure is 332300 Pa and emerges at tooth tip. Although the relative velocity gets its minimum at tooth tip, the included angle of the wedge reaches its maximum at the same location. Along with the increases of \(l\), the hydrodynamic effect declines gradually until it vanishes at \(l\) = 22.3 mm. As \(l\) > 36.1 mm, the tooth is out of the screw and there is no liquid film. Since the meshing line is constant, the water film in the 2D plane is a thin and long rectangle. Due to its weak hydrodynamic effect and small action area, the thrust force to the leading flank is small.  
For SCT, the peak value is 362000 Pa and also emerges at tooth tip. The acting area of the water film presents a triangle in the 2D plane since the meshing point varies from bottom to top of the tooth in the tooth thickness direction (\(t_{\text{h}}\)) along with the increment of \(l\). Compared to the SSLT, the hydrodynamic effect is much stronger and the acting area is bigger.  
For MCT, the peak value is 431102 Pa and still emerges at tooth tip. As \(l\) < 25.8 mm, the meshing line is on the column near bottom of the tooth and the action area is very wide. As \(l\) > 25.8 mm, the meshing line is on the column near top of the tooth and the action area is narrow. Under this \(\varphi_{\text{gr}}\), the MCT has the strongest hydrodynamic effect and the biggest action area among the 3 MPPs.  
It can be found that the hydrodynamic effect declines from tooth tip to tooth root for all types of MPP. It is mainly because both the contraction ratio of the wedge and the relative velocity decrease continuously with the increment of \(l\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline Discharge capacity & & & \\ (m\({}^{3}\cdot\)min\({}^{-1}\)) & 3/6/12 & & \\ Rated motor power & & & \\ (kW) & 18.5/37/75 & & \\ Rated motor speed & & & \\ (rpm) & 2970 & & \\ Rated discharge & & & \\ pressure (MPa) & 0.8 & & \\ Gap width \(h_{0}\) (mm) & 0.04 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the prototype.
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 7 Current Situation and Development Trend
### Development Trend
#### 7.2.5 5G Networks  
With the ever-growing development of networks, a 5G network has emerged. Compared with the 4G network, the innovation of the 5G network lies in: micro-base station, beam shaping, and D2D [62]. Reference [58] reported that PSO is used to deal with the problem of joint uplink subcarrier distribution and power control in the D2D underlying cellular network in 5G. Regarding the application of SI to 5G, there may be the following research direction: (1) 5G network is one of the core technology of massive MIMO, due to the intelligent algorithm in solving a large space, global optimum, nonlinear complex problems better than the traditional algorithms, so a lot of application examples of SI in MIMO (especially) based on PSO, follow-up can be further in this direction to massive MIMO; (2) as 5G network is oriented towards a variety of scenarios, different scenarios have different requirements on network mobility, security, delay, and other requirements. Therefore, it is necessary to divide the physical network into multiple virtual networks, which falls into the topic of network slicing technology. However, when a large number of users and devices increase dramatically, the virtual machine resources in the network slice may not meet the demands of users, so the network slice function migration mechanism is strongly required. The migration problem is an NP-hard combinational problem, which is suitable to be solved by the SI algorithm. Moreover, in the 5G-based migration problem, a low time delay has to be seriously considered. Both PSO and ABC algorithms have the advantage of fast convergence speed. We believe that introducing SI algorithms into the 5G migration problem is a feasible and advantageous research direction.
## 1 Introduction  
A bridge deteriorates because of the applied loads and surrounding environment during its service life. This causes the original performance of the bridge to degrade. The bridge structure should be maintained to retain the required performance to satisfy its intended function. However, even when bridges are constructed according to the same design standard, site conditions (e.g., traffic and corrosion) vary. The site environment should be considered for the maintenance of a bridge. Current bridge performance evaluation and maintenance procedures do not consider such aspects; a single method of repair and reinforcement is adopted according to the target performance determined in the design step. Thus, the current level of maintenance work is unsatisfactory.  
Research on evaluating the performance of a structure has mainly focused on safety evaluation of aging parts, evaluation of the system, and maintenance. Recent studies have focused on using a reliability-based evaluation technique based on statistical data to examine the loads and resistance capacities of actual structures. Several studies have followed the design concept suggested by [PERSON] [1], which uses the average and variance of a random variable to quantitatively evaluate the resistance capacity of a structure. [PERSON] et al. [2] suggested evaluating the safety of an existing structure by considering the remaining strength of a deteriorated structural member and the strength degradation regarding the load through the application of reliability theory. [PERSON] et al. [3] used probability theory to quantitatively analyze the safety of an existing reinforced concrete member. However, these studies did not consider the site conditions reflecting the traffic characteristics of the actual bridge. Several studies have developed models to predict the performance degradation of a bridge. [PERSON] et al. [4] represented the performance degradation of a bridge as a nonlinear continuous function and developed a performance level model and reliability index model. [PERSON] and [PERSON] [5] analyzed the changes in the performance degradation of a bridge depending on the settings of the maintenance standards. However, because these studies were limited in scope, these approaches are similarly limited in their ability to evaluate performance degradation reflecting the corrosive environment and traffic characteristics of an actual bridge.  
This paper presents a performance degradation and reliability evaluation method based on the history of actual bridges, reflecting various site conditions (e.g., corrosive environment, traffic characteristics), which has not been considered in previous studies. Corrosive environmental conditions are classified as mild, normal, and severe. Traffic characteristics are classified as light, normal, and heavy. The method also accounts for the probabilistic characteristics of the materials and section properties of the bridge members.
## 2 Background
### Overview of DoS and DDoS Attacks on CC  
A DoS attack is a malicious attempt by an adversary using a single attacking host to prevent the targeted victim from accessing the required services or a node providing a service to its consumers. On the other hand, a DDoS attack involves multiple attacking hosts flooding the victim's network or host with attack packets, resulting in a distributed multi-point attack [16].  
There are several types of DoS attacks, and the most popular is the one targeting the client's network bandwidth or connectivity. Bandwidth attacks significantly impact network performance by depleting all available network bandwidth, delaying or preventing users' requests from being fulfilled. Meanwhile, connectivity attacks overwhelm the victim's server with many application-layer requests, exhausting all available resources. As a result, the server stops responding to legitimate user requests.  
DoS attacks are classified as follows: (i) flood attack involves an adversary sending a large volume of continuous stream of packets to the victim, depleting the victim's resources and exhausting the victim's bandwidth. (ii) vulnerability attack involves an adversary exploiting a vulnerability in the victim's system and sending crafted messages to cause a DoS. The DDoS attack is frequently carried out by flooding the targeted system or network with a huge volume of traffic from multiple sources [17].