esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bridge Deck Waterproof Cohesive Layer Interlayer Design Method
### Axle Load Conversion Method Based on Shear Fatigue Damage  
Converting the number of actions between different axle loads follows the principle of equivalence, i.e., the bridge deck pavement structure calculated according to the design index with different converted different axle loads is always the same [33]. Therefore, one can derive an equation for converting the shear stress between the waterproof cohesive layers of the bridge deck to an axle load, allowing the design index to be determined.  
The axle load conversion method for a bridge deck pavement system based on fatigue equivalence is as follows: BZZ-100 (horizontal force coefficient \(f\) is taken as 0.15) is used as the standard axle load based on bond shear fatigue axle load conversion [34]. The maximum interlaminar shear stress ratio with different axle loads is defined [35] in Equation (7):  
\[\frac{\tau_{xy1}}{\tau_{xy2}}=\left(\frac{P_{1}}{P_{2}}\right)^{b} \tag{7}\]  
According to the shear fatigue test results for waterproof bonding materials, the relationship between shear fatigue life of the bonding materials and interlaminar shear stress can be expressed as shown in Equation (8):  
\[N=X_{N}\tau^{-Y_{N}} \tag{8}\]  
Based on the principle of equivalence for axle load conversion, the shear stress ratio under different axial loads is shown in Equation (9):  
\[\frac{\tau_{1}}{\tau_{2}}=\left(\frac{N_{1}}{N_{2}}\right)^{-\frac{1}{\tau_{N}}} \tag{9}\]  
Equation (10) can be obtained by combining Equations (7) and (9), where \(bY_{N}\) is equivalent axle load conversion factor:  
\[\frac{N_{1}}{N_{2}}=\left(\frac{P_{2}}{P_{1}}\right)^{bY_{N}} \tag{10}\]  
The different axle load ratios and their corresponding maximum shear stress ratios can be calculated, and regression can be used to determine the average standard axle load conversion index \(b\). Here, we determined \(b\) = 0.53. The fatigue life equation for different bonding materials was presented in Section 3.3, and the exponent \(Y_{N}\) can be determined. The \(bY_{N}\) values for different bonding materials are shown in Table 12.  
To ensure that the axle load conversion result is reliable, \(bY_{N}\) = 3.6 was selected as the equivalent axle load conversion factor for designing waterproof cohesive materials to prevent interlayer shear fatigue. The calculated result is smaller than the current asphalt pavement conversion index of 4.35 according to the deflection axis; thus, axle load changes have less influence on the interlayer than on permanent deformation and fatigue cracking in the pavement.
# Analysis, Evaluation and Simulation of Railway
## 3 Results and Discussion
### Freight Trains  
Results obtained for KPIs in freight trains (with the same considerations as in Table 12) are presented in Table 14.  
Results obtained for KPIs in freight trains considering that regenerative braking can be used during the whole itinerary (and by both locomotives) are presented in Table 15.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Locomotive**} & **KPI 1** & **KPI 2** & **KPI 3** & **KPI 4** & **KPI 5 kWh** \\  & & **kWh/kN-km** & **kWh/train-km** & **kWh/geost-km** & **kWh/pkm** & **gen/kWh cons** \\ \hline \multirow{3}{*}{Alcazar-Cartagena} & S-334 & 0.0883 & 15.58 & 0.0594 & 0.1016 & 0.1704 \\  & S-730 & 0.0432 & 9.47 & 0.0361 & 0.0618 & 0.3070 \\  & S-252 & 0.0113 & 3.40 & 0.0130 & 0.0222 & 0.5148 \\  & S-253 & 0.0090 & 2.70 & 0.0103 & 0.0176 & 0.4918 \\ \hline \multirow{3}{*}{Cartagena-Alcazar} & S-334 & 0.1314 & 23.19 & 0.0885 & 0.1513 & 0.0952 \\  & S-730 & 0.0931 & 20.40 & 0.0778 & 0.1331 & 0.1465 \\ \cline{1-1}  & S-252 & 0.0231 & 6.93 & 0.0264 & 0.0452 & 0.3859 \\ \cline{1-1}  & S-253 & 0.0207 & 6.21 & 0.0237 & 0.0405 & 0.3304 \\ \hline \hline \end{tabular}
\end{table}
Table 13: KPIs for passenger trains with regenerative braking available in the whole itinerary.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Locomotive**} & **KPI 1** & **KPI 2** & **KPI 3** & **KPI 4** & **KPI 5 kWh** \\  & & **kWh/kN-km** & **kWh/train-km** & **kWh/geost-km** & **kWh/pkm** & **gen/kWh cons** \\ \hline \multirow{3}{*}{Alcazar-Cartagena} & S-334 & 0.0916 & 16.16 & 0.0182 & 0.0323 & 0 \\  & S-252 & 0.0180 & 5.40 & 0.0061 & 0.0108 & 0.0521 \\ \hline \multirow{3}{*}{Cartagena-Alcazar} & S-334 & 0.1782 & 31.43 & 0.0353 & 0.0629 & 0 \\  & S-252 & 0.0304 & 9.11 & 0.0103 & 0.0182 & 0.1586 \\ \hline \hline \end{tabular}
\end{table}
Table 14: KPIs for freight trains with regenerative braking only between Alcazar and Chinchilla.  
In the case of freight locomotives, reports from the Spanish Government [74] calculate general data of the consumption rates, but it is not segregated in the different routes of the Spanish Railway System. There are reported values of 27.9 kWh/train-km for KPI 2, 0.03 kWh/gross tkm for KPI 3 and 0.07 kWh/net tkm for KPI 4. These values are slightly higher than those calculated in the present study for the diesel locomotive S-334 in the Alcazar-Cartagena direction, although there is not a big difference in the opposite direction (see Table 14: KPI 2--31.43 kWh/train-km; KPI 3--0.035 kWh/gross tkm; and KPI 4--0.0629 kWh/net tkm).  
Differences between both results could be explained in the peculiarities of this specific railway route and locomotive compared with the average characteristics of the Spanish railway freight transport (notice that mountain areas in Spain cover 20 to 40% of territory, and railway profiles are quite difficult in some areas, like in Italy, Norway or Switzerland railways).
## 2 Background
### Discrete Event Simulation (DES)  
A discrete event simulation models the operation of a system as a discrete sequence of events in real time. Each event occurs at a particular moment and marks a state change in the system. Between consecutive events, no changes in the system are assumed to occur; therefore, the simulation time can directly jump to the occurrence time of the next event, which is called the next-event time progression. The DES models are considered a specific  
Figure 1: Expansion operation in RRT algorithm.  
type of dynamic simulation model. As time progresses in the discrete parts, these models are processed based on the critical events occurring in the model. The operation of a DES model commences with a specific event, known as the 'initial event,' which sets the simulation in motion. This event could be anything from the initiation of a manufacturing process, the opening of a store, or the start of a project. Following this, subsequent events are triggered as a result of this initial event, like a domino effect, leading to the occurrence of multiple interrelated events [34]. DES has had numerous advantages for engineers in different industries, among which the following can be mentioned [35]:  
* Simulation paves the way to study and test a complex system.
* It makes feasible any hypothesis about how or why specific phenomena occur.
* It leads to flexibility in time management since it allows the increase or decrease in the pace of phenomena through extension or compression.
* It can create a helpful approach and specify the most critical variables by assessing different conditions of simulation (changing the inputs and observing obtained outputs).
* It dramatically helps with the formulation and confirmation of analytical solutions.
* Optimizing the production system can decrease the time and costs during the project cycle.
## 4 Conclusions  
A series of 12 novel fluorinated heterocyclic salts containing an asymmetric 1,2,4-oxadiazole moiety connected through its C(5) or C(3) to an \(N\)-alkyl-pyridinium unit and a perfluoroheptyl chain as cation and iodide, bromide and bis(trifluoromethane)sulfoinimide as counterions, has been synthesized. The physico-chemical properties have been studied by means of different techniques such as differential scanning calorimetry and polarized optical microscopy. The precursor salts containing iodide and bromide as counterions did not exhibit any liquid crystalline phase behavior--except for **2a**. However, after metathesis, the salts containing bis(trifluoromethane)sulfoinimide as the counterion (**1d-f**, **2d-f**) can be classified as enantiotropic smectic ionic liquid crystals. The thermotropic behavior, in particular the mesophase range of these salts, is a function of the linkage of the pyridinium ring on the 1,2,4-oxadiazol C(3) or C(5) and the alkyl chain length. In fact, the compounds linked to the more conjugated part of the oxazolic part C(5) form a wider mesophase range with respect to the salts linked to C(3). Furthermore, it can be assessed that the mesophase range increases with increasing alkyl chain length. All salts showed a melting point lower than 100 \({}^{\circ}\)C.  
We can conclude that these ILCs are promising candidates for innovative applications. In future studies, they will be investigated as components for smart materials, ion-conductive batteries, and dye-sensitized solar cells.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app112110347/s1](https://www.mdpi.com/article/10.3390/app112110347/s1)]([https://www.mdpi.com/article/10.3390/app112110347/s1](https://www.mdpi.com/article/10.3390/app112110347/s1)), \({}^{1}\)H-NMR spectra of **1**, **1a-c**, **2** and **2a-c** (S1-S8), Synthesis and analytical data for **1a'** (S9). Polarized optical microscopy images of **1d-f** and **2d-f** (S10 and S11).  
Conceptualization, I.P.; methodology, I.P.; formal analysis, M.S.W., G.L. and A.P.P.; investigation, M.S.W.; resources, A.P. and I.P.; data curation, M.S.W.; writing--original draft preparation, M.S.W.; writing--review and editing, M.S.; supervision, I.P. and M.S.; funding acquisition, I.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Palermo, grant number FFR2019 to [PERSON].  
Not applicable.  
The authors declare no conflict of interest.
## 6 Discussion
Indeed, download of such data by the C&C only happens when there is need to display images on the smart glasses (and/or on a security GUI). Moreover, the images of the commuters are anonymized by blurring the upper (approximately 20%) part of the detection, which usually contains the face, and the 3D Video system captures only three-dimensional coordinates of the tracked persons (i.e., no video or image snapshots). The secure communication is natively supported by MQTT, such as client authentication and encrypted communication. The extent to which such security measures may impact on the system performance will be assessed as future work. \"Usability\". The architecture supports processing and packaging of collected multi-source data into a single product of actionable information delivered to the operator. Moreover, as an innovative feature of the system is the support to security guards on field by means of smart glasses, usability of such technology is a key aspect which has been directly and positively evaluated at the BCT by the Italian police. \"Modularity\". This is the main feature of the architecture that allows fusion of disparate technologies into a single modular ensemble, which favor reuse. Furthermore, utilizing a modular \"OR\" type interface for threat detection is advantageous in data aggregation since it permits the application to adapt flexibly to more scenarios, including, but not limited to, for example, when one sensing opportunity is not present or possible. \"Reuse\". Such an aspect has to be discussed for each component of the system. The C&C has been designed to be: independent from specific sensor and video systems, as the messages provide final detections results, and data fusion only refers to attribution of an object to a person and to combination of the sensor results for alarm triggering; independent from geometrical/environmental characteristics of the place where devices are deployed, the only constraint is that the flux needs to be one-directional. The modularity of the internal architecture of the C&C allows one to re-use the data fusion component with different tools. In this respect, the MQTT broker, the Security client (including the smart alarm management system), and the Monitoring system could each be replaced by a different tool. The two video systems are also reusable. The 2D Video re-identification component can be installed in new environments due to rapid re-training [23]. This can support tracking and fusion in a region even where the field of views of the cameras are not overlapping. In addition to crowd management solutions, the 3D Video monitoring methods can be utilized in other industrial use cases, including, but not limited to, for example, human mobility and task monitoring in industrial and construction domains providing task monitoring and improved work safety. In addition, location information originating from other sensors, such as, for example, millimeter radar and UWB, which can be fused for obtaining more accurate location and person activity information.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 2 Image Watermarking Background
### Transmission Phase
#### 2.3.2 Intentional Attacks  
These are specific manipulations meant to intentionally destroy or fake the watermark in order to prevent its extraction/detection. All of the attacks presented above can be used for this purpose. We also mention:  
**Cryptographic attack:** The main goal of this attack is to crack the security codes and algorithms used in watermarking methods in order to remove the inserted watermark  
Figure 6: Detection by additive method.  
information. In this case, the attacker tries to identify the embedded secret key using exhaustive search techniques. This type of attack possesses high computational costs and it is limited, and therefore it is employed less frequently.  
**Protocol attack:** The idea behind this attack is to create document ownership ambiguity. Some protocol attacks are based on invertible operations, in which an attacker subtracts their watermark from the watermarked document and claims its ownership. Another type of protocol attack is the copy attack [45]. In this case, the goal is to remove the watermark from one container and place it in another target document. In all cases, the attacker can claim for the ownership of both the original and watermarked image.  
The second type of attacks is more dangerous. It primarily relates to system security. However, this type of attack is difficult to perceive and thus is omitted in most of the works reviewed. The attack of the first type is considered successful if the watermark is damaged. Accordingly, several counter-techniques have been developed by embedding in the most prominent features of the container. This goes against the concept of improving watermark imperceptibility. Thus, striking a balance between imperceptibility and robustness is a major challenge in creating a watermarking outline.
# Implementation of Digital Twin for Engine Block Manufacturing Processes
## 3 Materials and Methods
### Development of Communication Interface  
Data acquisition represents a crucial part of implementing the DT of engine block manufacturing processes. In order to obtain real-time status data of the production line, programmable logic control (PLC) sensors were used for raw data acquisition. Data were gathered from the distributed PLCs  
Figure 3: Development of simulation model in the Tecnomatix Plant Simulation environment.  
(18 collection points) according to the configurable trigger conditions by data logger (Softing Industrial Automation GmbH, echocollect e, Haar, Germany). Verification of the PLC signals was performed by a software system (Autem GmbH, PLC-Analyzer pro 6, Emden, Germany). Subsequently, collected data were transferred into the local Structured Query Language (SQL) database. Two types of network protocols were used: Transmission Control Protocol/Internet Protocol (TCP/IP) as transport protocol and Siemens S7 as application protocol. TPS has an Open Database Connectivity (ODBC) interface that is able to retrieve data of events from the physical layer via the cloud-based database. Data exchange between the SQL database and the TPS software was allowed using the object SQLite. Internal cloud platform was used as a cloud-based information repository. Used real data are collected and then implemented into the production line DT. Collected data from the production line include: the position of the robot, the occupancy of buffers in front of the CNC and inside the CNC, the occurrence of workpieces on conveyors, the handling time of the robot, the machining time of the CNC, and the time from the placement of the workpiece at the input of the conveyor to its exit from the conveyor. Synchronization between the physical and virtual worlds is achieved using PLC controllers, which are analyzed and tested by the external test software. DT mapping diagram between layers is shown in Figure 4.
## 2 Materials and Methods
### Statistical Analysis  
The measurement results were statistically processed using Statistica 13.3 software (StatSoft Inc., Tulsa, OK, USA). The data were analyzed using procedures that allowed the following: (1) statistical evaluation of the level and variability of physicochemical and electrical parameters; (2) identification of differences between the analyzed features of the 7 varieties (linden, rapeseed, buckwheat, goldenrod, phacelia, multifloral, and acacia) and 16 species (\(p<0.05\)) of honey; and (3) determination of correlations between dependent variables (variables: cluster 1 and 2) and independent (electrical) variables and then, on their basis, indication of the usefulness of electrical parameters for controlling selected quality and health-promoting parameters of the honey varieties studied.  
The analysis (ANOVA, Tukey HSD test) was aimed at distinguishing homogeneous groups at the alpha = 0.05 significance level. The Tukey HSD test (multiple comparisons  
Figure 1: RCC equivalent model for measuring of electrical properties of food products (Z—impedance; R—resistance; C\({}_{\text{s}}\)—series equivalent capacitance; C\({}_{\text{p}}\)—parallel equivalent capacitance; M—measuring device) [55].  
of means) allowed determination of which groups differed significantly from each other. As a result, honeys with similar electrical and physicochemical properties were grouped together, which may indicate similarities in the chemical composition or properties of the samples tested.  
The results of [PERSON]'s HSD test for electrical and physicochemical variables indicate that honey groups (marked as: a, b, c,...) can be classified based on their physicochemical and electrical properties. Homogeneous groups allow drawing of conclusions about similarities or differences between samples, which can be useful for assessing the quality of honey depending on its chemical composition and electrical properties.  
The first stage of analysis involved grouping the studied parameters using [PERSON]'s agglomerative method. This method was used to ensure a system with minimal differentiation of parameters within clusters [59]. Grouping was performed for standardized data in order to increase data comparability (eliminating distortions of further statistical analyses resulting from differences in scale) and to increase the accuracy of calculations. Two clusters of parameters were distinguished (cluster 1 and 2) within the physicochemical parameters studied (qualitative and health-promoting). These actions were taken to reduce the number of variables in the next stage.  
In the second stage of analysis, the obtained average values for clusters were used in the analysis of correlations between selected qualitative and electrical parameters of honeys, measured at the frequencies of 100 Hz, 1 kHz, 10 kHz, 100 kHz, 1 MHz, and 2 MHz.
# Optimization and Application of Drilling Parameters Based on Gas-Solid Coupling Simulation
## 2 Coupled Gas-Solid Modeling of Gas Extraction
### Mathematical Model
#### 2.2.3 Coal Body Deformation Control Equation  
After the coal body is affected by mining, the original stress equilibrium state is broken, and the coal body is deformed under the influence of mining stress and ground stress. The geometric equation of the coal body is shown in Equation (14):  
\[\varepsilon_{ij}=\frac{\mu_{ij}+u_{ji}}{2} \tag{14}\]  
where \(\varepsilon_{ij}\) is the coal body strain tensor; \(\mu\) is the displacement component; and \(i\) and \(j\) are the horizontal and vertical directions, respectively.  
Gas-bearing coals are in a state of stress equilibrium. The coal body stress balance equation is shown in Equation (15):  
\[\sigma_{ij,j}=-F_{i} \tag{15}\]  
where \(\sigma_{ij,j}\) is the stress tensor component and \(F_{i}\) is the volumetric force component.  
In the previous section, it is assumed that the coal rock body is isotropic material, and the effect of temperature change is neglected; therefore, its deformation follows [PERSON]'s law, and in the three-dimensional coordinate system, the deformation components of the coal rock body are the same in the \(x\), \(y\), and \(z\) directions. The coal body eigenstructure equation is shown in Equation (16) [24]:  
\[\varepsilon_{ij}=\frac{\sigma_{ij}}{2G}-\left(\frac{1}{6G}-\frac{1}{9K} \right)\sigma_{\bar{y}}\delta_{\bar{y}}+\frac{\alpha}{3K_{m,i}}P_{m,i}+\frac{ \beta}{3K}P_{f,i}\delta_{\bar{y}}+\frac{\varepsilon_{s}}{3}\delta_{\bar{y}} \tag{16}\]  
where \(G=E/2(1+\upsilon)\) is the shear modulus of the coal rock mass; \(K=E/3(1-2\upsilon)\) is the bulk modulus of the coal rock mass; \(\varepsilon_{s}\) is the adsorption strain of the coal rock mass; \(\alpha=1-K/K_{m}\) and \(\beta=1-K/aK_{m}\) are the Biot effective stress coefficient of the coal matrix and fissures, respectively; \(E\) is the elastic modulus of the coal; and and \(\upsilon\) is the Poisson's ratio.  
The coal body deformation control equations can be obtained by associating Equations (14)-(16) [23]:  
\[Gu_{i,j}+\frac{G}{1-2\upsilon}u_{jji}-\alpha P_{m,i}-P_{f,i}-K\varepsilon_{s,i }+F_{i}=0 \tag{17}\]The combination of Equations (9), (13) and (17) is the gas extraction gas-solid coupling model.
## Abbreviations  
\begin{tabular}{l l} \(x\), \(y\) & Cartesian coordinate system \\ \(b\) and \(c\) & Constants \\ \(u\), \(v\) & Velocity components along the x-, y-directions \\ \(\omega\) & Angular velocity \\ \(t\) & Time \\ \(\psi(x,y)\) & The stream function \\ \(Uw\), \(Vw\) & Velocity of the stretching sheet along x- and y-directions \\ \(T\) & Fluid temperature \\ \(n\) & boundary parameter \\ \(k^{*}\) & vortex viscosity \\ \(K_{p}\) & Porous medium parameter \\ \(Sv\) & Suction parameter \\ \(K\) & Material parameter \\ \(B_{0}\) & Magnetic field \\ \(A\) & Unsteadiness parameter \\ \(q_{r}\) & Radiative heat flux \\ \(a^{*}\) & Mean absorption coefficient \\ \(j\) & Micro-inertia per unit mass \\ \(Pr\) & Prandtl number \\ \(R\) & Radiation parameter \\ \(f\), \(g\) & Dimensionless components of velocities \\ \(\tau_{w}\) & Shear stress \\ \(C_{f}\) & Skin friction \\ \(M_{x}\) & Dimensionless couple stress \\ \(Nu\) & Nusselt number \\ \(\rho\) & Density of the fluid \\ \(\gamma\) & Spin gradient viscosity \\ \end{tabular}  
Figure 8: Temperature profile with increasing radiation parameter (\(R\)).