esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ionic Conductivity of LLZO-IL Composites
Stability of the composites against time exposure was evaluated by monitoring ionic conductivity of LLZO-FSI IL and LLZO-DFOB IL composites, pressed with 1 ton for the preparation of their pellets, for 24 h. As shown in Figure S5, the conductivity was constant throughout the course of measurements, suggesting absence of unfavorable side reactions, such as bleed out of the ILs, phase separation of composites, and decomposition of the ILs. The conductivity decreased by increasing the pressure for pellet preparation but was constant with time exposure, suggesting that the bleed out of DFOB IL is not spontaneous and occurs only when the pellet is pressed with a high pressure. Figure 6 shows temperature dependency of the ionic conductivity of LLZO-IL composites. The conductivities at 15 \({}^{\circ}\)C, 4.8 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\) for LLZO-FSI IL and 1.9 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\) for LLZO-DFOB IL, increased to 3.8 \(\times\) 10\({}^{-3}\) S cm\({}^{-1}\) and 1.7 \(\times\) 10\({}^{-3}\) S cm\({}^{-1}\) at 100 \({}^{\circ}\)C, respectively. The conductivity value of 1.0 \(\times\) 10\({}^{-3}\) S cm\({}^{-1}\), which is a preferred value for battery applications, was achieved at around room temperature in the case of the LLZO-FSI IL composite. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{SE} & \multirow{2}{*}{**Additive**} & **SE:Additive** & **Mixing** & **Pressure** & \multicolumn{2}{c}{**Conductivity**} & \multirow{2}{*}{**Ref.**} \\  & & **Ratio** & **Procedure** & **for Pellet** & & **(S/cm)** & \\ \cline{3-6}  & & **in Weight** & & & **r.t.** & **60 \({}^{\circ}\)C** & \\ \hline Li\({}_{6.24}\)La\({}_{3}\)Zr\({}_{2}\)Al\({}_{0.24}\)O\({}_{11.98}\) & FSI IL & 85:15 & mortar mixing & 1 ton & 4.8 \(\times\) 10\({}^{-4}\) c & 2.1 \(\times\) 10\({}^{-3}\) & - d \\ Li\({}_{6.24}\)La\({}_{3}\)Zr\({}_{2}\)Al\({}_{0.24}\)O\({}_{11.98}\) & DFOB IL & 85:15 & mortar mixing & 1 ton & 1.9 \(\times\) 10\({}^{-4}\) c & 1.0 \(\times\) 10\({}^{-3}\) & - d \\ Li\({}_{7}\)La\({}_{3}\)Zr\({}_{2}\)O\({}_{12}\) & (19:1 wt/wt) & 80:20 & ball milling & & & \\ Li\({}_{6.75}\)La\({}_{3}\)Zr\({}_{1.75}\)Ta\({}_{0.25}\)O\({}_{12}\) & (27:14)[TFSI] a & 86:14 & mortar mixing & 15 MPa & 6.7 \(\times\) 10\({}^{-4}\) & -3 \(\times\) 10\({}^{-3}\) & [19] \\ Li\({}_{1.5}\)Al\({}_{0.5}\)Ge\({}_{1.5}\)(PO\({}_{4}\))\({}_{3}\) & (9/1) & 50:50 & ball milling & & & \\ \hline \hline \end{tabular}  
* \(N\)-butyl-\(N\)-methylpyrrolidininium bis(trifluoromenthanesulfonyl)imide; b 1-butyl-3-methylimidazolium bis(fluorosulfonyl)imide; c measured at 15 \({}^{\circ}\)C; d present study.  
\end{table} 
Table 1: Composition, preparation procedure, and ionic conductivity of different hybrid materials based on SE and IL additive. Figure 6: Arrhenius plot of ionic conductivity of LLZO–15 wt% FSI IL composite (red) and LLZO–15 wt% DFOB IL composite (blue), pressed with 1 ton.
## 6 Important Applications  
Because of their porous characteristics and easy preparation, continuous porous polymers with a considerable specific surface area and large pore size and pore volume, have been widely used in processes which required high flow rates and low pressures. The more important application of monoliths is that as stationary phases in HPLC. This and other useful fields of application are detailed in the sections that follow: use as stationary phase in HPLC; use as reactive supports; use as bioreactors; use in detection in solid-phase; use in solid-phase extraction and decontamination.
# Cascaded Nonlinear-Optical Loop Mirror-Based All-Optical PAM Regenerator
## 2 Regeneration Characteristics of a Single NOLM-Based Regenerator
### Properties of Single NOLM-Based Regenerators  
We defined the normalized differential gain \(\mathrm{g}_{d}\) (NDG) to characterize the noise suppression performance of the PAM regenerator:  
\[\mathrm{g}_{d}(x)=\frac{\mathrm{d}P_{out}}{G_{0}\mathrm{d}P_{in}}=\frac{\mathrm{ d}y}{\mathrm{d}x}=1+\eta\sin^{2}(x_{0}x)+\eta\kappa_{0}x\sin(2\kappa_{0}x) \tag{9}\]  
The \(\left|\mathrm{g}_{d}(x)\right|<1\) means the noise of the output signal is less than the input signal, representing the amplitude regeneration happened. Moreover, we can locate the regenerative WPs (\(x_{l},y_{l}\)) at the input power \(x_{l}\) where \(g_{d}^{\prime}(x)=0\) and \(g_{d}^{\prime\prime}(x)>0\) as depicted in Figure 2. According to Equations (7) and (8), we can give the equation that all WPs should be satisfied:  
\[\tan(2\pi x)=-\frac{2\eta\pi^{2}}{2\eta+\pi}x \tag{10}\]  
Then, we discussed the regenerative range achieved at each WP. From Figure 2, we can find the strong fluctuation of \(g_{d}(x)\) happens for the high input power. Even at some high-level WPs, \(\left|\mathrm{g}_{d}(x)\right|<1\) will not be satisfied, but there are still two small regenerative intervals \(\mathrm{D}_{1}\) and \(\mathrm{D}_{2}\), as shown in Figure 2b. Therefore, we take the lower and upper cross-points (\(x_{1,l}\) and \(x_{u,l}\)) between the NPTF curve and the line of \(y-y_{l}=x-x_{l}\) to bound the regenerative dynamic range at the WP (\(x_{l},y_{l}\)), i.e., \(\Delta x_{l}=x_{u,l}-x_{1,l}\), as shown in Figure 2.  
Figure 3 shows the variations of the step power \(P_{0}\) (right axis, green line), the WP \(x_{l}\) (left vertical axis) and the dynamic range \(\left[x_{1,l},x_{u,l}\right]\) (left vertical axis) with the coupler's splitting ratio \(\rho\) (left vertical axis). Other parameters are identical to the aforementioned simulation. Four dashed lines represent four WPs. Blue and red lines represent lower and upper cross-points (\(x_{1,l}\) and \(x_{u,l}\)) of each interval, respectively. And shadow area 1, 2, 3, 4 means that \(\left|\mathrm{g}_{d}(x)\right|\) in this area is less than 1. From Figure 3, it can be seen that the normalized WP \(x_{l}\) and the input dynamic range for each level are independent on the splitting ratio \(\rho\), which maintains a stable relative level-spacing of the NOLM regenerator. Therefore, the variation of \(\rho\) induced by the manufactured process cannot affect the optimal normalized WP or regenerative range. It should be pointed out that the optimization of the splitting ratio \(\rho\) is crucial to the following design of cascaded NOLM regenerators.  
Figure 3: The variations of the step power, the WP and the dynamic range with the coupler’s splitting ratio.
## 2 Mission Concept
### Mission Objectives  
MeznSat mission objectives are as follows:  
* Primary science: To monitor methane and carbon dioxide levels in the atmosphere using a SWIR spectrometer.
* Tentative science: To study the feasibility of combining SWIR sensing with RGB images to estimate the concentration of nutrients in the coastal waters to predict the possibility of algal blooms
* Educational: Provide educational experience to university students in the UAE in building and designing space missions.
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
## 2 Materials and Methods
### Algorithm
#### 2.1.3 Train Parameters  
The necessary parameters of the train to perform the calculation can be grouped into the three categories of driving dynamics, ETCS braking performance, and latency.  
The delays for processing the balise group information between receiving the balise group message and updating the status on-board according to [43] are considered by adding the so-called \"processing time\" of the European Vital Computer (EVC) and the DMI, which adds latency of the specified amount to the modelled system.  
The braking curves are obtained using the official European Union Agency for Railways (ERA) braking curves simulation tool [44]. With that approach, the complexity of the algorithm developed can be limited to the core functions needed. We therefore can calculate trains both modelled as lambda trains as well as gamma trains [45]. Gamma trains are fixed train compositions for which the exact braking capabilities and properties are known. The braking performance is modelled by brake deceleration values as a step function over the train's speed. Lambda trains are the norm to model the braking behavior of changing compositions. The calculation is based on the known braking percentage of the train. Using the braking percentage of the train as well as its braking position and train length, the UIC Conversion Model [46] is used to calculate the estimated braking deceleration as a function of the speed.  
With this approach, possible future changes in the calculation of the braking curves can easily be integrated. Different scenarios using different supervision limits--for example, the Indication Curve for conventional trains driven by a human driver and the Permitted Curve for automated trains--can be calculated without the need to develop and rewrite essential parts of the algorithm. Even completely different spot-transmission train protection systems could be modelled inside the developed framework if they can be abstracted using approximated braking and acceleration curves.
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 2 Communication Signal Description and Dataset Generation
### Signal Eye Diagram and Vector Diagram Description  
The function of the eye diagram is to observe the baseband signal waveform by an oscilloscope. Through the eye-diagram, we can adjust the receiver filter to improve system performance. Besides, due to the characteristics of the modulated signal itself, different modulation modes have apparent visual differences in the eye diagram. As shown in Figure 2, because of the different modulation scales, there are different eye numbers in each eye diagram. For OQPSK, since the two orthogonal signals stagger for half a symbol period, the eye-opening position is always staggered, while other modulated signals always appear at the same time.  
By reconstructing the signal IQ waveforms in the corresponding time, the signal vector diagram shows the symbol trajectory. From its formation mechanism, it is similar to the signal constellation diagram. However, unlike the constellation diagram, the vector diagram can reflect the signal phase information. For example, it can easily distinguish QPSK from OQPSK with the same initial phase, because there is no 180\({}^{\circ}\) phase shift in OQPSK, while it exists in QPSK. Meanwhile, compared with the constellation diagram, the vector diagram is more convenient to obtain and requires less prior information.
## 1 Introduction
### State of the Art on HBIM-GIS integration: IFC and CityGML Standards
However, this last LoD is not enough accurate for representing building information as well as BIM. Moreover, it is a model that can be used to store and exchange 3D models of virtual cities. It is implemented as an application schema for Geography Markup Language version 3.1.1 (GML3), the extensible international standard for data exchange released from OGC and ISO TC211. It is a standard rich of semantic data in which are assigned unique IDs, names, and descriptions of the different building components. CityGML is a multi-scale representation standard, from which precision requirements derive. On the other hand, the IFC open standard data, differently from CityGML, is based on the concept of LOD (Level Of Development). These are used to monitor the design phases of building construction and they are not related to the scale of visualization. \begin{table}
\begin{tabular}{c} Real object \\ form \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of levels of detail 0–4 representations in CityGML—buildings [24; 25]. The IFC format is based on the EXPRESS language defined by the standard \"ISO 10303-11: Industrial automation system integration--Product data representation and exchange--Part 11: Description methods: The EXPRESS language reference manual\". Being an EXPRESS-based standard, means that the entities are referred to each other by line number, whereas CityGML is an XML-based schema which uses the XML Schema Definition (XSD) to define the relationships between entities [26] and this different conceptual base makes difficult the interrelation with these two standards. Starting from the standards, some researchers tried to develop an extension of CityGML to obtain semantic IFC data into a GIS context [27], using the IFC standard to convert both GIS and BIM data [28] and to convert the IFC into a geographic vector format, which includes spatial data [6]. In this context, an interesting contribution to the theme has been given by [29; 30; 31] with the Unified Building Model (UBM) that strives to avoid loss of information, redefines spatial relationships between the objects, after the evaluation of the overlapping concepts and data, and determines some extensions for both CityGML and IFC taking into account also the LoD concept. In this research, the two standards are considered in the second methodological phase in order to allow the data conversion from BIM to GIS. The IFC open file format, which allows us to create symbols in a consistent way and ensures uniform use including interoperability, is here used for the data exchange, transforming the parametric model into the CityGML standard data model; in this way it is possible to guarantee a good level of interoperability, considering BIM features related to GIS objects.
# Sentinel: Automated Detection of Android Vishing Apps Using Optical Character Recognition
## 3 Background
### Display Overlay Detection  
Vishing applications employ Display Overlay Attacks that present a fake screen during call events (incoming or outgoing), preventing victims from identifying the actual numbers. To detect these attacks, we compare the number displayed on the screen with the actual number retrieved using Android APIs. We capture a screen during call events and extract the phone number from the captured image using OCR. We also retrieve the actual incoming or outgoing number by invoking the Android APIs in the background application, and then we compare the extracted number from the screen with the actual number. Our approach enhances the detection of Call Redirection Attacks by double verification.
# Comparison of the Sensitivity of Various Fibers in Distributed Acoustic Sensing
## 1 Introduction
The cable's efficiency in detecting certain acoustic frequencies varies with the strain, winding, twist and bend of the fiber inside the cable, as well as the contact type between the cable and the environment. A number of works study the influence of this parameter [7; 32; 33; 34]. However, the main element of a sensing cable is still the optical fiber. As DASs usually utilize Rayleigh backscattering in their optical fiber [35], a common way to increase their efficiency is to enhance this scattering. We can achieve this, for example, by increasing the number of Rayleigh scatterers in the fiber [36]. However, the more optical power is scattered back to its source, the higher the transmission losses will be. Another method is to make fiber Bragg gratings (FBGs) inside the fiber [37]. Along with increased transmission losses, such fibers can cost significantly higher amounts, and result in quasi-distributed sensing. Figure 1: Simplified schematic of a DAS setup. That is why the influence of a standard single-mode fiber coating type on DAS efficiency should be considered. For example, [PERSON] et al. compare a bare fiber to loose-tube, thermoplastic, polyurethane, tight-buffered and polyethylene, tight-buffered fibers. Fiber outer diameters vary from 2 to 7.2 mm. Their results show that a simply tight-buffered fiber (with no reinforcement) has the highest acoustic sensitivity. And, the difference in the fibers' sensitivity decreases with an increase in the acoustic frequency. The range of the acoustic impact frequencies the authors used for their tests laid between 0 and 500 Hz [38]. However, the fiber optic industry can also offer standard single-mode and anisotropic fibers of different diameters, produced by MCVD, VAD or OVD technology, which are coated in acrylate, imide, fluorocarbon, propylene, vinyl polymer silicone, carbon, metal and many other coatings. Moreover, the range of acoustic frequencies to be detected by DASs in different applications includes both infra- and ultra-sound frequencies. Being closer to practical research than to theoretical research, the aim of this work was to study the performance of a DAS setup with seven different fiber types available.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 4 Theoretical Results
### Generation Task
Specifically, for a given distortion level \(D\), the required semantic rate decreases as \(\eta\) increases. Furthermore, the maximum achievable distortion diminishes with larger values of \(\eta\). These observations suggest that side information effectively acts as shared background knowledge between the encoder and the decoder, facilitating more efficient semantic communication. The stronger the correlation, i.e., the higher \(\eta\), the greater the reduction in the required rate for a given level of reconstruction fidelity.
## 3 Related Surveys  
Although there is a significant number of surveys focusing on botnet attacks and their detection mechanisms, such as those of [PERSON] et al. [14], [PERSON] [15], [PERSON] et al. [5], and [PERSON] et al. [16], only [PERSON] et al. [3] specifically address botnets abusing CPLS as C&C channels. In their survey, [PERSON] et al. present a taxonomy of malicious social bots and discuss various attack types at different stages. They also propose four abusive tactics, including encoding communications within text-based social media posts, using steganography, exploiting free accounts, and implementing a username generation algorithm (UGA). Despite recognizing four abusive techniques in this survey, our proposed SLR identifies and categorizes nine different types of abusive attacks.  
Table 2 compares our SLR to the survey by [PERSON] et al. [3]. Our SLR adopts a unique methodology that diverges from the approach utilized in the related work. The SLR we conduct incorporates a structured and comprehensive search strategy, criteria for inclusion and exclusion, an evaluation of study quality, and data synthesis to formulate a taxonomy of attack techniques employed by botmasters to exploit CPLS C&C channels. Conversely, the related work does not follow a systematic review methodology. It instead provides an overview of current research on botnet C&C channels through social  
Figure 2: Illustration of (**a**) traditional C&C server communication and (**b**) CPLS platforms being abused as C&C.  
media, investigating emerging trends, potential defense strategies, and areas warranting further research.  
[PERSON] et al. [14] offer a concise overview of an evasive strategy that abuses social networking websites like Facebook and Twitter for C&C functions and provides a detailed taxonomy of the botnet phenomenon. This review also anticipates a trend, wherein CPLS would either be used to create bots (i.e., botcloud) or to host the C&C on the cloud in the future.  
[PERSON] [4] proposes a taxonomy of malicious social bots and characterization of various attack strategies at different stages (initiation, listening, and execution-stage attacks). The main focus is on a social bot, with the discussion being around one attack technique, which involves using steganography to abuse Facebook and Twitter as C&C mediums.  
All the aforementioned reviews lack a systematic approach, thereby failing to provide a comprehensive overview of this research area. Despite the differing focuses and methodologies between the related surveys and our SLR, they offer significant insights into the evolving threat landscape of botnets and their use of non-traditional C&C channels. Our SLR, along with two other reviews [3, 4], discusses the challenges associated with detecting and preventing the botnet exploitation of these non-traditional C&C channels. These reviews emphasize the necessity for ongoing research and innovative solutions to tackle emerging cyber threats.
## 5 CFD Modeling
### Analysis Conditions  
The flow enters the computational domain at the panel inlet, at a constant air quantity, perpendicular to the inlet surface. The panel outlet was imparted a relative static pressure of 0 Pa. Steady-state simulations were run at three different scrubber air capacities. The values for the panel inlet flow rate for the CFD simulations were obtained from the weighted average value of air quantity measured at the panel inlet. As the inlet of the scrubber was withdrawing air from the computational domain, it was given fixed flow quantities of \(-6.5\) (13,700 cfm), \(-3.0\) (6336 cfm), and \(-0.0\) m\({}^{3}\)/s (0.0 cfm) for the three fan speeds, 100%, 50%, and 0%, respectively. The same values, with opposite signs, were assigned to  
Figure 8: Comparison of average velocity along Line 1 and 2 for the four different turbulence models.  
the outlet of the scrubber. The following equations were used to calculate the turbulence energy and turbulence dissipation rate of the working fluid at the panel inlet.  
\[k=\frac{u^{2}}{100} \tag{7}\]  
\[\varepsilon=\frac{0.09^{\frac{3}{4}}k^{\frac{3}{2}}}{lm} \tag{8}\]  
\[lm=0.07D \tag{9}\]  
where \(k\) is the turbulence energy in m\({}^{2}\)/s\({}^{2}\), \(u\) is the flow velocity at the panel inlet, \(\varepsilon\) is the turbulence dissipation rate in m\({}^{2}\)/s\({}^{2}\), and \(D\) is the equivalent diameter.  
Boundary walls were considered to be smooth and stationary, except for the headgate drum, which was assigned a rotating wall condition to mimic the experimental condition. During the laboratory experiments, the headgate drum was given a circular motion to represent the rotating drum during the real shearer operation. No-slip wall conditions were applied to all surfaces, and the standard wall function based on the following logarithmic law (log-law) was used to predict fluid velocity close to the walls of the computational domain.  
\[\frac{u}{u^{*}}=\frac{1}{k}ln\frac{u^{*}y}{\
u}+A \tag{10}\]  
where \(u\) is the flow velocity at position \(y\) in m/s, \(k\) is the Karman constant (=0.4), \(y\) is the distance from boundary wall surface in m, \(\
u\) is the kinematic viscosity in m\({}^{2}\)/s, and \(A\) is a constant (=5.5).  
\[u^{*}=\sqrt{\frac{\tau_{0}}{\rho}} \tag{11}\]  
where \(u^{*}\) is the friction velocity in m/s, \(\tau_{0}\) is the shear stress in kg/m.s\({}^{2}\), and \(\rho\) is the fluid density in kg/m\({}^{3}\).  
\[\
u=\frac{\mu}{\rho} \tag{12}\]  
where \(\
u\) is the kinematic viscosity in m\({}^{2}\)/s, \(\mu\) is the fluid viscosity in m/s, is the shear stress in kg/m.s\({}^{2}\), and \(\rho\) is the fluid density in kg/m\({}^{3}\).  
Iterative convergence was achieved when the order of magnitude decrease in the normalized residuals for all variables was below \(10^{-5}\) within a cycle, and mass flow was balanced. The pressure-based solver of SC Tetra was used to solve mass, momentum, and energy conservation equations with under-relaxation factor for pressures set to 0.6. Initially, the fluid velocities in X, Y, and Z directions were zero, and the fluid pressure was one atmospheric pressure.