esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### The Lunar Orbiter Scenarios  
#### 3.1.1 Presentation  
In order to analyze the applicability of the concept in space, a scenario including two lunar missions foreseen in the next years has been taken as a meaningful example. The estimation of the baseline length is performed between a lunar CubeSat, Volatile and Mineralogy Mapping Orbiter (VMMO), and the communication relay Lunar Pathfinder (LPF). Both mission trajectories have been obtained based on an initial state found in the literature and propagated with the High-Precision Orbit Propagator (HPOP) of STK [62]. The force model applied to propagate the initial state considers the Moon as a central body using a maximum degree and a maximum order of geopotential coefficients of 48 to be included for gravity computations. The gravity file 'GL0660B.grv' from the GRAIL mission was used [63]. The Earth and the Sun were considered as point masses and the STK default SRP model was applied. A representative orbit for the VMMO mission consists in the Low Lunar Frozen Orbit (LLFO) of which the keplerian parameters are reported in [64].  
The second lunar mission considered in this paper, the LPF, is planned to fly to the Moon in an Elliptical Lunar Frozen Orbit (ELFO) [6; 65]. The goal of this mission is to serve as a relay between lunar assets via the S-band and Earth in the X-Band [66]. The orbit of LPF favors a long-duration coverage of the lunar southern hemisphere attractive for early lunar missions, and its orbital keplerian parameters are given in [65]. It covers the far side of the Moon and benefits from long access times to Earth to relay back customers' data, which will also benefit its GNSS visibility.  
The overall scenario is depicted in Figure 3. This scenario is a potential real scenario as these two missions will both have a GNSS receiver aboard and a communication link to communicate with neighboring space missions. This scenario is simulated on 09-Nov-2025 00:00:00.00 Coordinated Universal Time (UTC) when the presence in space of both user missions can be assumed. The VMMO and Pathfinder were chosen as potential users for three reasons. The first is related to the certitude that they will both fly soon into space, with a planned date in 2025 [6; 67]. The second is that Lunar Pathfinder will be one of the two very first missions ever to fly a Galileo-GPS receiver onboard all the way to the Moon, and will return GNSS Doppler, Pseudoranges, and Carrier Phase measurements as well as signal samples back to the ground. Finally, the CubeSat was an ideal candidate for presenting this concept given its low-cost constraint that justifies a potential need for low-complexity ranging techniques.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 5 Performance Analysis
### Block Error Rate (\(Bler\))  
\(BLER\), used for link-level performance evaluation, is calculated as follows:  
\[BLER=\frac{failure\ TBS}{total\ TBS}, \tag{12}\]  
where \(total\ TBS\) is the total number of \(TBS\) sent and \(failure\ TBS\) is the number of \(TBS\) that failed to transfer. This study denotes an SNR that satisfies \(BLER\leq 0.1\) as \(SNR_{required}\)[30].
## 5 Low-Power Cellular Technologies for IoT
### NB-IoT
#### 5.2.2 NB-IoT Deployment Modes  
NB-IoT has three modes of deployment options, as presented in Figure 5. The standalone deployment, which is considered the most expensive, requires the installation of a new radio--such as in the case of GSM--and skills to develop and deploy. On the other hand, NB-IoT can be deployed in either the in-band or guard-band mode. The guard-band and in-band deployment options can use a single 180 kHz PRB of the LTE network. The standalone mode is independently deployed as a dedicated carrier using a spectrum  
Figure 4: Overview of UE’s duty cycle and power utilisation due to the 3 GPP standardisation effort.  
greater than the required 180 kHz frequency bandwidth (400 kHz minimum). The GSM wireless access networks and satellite communication systems may be used to deploy NB-IoT in standalone mode. This is achieved by refarming a small slice of spectrum to include a 200 kHz and 100 kHz guard-band for different operators and the same operator, respectively, deploying both GSM and LTE [27]. Guard-band mode is deployed within the existing guard-band of LTE networks since 5% of the LTE channel bandwidth is not fully occupied. NB-IoT design technology, according to 3 GPP release 15, will be migrated to 5G radio access to provision LPWAN services since both have been designed to support the same frequency bands and can coexist orthogonally [66]--but there is a limitation in terms of unwanted NR emission which degrades the NB-IoT carrier power level. The in-band deployment mode exists in the PRB of an LTE carrier. This is the operational mode on which the NB-IoT study was conducted. The initial features and specification of the NB-IoT is outlined in Table 4, leading to MNOs running trials and testbeds in different countries, as shown in Figure 6.  
Figure 5: NB-IoT deployment modes.  
Figure 6: Global NB-IoT deployment. Reproduced with permission from [PERSON], Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives, _Sensors_; published by MDPI, 8 June 2019 [21].
# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 2 Theory and Method  
### Degradation Model  
Before solving the problem of laser ultrasound super-resolution, it is more important to clearly understand the degradation model of the image. The degradation model of an image can be given by  
\[I^{LR}=(I^{HR}\downarrow_{s})\otimes k+n \tag{1}\]  
where \(\downarrow_{s}\) is the downsampler, \(\otimes\) is the convolution operation, \(k\) is the blur kernel, and \(n\) is the noise. Below we provide a brief discussion of these parameters.  
**Downsampler.** As far as the author knows, the existing downsamplers are divided into two categories: one is the direct downsampler [29] and the other is the bicubic downsampler [30]. In this paper, we use a bicubic downsampler without considering the noise and the blur kernel is \(k\). It should be noted that the blur kernel and noise in the general degradation model vary and the downsampler is assumed to be fixed.  
**Blur kernel.** The more common known blur kernel in the model is the isotropic Gaussian blur kernel [31], and the anisotropic Gaussian blur kernel [32] is also used in some models. However, in practical applications, the blur kernel of the image is complex and changeable. We cannot use a hypothetical model to represent the blur kernel. If we assume that the blur kernel is smoother than the actual blur kernel, the generated high-resolution image will be excessively blurred. If the assumed blur kernel is sharper than the actual blur kernel, high-frequency artifacts will appear in the generated high-resolution image.  
**Noise.** In practical applications, LR images are usually accompanied by noise. The most direct way to solve this problem is to pre-denoise the image before performing super-resolution on the LR image, but such an operation will lose some detailed features of the image and reduce the image quality after super-resolution. Therefore, it is a very good choice to hand over the noise and the blur kernel to the network for learning.  
How to accurately learn the degraded features and noise features of images is recognized as a key factor for image super-resolution. We learn the degradation information from the HR image to the LR image through the up and down projection layers and use a multi-layer residual network to learn the basic features and the noise information of the LR image. We use such a network framework to achieve the effect of considering both image degradation information and noise information in a model.
## 3 Results and Discussion
### Detection of Malachite Green Using Gold Nanords or Nanowires as a Solid Substrate  
In addition to non-polar hydrophobic analytes (e.g., 3,4-benzopyrene), the gold nanomaterials in the current work could also serve as solid substrates for the detection of strong polar analytes, such as malachite green. Figure 6,b demonstrates the SERS spectra of malachite green using the ultralong gold nanowires or long nanorods deposited on a piece of glass as a solid SERS substrate.  
Figure 4: (**a**) The dependence of the size of the spherical gold nanoparticle on the growth/seed ratio; (**b**) XRD pattern of the gold nanoparticles prepared at a growth/seed ratio of 8:1.  
Figure 5: (**a**) Surface-enhanced Raman scattering (SERS) spectra of 3,4-benzopyrene with and without spherical gold nanoparticles (Au NPs) as a hydrophobic substrate; (**b**) SERS spectra of benzidine with and without Au NPs as a hydrophobic substrate.  
Apparently, without the nanowires or nanorods as a substrate, no typical SERS signals of malachite green at 1613 cm\({}^{-1}\), 1390 cm\({}^{-1}\), and 1172 cm\({}^{-1}\) were observed on glass. A substantial increase in signal intensity was detected when the nanowires or nanorods was used as a solid substrate. These results indicate that all gold nanoparticles, long nanorods, and silk-like ultralong nanowires exhibited excellent surface-enhanced Raman scattering activities and can be employed as efficient SERS substrates for the detection of many hydrophobic toxicants and strong polar analytes.
## 3 Results and Discussion
### Volatile Compounds
#### 3.4.4 Others  
In addition to esters and alcohols, the composition of cider is also influenced by the phenolic compounds present in apples [37; 38]. Phenolic acids and flavan-3-ols are two important phenolic substances associated with apples and cider. Phenolic acids and flavan-3-ols contribute to the bitterness, astringency, color, and certain aromatic compounds in cider. Apples contain a variety of phenolic compounds, with significant variations between apple varieties [38; 39]. Terpenes are aliphatic long-chain hydrocarbons derived from isoprene or dimethylallyl pyrophosphate, forming monoterpenes, sesquiterpennes, or diterpenes. They include oxidized derivatives such as alcohols, aldehydes, ketones, esters, and oxides. Previous studies have identified a-Farnesene, eugenol, piperool, and isoeqenol in apple-fermented beverages [40; 41; 42].  
Volatile esters, alcohols, aldehydes, and other compounds collaboratively shape the aroma profile of cider, significantly influencing its aroma characteristics and overall quality. When selecting apple varieties for juicing, it is important to prioritize those with high organoleptic and health properties, including good eating quality, rich flavor, and appealing juice appearance. Achieving optimal taste in single-variety apple juice relies on soluble solid measurement, with acidity acting as a vital regulator. Polyphenolic compounds contribute to the flavor and antioxidant activity of apple juice. With strong antioxidant properties attributed to its polyphenols and ascorbic acid, apple juice exceeds the total antioxidant activity of quercetin, epicatechin, and proanthocyanidin B2 in terms of vitamin C equivalents. Alcohols, aldehydes, and other volatile esters are the predominant volatile substances in cider, collectively shaping its aroma profile.
# Simulation of Wave Propagation Using Finite Differences in Oil Exploration
### Acoustic Wave Equation  
The acoustic wave equation is a mathematical equation that describes how sound waves propagate in an acoustic medium, such as air, water, or any other elastic medium [26]. This equation represents the law of energy conservation. It states that the sound pressure variation at a given point equals the rate at which the pressure changes and propagates in the acoustic medium. In other words, the acoustic wave equation describes how sound waves propagate in the medium and how they change over time [35; 36].  
The acoustic wave equation for heterogeneous media is derived using the Euler relation (1) and the continuity Equation (2) from the following, as stated in [6]:  
\[\frac{\partial v}{\partial t}+\frac{1}{\rho}\overset{\rightarrow}{\
abla}p=0 \tag{1}\]  
\[\frac{\partial p}{\partial t}+\rho c^{2}\overset{\rightarrow}{\
abla}v=0 \tag{2}\]  
where \(v\) represents the velocity of a particle, \(p\) is the sound pressure, \(\rho(x,z)\) is the density, and \(c=c\) (\(x\), \(c\)) is the velocity of the wave in the corresponding medium. Applying the divergence to Equation (1), it has:  
\[\frac{\partial\left[\overset{\rightarrow}{\
abla}v\right]}{\partial t}+ \overset{\rightarrow}{\
abla}\left[\frac{1}{\rho}\overset{\rightarrow}{ \
abla}p\right]=0 \tag{3}\]Deriving Equation (2) concerning time, it has:  
\[\frac{\partial^{2}p}{\partial t^{2}}+\rho c^{2}\frac{\partial\left[\overrightarrow {\
abla}\
abla\right]}{\partial t}=0 \tag{4}\]  
Clearing the first term of Equation (3) and substituting the one in (4) yields:  
\[\frac{\partial^{2}p}{\partial t^{2}}-\rho c^{2}\overrightarrow{\
abla}\left[ \frac{1}{\rho}\overrightarrow{\
abla}p\right]=\delta(r)f(t) \tag{5}\]  
Equation (5) is the acoustic wave equation in terms of the pressure of the medium. Note that the equation includes the source of the right side of the equation. The Dirac delta function positions the source in space, and the function \((t)\) defines its characteristic shape in time [27]. In this paper, the Kronecker delta replaces the Dirac delta, which has a value of 1 at the location of the source and 0 at any other point in the region under study.  
Using the properties of the Dirac and Kronecker delta values, it is possible to analyze the characteristics of the considered source. In this case, the source is of the discrete impulse type, representing an instantaneous peak. The Dirac delta function behaves as a function of the two variables, usually restricted to non-negative integers. For example, in a referential system, the horizontal axis, \(x\), is a given value, and \(y\) is zero. For any other \(x\) value, the Kronecker delta function comes into play as a function of two variables, usually restricted to non-negative integers. It produces a value of 1 when the two variables are equal and 0 otherwise. The Kronecker delta function avoids using an \"infinite impulse\" as a source [28].
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### Application Scenarios
#### 4.2.5 General Scenarios  
(1) Cloud Server: More devices hand over partial calculations and logical judgments to the cloud server. This operation enhances the computing power of devices, but it also leads to more security problems. IFTTT (if this then that) is a typical third-party application that has attracted security researchers' attention. Researchers provides a privacy protection scheme for IFTTT applets [65] and detected interrule vulnerabilities in them [71]. As a decentralized CPS network is compatible with the blockchain's design concept, the number of related studies has increased rapidly. Blockchain techniques, such as smart contracts [69; 69], consensus mechanisms [74; 148], and decentralized systems [66; 70; 70], have been applied to CPS security and have achieved good results.  
(2) Industrial Control System: ICS refers to distributed control systems in many industries, such as water treatment [88] and IP cameras [130]. Programmable logic controllers (PLCs) are popular controllers designed for the industrial environment. To study the safety problems in PLC, researchers have designed MiniCPS [92], a PLC simulator; HARVEY [13], a PLC rootkit; and ICSREF [93], reverse engineering for PLC binary. Usually, ICS uses wireless or wired communication protocols according to the surrounding environments. Therefore, to discover unknown bugs, researchers have fuzzified several stateful protocols (e.g., snmp, ftp, ssl, bgp, and smb) [79; 167] and evaluated simple radio protocols (e.g., ZigBee, ZWave, WiFi, and Bluetooth) [80]. They also optimized these protocols, such as the 802.15.4 protocol [82], constrained application protocol (CoAP), and MQ telemetry transport (MQTT) protocol [81], to enhance their reliability and robustness.  
(3) Embedded Device: Embedded devices are usually parts of larger devices, such as switchers, routers, and gateways, for specialized tasks. Many of them are designed with a specialized OSs and are optimized to minimize computation and memory usage. As a result, traditional PC security solutions cannot address the challenges in embedded devices. There is not a one-size-fits-all security approach to embedded devices. Therefore, we must consider the risk of attack, possible attack vectors, and the cost of implementing a security solution. Features that need to be considered are listed in Table 3.
# Automation and Robotics Pilot Lines in the Context of Industry 5.0
## 3 GAMHE Pilot Line
### Comparative Analysis  
Consistent with the majority of lines previously analyzed, GAMHE has a clear focus on Industry 4.0. Due to its generic nature and adaptability, it can be classified as a general pilot line with a clear emphasis on automation. Regarding Industry 5.0, the key technology aspects focus on robotics, artificial intelligence, IoT, and communications. This makes GAMHE similar to the lines discussed in [16; 36; 50].  
In terms of human aspects, GAMHE places significant emphasis on human-centric design, being prepared for validation of human-in-the-loop cases and Human Digital Twins. It also serves as a valid educational tool and is fully replicable as a capacity-building asset, like those mentioned in [40; 43].  
GAMHE focuses on predictive maintenance and quality control, prioritizing early defect detection and reducing waste at the end of production lines. This aligns the line closely with the examples provided in [24; 26]. All these lines highlight sustainability as a key element of the new industrial paradigm.  
In terms of hardware, GAMHE shares certain similarities with [30; 33; 34; 38; 40]. It aligns with [30] in the use of robots, conveyor belts, and vision systems, and with [40] in its focus on human-robot collaboration, robotic arms, and IoT protocols. With [33; 38], it shares similarities in mobile platforms and AMRs (Autonomous Mobile Robots). Like [34], GAMHE focuses on Digital Twins, cyber-physical systems, and the acquisition and generation of data. In turn, GAMHE includes CNC machinery for macro- and micro-milling.  
Its modularity and scalability are comparable to those found in [33]. Reconfigurability and flexibility are highly demanded in manufacturing industries and are therefore fundamental requirements when designing lines like GAMHE. This flexibility and scalability enable the line to validate multiple technologies and processes. Additionally, it offers the capability to generate and manage data for subsequent analysis or integration into models, in a similar manner to [26; 28].  
Lastly, a key aspect of GAMHE 5.0, also highlighted as a key feature in other works [35], is its ability to integrate legacy equipment with innovative technologies. This capability enables the line to support a broad range of devices and technologies and adapt to various use cases.
## 5 Conclusions  
The CSI effect has a negative influence in an AMD control system. To address this issue, three mathematical models of a four-storey steel frame are established, to consider the CSI effect in the paper. Several influence factors of a higher-order CSI effect are analyzed, including structural parameters and control gains. Finally, based on a GCC algorithm, the time-delay compensation controller is designed for the experimental system. Based on the results, the following conclusions can be drawn.  
1. As the CSI effect is discarded (Ideal situation), the magnitude of the transfer function is a constant, and the phase of the transfer function is zero, meaning that the transformation process does not contain a time-delay.
2. When the CSI effect is considered, the above two parameters (magnitude and phase) are affected by the frequency of the control voltage, and the optimal frequency needs to approach the peak values of the magnitude and phase curves. As a result, a unit control voltage can be converted into a relatively large control force, and the additional time-delay is relatively short.
3. The chosen frequency of the control voltage should be greater than the fundamental frequency of the controlled structure. At this time, the time-delay of control systems becomes longer, as the structural damping increases.
4. The time-delay is inversely proportional to the displacement gain, and is proportional to the velocity gain. Therefore, it is necessary to guarantee that the weight coefficient according to displacement in the \(Q\) matrix is large and that the weight coefficient according to velocity is small.
5. The time-delay of the system has time-varying characteristics. The controller based on the GCC algorithm in this paper can significantly improve the performance under the adverse influence of the time-varying delay, and its performance is close to the system without time-delay. As a result, it can effectively compensate for the time-delay and enhance the robustness of the control system with parametric uncertainties.  
The research described in this paper was financially supported by the National Key Research and Development Program of China (Grant No. 2016 YFC0701102), the National Natural Science Foundations of China (Grant No. 51378007 and 51538003), and the Shenzhen Technology Innovation Programs (Grant No. JSGG20150330103937411 and No. JCJJ20150625142543473).  
[PERSON] wrote the paper and summarized the results. [PERSON] participated in the data analysis and conceived the study. [PERSON] and [PERSON] reviewed the study plan and corrected the grammatical mistakes.  
The authors declare no conflict of interest.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 8 Theoretical Analysis and Simulations of Hollow-Core Fibers
### Simulation Tools  
In recent years, most of the numerical analysis of guidance mechanism of AR-HCF has been based on full-vector finite element method (FEM) using the commercial software COMSOL Multiphysics. Alternative tools are FEM-based Femlab [46; 47] and finite difference eigenmode (FDE) solver-Lumerical(r) MODE Solutions [65]. In order to efficiently and accurately simulate HCFs using the FEM model, it is important to carefully optimize both computational mesh, particularly sizes of finite elements, and a perfectly matched layer (PML)--an artificial, non-reflective and totally absorbing layer, allowing for the truncation of computational regions while simulating the propagation in HCF with a transparent jacket.  
The mentioned solvers enable the calculation of the mode field distribution of individual fiber modes, their propagation losses--including higher-order mode extinction ratio (HOMER)--the measure of the single-modedness of the fiber (all HCFs for telecom networks and data transmission shall be single-mode, but ensuring this property in HC-PBGF is challenging--see Section 4.2), and group-velocity dispersion (GVD), a parameter which can be converted into the chromatic dispersion (CD) coefficient of the fiber. All those parameters are wavelength-dependent.