esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mathematical Model of Tail Boom Vibrations  
The tail boom of the helicopter is susceptible to vibration. Several factors contribute to this; in particular, the impact of the main and tail rotor loads. An approximate analytical method for simulation of the tail boom vibrations is considered here. The mathematical formulation of the problem is bound by the following limitations and assumptions: (1) The tail boom is considered to be of a constant diameter thin-walled cylindrical structure with continuously distributed mass (no concentrated mass points), so that the total mass of the beam is equal to the one of the light helicopter boom (but also adding the horizontal tail, transmission and other design elements); (2) The tail boom is rigidly fixed at the fuselage end and the other end is free; (3) The mathematical model of vibrations (vertically directed) without viscous damping is determined by the Euler-Bernoulli equation with one spatial coordinate and variable tail boom geometry along the tail boom span. Under these assumptions, the equation of the tail boom deformation is described by equation [4; 7; 12]:  
\[\frac{\partial^{2}}{\partial x^{2}}\left\{EI\frac{\partial^{2}v}{\partial x^{2 }}\right\}+m_{L}\frac{\partial^{2}v}{\partial t^{2}}=F_{L}(t,x). \tag{3}\]  
Here \(x\) is the longitudinal coordinate; \(t\) is the time coordinate; \(EI\) is the flexural rigidity (\(E\) is Young's modulus, \(I\) is a moment of inertia); \(v(t,x)\) is the transverse vertical deformation; \(m_{L}\) is the mass per unit length. The normal (vertical) component \(F_{L}(t,x)\) of the force acting on the surface of the tail boom per unit length is given by  
\[F_{L}(t,x)=\frac{F_{Bz}(t,x)}{L}=q_{\infty}\frac{S_{F}}{L}c_{F}(t,x). \tag{4}\]  
Here \(c_{F}(t,x)\) is the normal force coefficient; \(L\) is length of the beam. The boundary conditions for the Equation (3) can be written as  
\[v(0,x)=\varphi(x);\ \frac{\partial v}{\partial t}(0,x)=\xi(x);\]  
\[v(t,0)=0;\ \frac{\partial v}{\partial x}(t,0)=0; \tag{5}\]  
\[\frac{\partial^{2}v}{\partial x^{2}}(t,L)=0;\ \frac{\partial^{3}v}{\partial x^{3}}(t,L )=0.\]  
According to [12], the solution of Equation (3) with boundary conditions (5) can be written in the form  
\[v(t,x)=v_{0}(t,x)+v_{1}(t,x). \tag{6}\]  
Here the functions \(v_{0}(t,x)\) and \(v_{1}(t,x)\) can be determined by using constraints of the physical and mathematical formulation of the vibration task: the function \(v_{0}(t,x)\) determines the natural and the function \(v_{1}(t,x)\) determines forced tail boom vibrations. Substitution of (6) in Equation (3) leads to the expression:  
\[b^{2}\frac{\partial^{2}}{\partial x^{2}}\bigg{\{}\bigg{(}\frac{\partial^{2}v_{0} }{\partial x^{2}}+\frac{\partial^{2}v_{1}}{\partial x^{2}}\bigg{)}\bigg{\}}+ \bigg{(}\frac{\partial^{2}v_{0}}{\partial t^{2}}+\frac{\partial^{2}v_{1}}{ \partial t^{2}}\bigg{)}=F_{Lm}(t,x). \tag{7}\]  
Here \(F_{Lm}(t,x)=F_{L}(t,x)/m_{L}\), \(b^{2}=EI/m_{L}\).
## 2 Formulation for Damage Identification  
### Element Stiffness Matrix Based on Timoshenko Beam Theory  
Each node has three degrees of freedom (DoFs) in the 2D analysis, and the order of the stiffness matrix, \(k^{\prime}\), is 6 \(\times\) 6 for a beam element with a uniform cross-section. In the element stiffness matrix of the Timoshenko beam theory, a dimensionless quantity dependent on the cross-sectional shape is provided as the shear coefficient, \(K_{\rm S}\). Based on published literature [33; 34], the stiffness matrix of elements based on the Timoshenko beam theory can be expressed as follows:  
\[k^{\prime}=\left[\begin{array}{ccccc}\frac{EA}{L}&0&0&-\frac{EA}{L}&0&0\\ 0&\frac{12 EI}{L^{3}(1+\Gamma)}&\frac{6 EI}{L^{2}(1+\Gamma)}&0&-\frac{12 EI}{L^{ 3}(1+\Gamma)}&\frac{6 EI}{L^{2}(1+\Gamma)}\\ 0&\frac{6 EI}{L^{2}(1+\Gamma)}&\frac{EI(4+\Gamma)}{L(1+\Gamma)}&0&-\frac{6 EI}{L^ {2}(1+\Gamma)}&\frac{EI(2-\Gamma)}{L(1+\Gamma)}\\ -\frac{EA}{L}&0&0&\frac{EA}{L}&0&0\\ 0&-\frac{12 EI}{L^{3}(1+\Gamma)}&-\frac{6 EI}{L^{2}(1+\Gamma)}&0&\frac{12 EI}{L^ {3}(1+\Gamma)}&-\frac{6 EI}{L^{2}(1+\Gamma)}\\ 0&\frac{6 EI}{L^{2}(1+\Gamma)}&\frac{EI(2-\Gamma)}{L(1+\Gamma)}&0&-\frac{6 EI}{L ^{2}(1+\Gamma)}&\frac{EI(4+\Gamma)}{L(1+\Gamma)}\\ \end{array}\right] \tag{1}\]  
In Equation (1), \(E\), \(I\), \(A\), and \(L\) are the elastic modulus, the moment of inertia, the cross-sectional area, and the length of the member, respectively. The coefficient \(\Gamma\) is known as the shear parameter, and can be expressed as  
\[\Gamma=\frac{12 EI}{GA_{\rm S}L^{2}} \tag{2}\]  
where \(G\) represents the shear modulus of the material, and \(A_{\rm S}\) denotes the shear area, which is related to the shear coefficient, \(K_{\rm S}\). It should also be noted that different cross-sectional shapes have different values of \(A_{\rm S}\). For instance, for a rectangular cross-section, \(A_{\rm S}\) equals 5/6 times the cross-sectional area \(A\), while for a wide flange cross-section, \(A_{\rm S}\) is calculated as the product of the thickness of the web and the width of the flange [35]. When the shear parameter (\(\Gamma\)) is equal to 0, Equation (1) reduces to the matrix that disregards shear deformation, i.e., the stiffness matrix of elements based on the Euler-Bernoulli beam theory.
## 2 Related Works and Problem Statement
### Problem Definition Caused by Movement of Mobile Devices  
As mentioned above in Section 2.1, WirelessHART exploits neighbor discovery to respond to network changes. However, because discovery messages are sent at intervals predetermined by the network manager, it is difficult to respond quickly to network changes. That is, the existing discovery process has difficulty responding to continuous topology changes caused by device movement.  
For example, let us assume that device 6 is a mobile device in Figure 1 and is connected to the network through the join process. The network manager constructs a graph for communication for device 6 and allocates resources. After resource allocation, even if device 6 moves close to device 7 and device 8 and disconnects from device 3 and device 4, this disconnection cannot be resolved until the network manager recognizes the change in the network topology through the neighbor discovery process. In other words, until the network changes are discovered by the neighbor discovery process, device 6 would attempt to transmit its sensed data to the gateway through device 3 or device 4 at a designated time and channel, but all data would be lost. This problem is not only a problem for mobile devices but also extends its adverse effects to child nodes with mobile devices as parents. Additionally, even if the network manager constructs a new graph and reallocates resources for network changes discovered through the neighbor discovery process, the discovered location may differ from the actual current location due to device mobility. In conclusion, network changes caused by the movement of devices could lead to communication instability and disconnections in the existing WirelessHART network.
## 2 Materials and Methods
### Determination of Volatile Compounds by GC-MS
#### 2.4.2 GC-MS Analysis  
GC-MS analyses were performed using a Thermo-Finnigan Trace 2000 gas chromatograph (Thermo Finnigan, Waltham, MA, USA) with an HP-INNWAX column (60 m, 0.25 mm I.D., 0.25 \(\upmu\)m; Agilent, Shanghai, China). The solid-phase micro-extraction fiber (SPME, DVB/CAR/PDMS 2 CM, 50/30-\(\upmu\)m) was heated at 250 \({}^{\circ}\)C for 2 h. The vial containing the sample (wine) was extracted in a 40 \({}^{\circ}\)C water bath for 30 min while being stirred and then desorbed at 230 \({}^{\circ}\)C for 3 min into the splitness injection port of the GC-MS instrument. Helium was used as the carrier gas (1 mL/min). The chromatographic conditions consisted of an initial oven temperature of 40 \({}^{\circ}\)C for 2.5 min, increasing the temperature to 150 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, increasing to 220 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min and holding for 30 min. The temperatures of the ion source and MS transfer line were 250 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively. The scan range was 33-450 \(m/z\) with electron impact (EI) mode at 70 eV.  
The NIST 2002 mass spectroscopy library (National Institute of Standards and Technology, Gaithersburg, MD, USA) and the retention times of the authentic standards were used to identify the volatile compounds. Quantification was performed using the peak areas on the total ion chromatogram. The concentrations of the volatile compounds were calculated based on their calibration curves (the relative response rate was greater than 98%), which were built by plotting the area ratio of the target compounds to the internal standard against the concentration ratio [24]. All determinations were performed in triplicate.
## 3 Results
\begin{tabular}{c} **Condition I** \\ **(Hz)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Condition II** \\ **(Hz)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Mean** \\ **Differences** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **\(p\)** \\ **Lower** \\ \end{tabular}  } & \multicolumn{2}{c}{**CI (95\%)**} \\ \cline{3-8}  & & & \(-11.60\) & \(-014\) & \(-21.60\) & \(-1.59\) \\ \cline{3-8}  & \multirow{3}{*}{30} & 40 & \(-21.45\) & \(<\)0.001 & \(31.45\) & \(-11.44\) \\ \cline{3-8}  & & 45 & \(-28.60\) & \(<\)0.001 & \(-38.60\) & \(-18.59\) \\ \cline{3-8}  & \multirow{3}{*}{35} & 40 & \(-9.85\) & \(0.056\) & \(-19.85\) & \(0.15\) \\ \cline{3-8}  & & 45 & \(-17.00\) & \(<\)0.001 & \(27.00\) & \(-6.99\) \\ \cline{3-8}  & & 40 & \(45\) & \(-7.15\) & \(0.339\) & \(-17.15\) & \(2.85\) \\ \hline \multirow{4}{*}{\ \begin{tabular}{c} \(\mathrm{VO_{2 max}}\) \\ (L/min) \\ \end{tabular}  } & \multirow{3}{*}{30} & 35 & \(-0.18\) & \(0.275\) & \(-0.43\) & \(0.06\) \\ \cline{3-8}  & & 40 & \(-0.38\) & \(<\)0.001 & \(-0.63\) & \(-0.13\) \\ \cline{3-8}  & & 45 & \(-0.57\) & \(<\)0.001 & \(-0.82\) & \(-0.32\) \\ \cline{3-8}  & & 40 & \(-0.20\) & \(0.187\) & \(-0.45\) & \(0.04\) \\ \cline{3-8}  & \multirow{3}{*}{35} & 45 & \(-0.38\) & \(<\)0.001 & \(-0.63\) & \(-0.13\) \\ \cline{3-8}  & & 40 & \(45\) & \(-0.18\) & \(0.291\) & \(-0.43\) & \(0.06\) \\ \hline \multirow{4}{*}{ \begin{tabular}{c} \(\mathrm{VO_{2 max}}\) \\ (mL/kg/min) \\ \end{tabular}  } & \multirow{3}{*}{30} & 35 & \(-2.47\) & \(0.177\) & \(-5.49\) & \(0.54\) \\ \cline{3-8}  & & 40 & \(-5.10\) & \(<\)0.001 & \(-8.12\) & \(-2.07\) \\ \cline{3-8}  & & 45 & \(-7.54\) & \(<\)0.001 & \(-10.56\) & \(-4.51\) \\ \cline{3-8}  & & 40 & \(-2.62\) & \(0.127\) & \(-5.64\) & \(0.39\) \\ \cline{3-8}  & & 45 & \(-5.06\) & \(<\)0.001 & \(-8.08\) & \(-2.04\) \\ \cline{3-8}  & & 40 & \(45\) & \(-2.44\) & \(0.190\) & \(Finally, Figure 4 shows the linear regressions between the physiological parameters analysed and MV. Significance was observed in the relationships between SampEn and HR (\(p=0.02\)) and between SampEn and VO\({}_{\rm 2 max}\) absolute and relative to body weight (\(p=0.02\); \(p=0.04\)). In the rest of the figures, the \(p\) value was greater than 0.05. Figure 4: Linear regressions between physiological parameters analysed and SampEn. (**A**) RPE: rating of perceived exertion; (**B**) HR: heart rate; (**C,D**) VO\({}_{\rm 2 max}\): maximal oxygen uptake; (**E**) VO\({}_{\rm 2 max}\): peak carbon dioxide; (**F**) RER: respiratory exchange ratio; (**G**) VE: expired volume; SampEn: sample entropy.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
###### Abstract  
This paper focuses on the exploitation of fifth generation (5G) centimetre-wave (cmWave) and millimetre-wave (mmWave) transmissions for high-accuracy positioning, in order to complement the availability of Global Navigation Satellite Systems (GNSS) in harsh environments, such as urban canyons. Our goal is to present a representative methodology to simulate and assess their hybrid positioning capabilities over outdoor urban, suburban and rural scenarios. A novel scenario definition is proposed to integrate the network density of 5G deployments with the visibility masks of GNSS satellites, which helps to generate correlated scenarios of both technologies. Then, a generic and representative modeling of the 5G and GNSS observables is presented for snapshot positioning, which is suitable for standard protocols. The simulations results indicate that GNSS drives the achievable accuracy of its hybridisation with 5G cmWave, because non-line-of-sight (NLoS) conditions can limit the cmWave localization accuracy to around 20 m. The 5G performance is significantly improved with the use of mmWave positioning with dominant line-of-sight (LoS) conditions, which can even achieve sub-meter localization with one or more base stations. Therefore, these results show that NLoS conditions need to be weighted in 5G localization, in order to complement and outperform GNSS positioning over urban environments.  
GNSS; 5G positioning; cmWave; mmWave; hybrid localization +
Footnote †: journal: Sensors
# Cloning and Functional Identification of _Sipg49_ in _Solanum Lyopersicum_
## 2 Materials and Methods
### Transient Expression Analysis of SIPG49  
The coding sequence (CDS) of _SIPG49_ was amplified and cloned into _pFGC-GFP_ to form the _CaMV355::GFP-SIPG49_ fusion construct. The recombinant product was transformed into _E. coli_ DH5\(\alpha\) competent cells and verified by sequencing. Transient expression of the GFP-SIPG49 fusion protein in onion epidermal cells was carried out by a gene bombardment transformation method. The validated plasmids harboring _CaMV355::SIPG49-GFP_ were used to prepare the particles for bombardment. The inner epidermis of the onion (1-2 cm) was pre-incubated on Murashige and Skoog (MS) medium. The bombardment was performed using a gene gun at 1100 p.s.i. with DNA-coated gold particles from a distance of 9 cm. The bombarded samples were cultured on MS medium for 16-24 h at 25 \({}^{\circ}\)C in the dark. The images were taken with a laser scanning confocal microscope (Olympus, Japan).
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 4 Proposed System Overview and Models
### System Models
#### 4.2.2 Energy Consumption Model  
For sensor node energy usage management, the first order radio model [11; 12], a channel model with a free space (i.e., \(d^{2}\) ), has been used. In this paper, energy dissipation of radio components and circuitry was considered as illustrated in Figure 8.  
A packet consisting \(b\) bits is transmitted at a \(d\) distance between the transmitter (\(T_{x}\)) and receiver (\(R_{x}\)), using transmission energy \(E_{T_{x}}(b,d)\), illustrated by Equation (8) as  
\[E_{T_{x}}(b,d){=}E_{elec}{\times}b+E_{amp}{\times}b{\times}d^{\lambda}, \tag{8}\]  
where \(E_{elec}\) represents the energy consumed by the electronics circuitry, whereas \(E_{elec}{\times}b\) represents the energy needed by the \(T_{x}\) electronics to propagate \(b\) bits. Furthermore, \(E_{amp}\) is the amplifier energy, and the path loss constant is represented by \(\lambda\). The required energy to receive \(b\)-bits is denoted by \(E_{R_{x}}(b)\) as shown in Equation (9),  
\[E_{R_{x}}(b){=}E_{elec}{\times}b. \tag{9}\]  
The energy for transmission used by the \(T_{x}\) amplifier is \(E_{amp}{=}100p/b/m^{2}\). Moreover, required energy by circuitry of \(T_{x}\) and \(R_{x}\) is \(50~{}nJ/b\). The values of \(E_{elec}\) and \(E_{amp}\) are chosen in such way that they result in an acceptable \(E_{b}/N_{0}\)[12].
# Detecting Symptoms and Dispersal of Pine Tortoise Scale Pest in an Urban Forest by Remote Sensing
## 2 Material and Methods  
### Study Site  
The Presidential Estate of Castelporziano is a natural vegetation area of approximately 6000 hectares under legal protection status, located 25 km southwest of Rome in the region of Lazio (Figure 1A).  
Lazio lies within a Thermo-Mediterranean zone, marked by extended aridity in summer and moderate cold snaps in winter. Low-pressure episodes steer the synoptic-scale air mass movements, while high-pressure periods predominantly dictate circulation patterns, fostering a local sea-land breeze regime [47]. Wind data were collected from the meteorological station in Castelporziano. Wind patterns were aggregated in a study period from April to June (i.e., when new generations of PTS arise after overwintering) for the years 2019 and 2020.  
The Estate features a wide range of vegetation types, including sand dune vegetation, Mediterranean maquis, high maquis, _Quercus 'lex'_ evergreen forests, _Quercus'super'_ forests, broadleaf mixed forests, _Eucalyptus globulus_ plantations, and cropland [48]. Pine plantations account for 678 ha and are mainly formed by _Pinus pinea_, with a minor presence of _P. halepensis, P. bruttia, and P. pinaster_[27; 49].  
The onset of the PTS pest outbreak on the stone pine forests in the study site was reported by the Estate's personnel during visual ground checks in 2019. The first infestation hotspot (Figure 1B) was pinpointed by high-resolution multispectral imagery in the northeastern part of the Estate and dated as early as August 2018 [50].
# Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor
## 2 Materials and Methods
### Character-Level Encoding  
Regarding the actual input and output used in the model, we use a one-hot encoding, i.e., a string \(s=s_{1}s_{2}\ldots s_{l}\) of size \(l\in\mathbb{N}\) is transformed into a matrix \(\mathbf{X}\in\mathbb{M}_{v\times l}\), where the \(i\)-th column \(x_{i}\in\mathbf{X}\) is set to zero in all positions except the one corresponding to the index of the character \(s_{i}\), according to the model alphabet. Recall that \(v\) is the size of the alphabet (127 in our case).  
The advantages and disadvantages of using a character-level model--as opposed to word-level models--have been described by some authors, since it appears as a basic design decision in many NLP problems. Four arguments in favor of character-level approaches are shown, three of them introduced by [PERSON] et al. [14], and the last one given by [PERSON] et al. [15]:  
* Out-of-vocabulary issues do not appear, as it could happen in word-level models. We could suffer from out-of-alphabet issues, but these can be easily solved.
* Such approaches are able to model rare morphological variants of a word.
* Input segmentation is no longer required.
* By not segmenting into words, the models have to discover the internal rules and structure of the sentences by themselves.  
Since text segmentation is known to be problematic and error-prone, even for well-known languages such as English, removing this step without losing performance is a significant advantage to consider. Moreover, we can provide an additional argument for character-level approaches: If the model uses attention mechanisms, observing the attention matrices after a particular sample gives a better understanding of the system's logic and the language itself. For example, consider the case where the model transforms _2s_ into _two seconds_; its attention matrix could potentially show that the last letter was produced by looking at the number.
###### Abstract  
Alleopathy is a biological phenomenon of synthesizing and excreting compounds that can affect the growth of various organisms, i.e., plant species. The aim of this work was to investigate the possible allelopathic influence of aqueous extracts, obtained from plant species chia (_Salvia hispanica_ L.), black cumin (_Nigella sativa_ L.), wormwood (_Artemisia absinthium_ L.), and nettle (_Urtica dioica_ L.), on the seed germination growth and morphometric characteristics of pepper (_Capsicum annuum_ L.), spinach (_Spinacia oleraca_ L.) and lettuce (_Lactuca sativa_ L.) seedlings in laboratory conditions. Pepper, spinach, and lettuce seedlings were treated with aqueous extracts of chia, nettle, wormwood, and black cumin of different concentrations (2.5, 5 and 10%, respectively). The aqueous extracts were prepared according to the method developed by [PERSON] and the qualitative and quantitative analysis of phenolic compounds in aqueous extracts were performed using ultra-performance liquid chromatography electrospray ionization tandem mass spectrometry (UPLC-ESI-MS/MS). Phytochemical composition of chia aqueous extracts showed the highest content (above the 1 mg L\({}^{-1}\)) of epicatechin, quinic acid, caffeic acid, esculetin and cinnamic acid in the comparison with others from the total of 19 detected chemical compounds. In aqueous extracts of black cumin, there were six compounds detected in content above 1 mg L\({}^{-1}\), i.e., epicatechin, quinic acid, caffeic acid, esculetin, cinnamic acid, and kaempferol. The same compounds were the most abundant in wormwood extracts, along with gallic acid. Epicatechin and esculetin were only two compounds detected in aqueous extract of nettle in concentration higher than 1 mg L\({}^{-1}\). According to the results of this study, only the treatment of spinach seeds with chia aqueous extract in concentration of 2.5% before germination stimulates the germination of spinach seeds, and wormwood herbs and chia extracts in concentrations of 2.5 and 5% stimulate the germination of lettuce seeds and the growth of hypocotyl and radiicle length of developed seedlings. The treatment of pepper and lettuce seeds with aqueous extract of nettle in concentration of 10% completely inhibited seed germination.  
medicinal plants; horticultural plants; aqueous extracts; allelopathy; seeds germination; hypocotyl; radiicle; pepper; spinach; lettuce 2023
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 2 NDT Architecture for the RAN  
The design of the RAN NDT needs to allow the accurate characterization of the real RAN behaviour and its performance assessment, as well as to be flexible for use in different applications and cases. Considering this, Figure 1 shows the architecture of the NDT of the RAN that is proposed in this paper. For facilitating the practical feasibility of the proposed architecture, it is aligned with the terminology and concepts proposed in IETF in [10]. The RAN NDT architecture includes three main modules: the _data repository_, which gathers and stores the data from the real RAN, the _service mapping models_, which allow representing the different elements and the operation in the real RAN, and the _digital twin management_, which manages the NDT. In addition, the architecture identifies different potential applications, or _Apps_, that can benefit from a RAN NDT instance of the proposed architecture customized to its requirements. In the following, further details on these modules are provided.