esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Study
### Structural Health Monitoring of the Underground Box-Type Structure Using FBG Sensors  
As shown in Figure 2, the deformation, crack development and ambient temperature of 17 sections of the box-type structure were monitored using FBG sensors. The selection criteria for these 17 sections were based on (1) maximum possible uneven settlements predicted by the numerical simulations, (2) the frequency of occurrence of cracks in the pavement of the road above a section, (3) the occurrence of bubbles and seepage in the ceiling of a section and (4) a section that goes across the subway tunnel.  
As shown in Figure 3, the numerical simulation results show that the stress concentration regions are mainly located in the top and bottom corners of the tunnel. Thus, the FBG sensors were installed in these positions (Figure 4). In addition, it has been observed that most of the cracks normally occur near the middle of the two lanes and drainage ditches of the highway above the box-type structure, so the installation of FBG displacement sensors was mainly concentrated near the middle passage, i.e., displacement sensors at A and B, respectively, and strain sensors at C and D, respectively. Further, the FBG temperature sensors were installed in each of the sections to monitor the temperature inside the box-type structure.  
Figure 1: Population distribution and cross-sectional view of the composite road box-type structure.  
Figure 2: Schematic diagram of the 17 sections monitored in this study.  
The details of the different FBG sensors are shown in Figure 5. For FBG displacement sensors the measurement range was 20 mm, the resolution was less than 0.1% FS (full scale), the accuracy was less than 0.3 FS and the central wavelength of the grating was between 1525 and 1565 nm (10 nm = 10\({}^{-8}\) m). For FBG strain sensors the test resolution was less than 0.1% FS, the precision was less than 2% FS, the working temperature range was between \(-\)20 and +50 \({}^{\circ}\)C, the strain measurement range was between \(-\)1000 and +1000 \(\mu\)c and the center wavelength shift range was between 1.5 and 1.8 nm. As FBG sensors were installed on the inner surfaces of the box-type structure, the effects of tides and rainfall could not be captured. Further, for FBG temperature sensors the range of measurement was between \(-\)50 and +80 \({}^{\circ}\)C, the temperature coefficient was 0.1 \({}^{\circ}\)C/pm (1 pm = 10\({}^{-12}\) m), the measuring  
Figure 4: The positions of the fiber Bragg grating (FBG) sensors installed in underground box-type structure.  
Figure 3: Stress nephogram of the cross-section of the tunnel under normal operation conditions.  
accuracy was less than 1 \({}^{\circ}\)C and the central wavelength of the grating was between 1525 and 1565 nm. The types and quantities of fiber grating sensors are summarized in Table 1. The integrated monitoring system consists of FBG and temperature sensors connected in series, so that the change of wavelength in the FBG strain sensors can be compensated for through consideration of the temperature field by measuring the wavelength change in the temperature sensors.
## 2 Materials and Methods
Set-point, scan rate, and gain values were chosen to optimise image quality. The AFM scanner was calibrated in x, y, and z directions using silicon calibration grids (Bruker model numbers PG: 1 \(\upmu\)m pitch, 110 nm depth and VGRP: 10 \(\upmu\)m pitch, 180 nm depth, Bruker Corporation, Billerica, MA, USA). AFM topography images have been flattened, and thickness and roughness measurements determined using Nanoscope Analysis 1.4 (Bruker Corporation, Billerica, MA, USA). Scanning electron microscopy (SEM) images were obtained using an Inspect FEI F50 SEM (Hillsboro, OR, USA) with beam spot size and high voltage adjusted to optimise image quality. The image resolution was generally \(2048\times 1768\) with an imaging speed of 30 \(\upmu\)s.  
Polarised Raman spectroscopy was performed using a Horiba XploRA (Kyoto, Japan) scientific confocal Raman microscope. The laser used was a Nd:YAG 532 nm laser and the power used at the sample was 1.2 mW. A \(\times 50\) magnification objective was used. The data was collected at 25 locations in \(5\times 5\) grid 2 spectra were collected per location with a 30 s integration time per spectrum. The grating used was 1200 grooves per mm which gives spectral resolution of approximately 3 wavenumbers.
## 3 Proposed Methodology
### Classification
#### 3.4.1 Feature Extraction  
The extraction of features is a critical component of the model. We train a specific hybrid model for feature extraction. The applied models (CNN-1 and CNN-2) are developed on a convolutional layer sequence that includes 2-Dimensional convolutional layers, batch normalisation layers, ReLU layers, and 2-Dimensional max-pooling layers. Through adding filters, the dataset is transferred to each layer of convolution. Before the last max-pooling, each convolution layer extracts relevant information. Finally, feature extraction is performed on both networks via a fully connected layer.
# Investigation of Deformation Inhomogeneity and Low-Cycle Fatigue of a Polycrystalline Material
## 3 Measurements of Deformation Inhomogeneity and Predictions of Fatigue-Life Curve
### Fatigue Indicator Parameters (FIPs), Life Prediction and Verification
#### 3.3.1. Inhomogeneity Measurement of Distribution of Different Strain Components  
Taking \(E_{a}\) as a parametric variable, the curves of SD \(\varepsilon_{ij}(E_{a},N)\), \(\varepsilon_{eq}(E_{a},N)\), \(\varepsilon_{1}(E_{a},N)\) and \(\varepsilon_{13}^{\text{M}}(E_{a},N)\), which are obtained from simulation, are plotted in Figure 5. They, respectively, reflect the growth of deformation inhomogeneity of the material with the cycle number at tension peak of cycles, under different strain amplitude cycles. The strain quantity \(\varepsilon_{ij}\varepsilon_{eq}\varepsilon_{1}\) and \(\varepsilon_{13}^{\text{M}}\)(here, \(\varepsilon_{13}^{\text{M}}=\frac{1}{2}(\varepsilon_{1}-\varepsilon_{3})\)) is strain components, effective strain, first principal strain and maximum principal shear strain, respectively. In the figure, the horizontal axis is logarithmically scaled.  
It needs to be emphasized, the curves in Figure 5 show that the heterogeneity and its monotonously increase with the number of cycles are extremely similar, no matter what a measurement adopted for material deformation: strain component, effective strain, first principal strain or maximum principal shear strain.  
By applying Equation (15) and taking \(E_{a}\) as the parametric variable, the differential entropy curves \(H_{\varepsilon_{ij}}(E_{a},N)\) for all strain components \(\varepsilon_{ij}\) are plotted after the simulation tracking the cyclic process, with logarithmically scaled horizontal axes, as showed in Figure 6. One can see that the differential entropies of all strain components increase monotonously with the number of cycles, like the curves of SDs for them. Due to page limitation, the differential entropy curves for effective strain, first principal strain and maximum principal shear strain are omitted here.
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
## 4 Cell Model Linearization  
The interest of closed-loop control for fast charging has been highlighted in Sections 1 and 3. The design of a linear controller to implement such a loop is thus the objective of this section. The block diagram of the cell model in Figure 1 suggests that the cell is highly nonlinear. This is confirmed by the simulations of this model, which show a high sensitivity to temperature, lifetime stage and charging current level. Such a nonlinear behaviour is taken into account in the approach presented here with a linearized model set that is computed for a set of operating points (temperature, charging current, stage of life) and various optimal charging profiles, and thus side reaction current (\(I_{sr}\)) profiles.
## 3 Proposed Method
### Preliminaries
#### 3.1.1 Network Model  
In the MANET, the locations of nodes is not fixed; therefore, it needs to frequently update its location information to all other nodes. The node location is tracked by GPS, which is used to determine the extra coverage that a node's transmission accomplishes. A MANET is made up of a mobile node count, which is referred to as \(m\) (i.e., \(1<i<m\)). The mobile nodes are arranged in a rectangular area of \(R\times R\)\(unit^{2}\) size. In the defined location, the mobile nodes are deployed uniformly and randomly. The transmission among the mobile nodes is represented by a wireless link among the nodes. The nodes' interconnections are limited by the radio range, i.e., \(R\). The size \(m\) is distributed to each mobile node \((X_{d},Y_{d})\). The nodes travel about the region because of their mobility and the direction. In a MANET, the mobile node can serve as both a host and a router. Depending on the required needs, a single node in the MANET acts as the source or destination. Even though the nodes are movable, the reference value of the nodes in the MANET could be used to determine their location. The routing method can be used to forward data between the nodes in the MANET [31; 32]. The path selection is tough due to the erratic node mobility. The data transmission from the source to the destination can be done in numerous paths by the mobile nodes. The routing protocol requires the appropriate route from source to destination with QoS support. The information is transmitted from the source to the destination when the appropriate pathways are determined with the QoS commitment. The MANET model is given in Figure 1.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 7 Performance Evaluation
### Evaluation in Real Systems
#### 7.2.3 Routing Path Reconstruction  
In addition to the link quality reconstruction, we also verify routing path reconstruction accuracy, which is calculated from the reconstructed link quality matrix. Routing path reconstruction accuracy  
Figure 11: Comparison between LESS-RS and Spline-based approach (SR = 0.5, 0.6, 0.7).  
Figure 12: Evaluation of LESS in real trace (Zhoushan system): (**a**) comparison of RS, K-Spatial sampling and K-Temporal sampling in link quality reconstruction accuracy; (**b**) comparison of RS, K-Spatial sampling and K-Temporal sampling in routing path reconstruction accuracy; and (**c**) evaluation of K-Temporal sampling in topology reconstruction accuracy.  
indicates the capability of LESS in selecting proper next-hop node, although based on link quality reconstruction but is more important than link quality reconstruction in terms of data collection. Figure 12b reveals the relationship between sampling rates and routing path reconstruction accuracy with the three algorithms.  
Obviously, the reconstruction accuracy increases with the growth of sampling rates, and reveals the same patterns between RS and K-S and K-T in Figure 12a. It is worth mentioning that, even when the sampling rate is 0.4, the routing path reconstruction accuracy is over 90% in each algorithm and over 80% with a sampling rate of 0.2. Second, routing path reconstruction is better than that of the link quality, especially in low sampling rates. This is because a certain amount of estimation error may not change the final decision in routing path selection. According to Figure 12b, we can draw the conclusion that utilizing compressive sensing in link quality estimation can effectively guide the choice of the next hop with high accuracy.
# Charge-Line Dual-FET High-Repetition-Rate Pulsed Laser Driver
## 3 System Implementation
### Input Buffers  
In the pulse mode, high current transient states in HEMT transistors are observed. Therefore, the process of switching at inputs (gates) of HEMTs should be as short as possible. Moreover, the low inclination of Trg1 and Trg2 rising edges could be easily affected by interferences caused by glitches coming from the switching process of HEMTs. Therefore, in order to ensure the fast switching of the transistors, each input buffer (between FPGA outputs and HEMT gates) combines four connected in-parallel AC family Complementary Metal Oxide Semiconductor (CMOS) logic gates. The buffers' inputs are directly driven by the FPGA timing subsystem shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Rating** \\ \hline \(\mathrm{U_{DS\,MAX}}\) & 84 V \\ \(\mathrm{I_{D\,SAT}}\) (\(\mathrm{U_{DS}}\) = 6 V, \(\mathrm{U_{GS}}\) = 2 V) & 2 A \\ \(\mathrm{R_{DS\,ON}}\) & \(<\)2 \(\Omega\) \\ Operation frequency & DC to 6 GHz \\ \(\mathrm{P_{diss\,max}}\) & 8 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: GaN High-Electron-Mobility Transistors (HEMT) electrical characteristics.
# Covert Communication for Integrated Satellite-Terrestrial Relay Networks with Cooperative Jamming
## 2 System Model and Problem Formulation
### System Description  
In Figure 1, we establish the ISTRN with cooperative jamming, including one satellite \((S)\) equipped with a multi-beam antenna; a legitimate user \((B)\); one full-duplex terrestrial relay \((R)\), which has a set of transceiver antennas and adopts the AF model; and one eavesdropper \((E)\), which is located near the \(R\). \(S\) utilizes the fixed spot beam to communicate with \(B\), directly covering \(R\) and adjacent \(E\).  
In the considered system model, the satellite \(S\) always transmits source information to \(B\) via relaying. The direct link from \(S\) to \(B\) is unreachable because of the bad weather, building blocks and other heavy fading. \(E\) attempts to detect and confirm whether the transmission from \(S\) to \(B\) is on or off. Therefore, a cooperative jamming scheme for the terrestrial relay is proposed to achieve efficient covert communication.  
\(R\) uses the full-duplex mode, which not only receives the signals from \(S\) as a relay but also generates jamming signals for confusing \(E\) in the first time slot. Then, the retransmission enables source information to be obtained by \(B\) covertly in the second time slot. We consider the worst case, namely, that \(E\) is aware of the possible time when \(S\) may send its source information and owns all the knowledge of the system. (If it owns all the knowledge of the system, \(E\) is able to reduce the uncertainty and enhance its accuracy of detection significantly. The assumption here is the worst case, which enables our model credible and universal [32]).  
Moreover, for the link \(a\to b\) (\(a,b\in\{S,R,E,B\}\)), we define its channel coefficient as \(h_{ab}\). This is an independent, zero-mean circularly symmetric complex Gaussian random variable with unit variance. A block fading environment is considered, implying that discrete-time channel coefficients are unchanged in one block but vary independently between blocks. Lastly, we deem that \(R\), \(E\) and \(B\) are all constantly disturbed by additive white Gaussian noise (AWGN).
# _Pistacia Atlantica_ Desf., a Source of Healthy Vegetable Oil
###### Abstract  
_Pistacia atlantica_, which belongs to the Anacardiaceae family, is an important species for rural people in arid and semi-arid areas. The fruit, rich in oil, is used in traditional medicine for the treatment of various diseases. The oil extracted from this species growing in a northern area of Algeria and its fatty acid composition were previously studied. However, the largest areas where this species is present (traditional cultivation) is located in southern Algeria. Moreover, studies on oil fatty acid composition and essential oil were always conducted separately. This study was performed in order to assess the fatty acid and volatile organic compound composition of _P. atlantica_ vegetable oil. The seeds were collected randomly from Djelfa (300 km South of Algiers, Algeria). Oil content and fatty acid composition were determined by Soxhet extraction. The seeds contained high concentrations of oil (32-67%). The major fatty acids were oleic (39-49%), linoleic (23.6-31%), and palmitic (21.3-26.6%) acids. The ratio of polyunsaturated fatty acids (PUFA) to saturated fatty acids (SFA) indicated that the content of unsaturated fatty acids was approximately three times higher than that of SFA. This ratio is widely used in epidemiological studies and research on cardiovascular diseases, diabetes, and metabolic syndrome. The ratios of \(\omega\)-acids, i.e., \(\omega\)-9/\(\omega\)-6 and \(\omega\)-6/\(\omega\)-3, were 1.3-2 and 18.5-38.3, respectively. Crushed seeds were analyzed by headspace solid-phase microextraction (SPME) coupled with gas chromatography-mass spectrometry. More than 40 compounds were identified, mainly monoterpenes (C\({}_{10}\)H\({}_{16}\)), such as \(\alpha\)-terpinene and terpinolene, but also sesquiterpenes (C\({}_{15}\)H\({}_{24}\)) at lower levels. The value of this species as a source of healthy oil rich in \(\omega\)-3 acid and its effects on cardiovascular disease risk are discussed.  
_Pistacia atlantica_; oil content; fatty acid composition; Solid-Phase Micro-Extraction (SPME); volatile organic compounds Article
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 1 Introduction
However, by resolving the problems and capitalizing on the advantages, 6G technology has the ability to exploit the full potential of VANETs and revolutionize the transportation sector. In the context of VANETs, edge computing [7] can be used to address the challenges associated with data processing, communication, and storage in a highly dynamic and resource-constrained environment. This research will discuss the potential benefits of edge computing in VANETs, the challenges associated with implementing edge computing in VANETs, and the current research trends in this area. One of the main advantages of edge computing is that it can significantly reduce the latency in data processing and communication. By processing data closer to the source, edge computing can eliminate the need to transmit data to a centralized server for processing, which can result in significant delays. In the context of VANETs, reduced latency is particularly important for applications that require real-time processing, such as collision avoidance and emergency response systems. The work is organized as follows. Section 2 discusses the past research work on network simulation in the context of a vehicular environment. Section 3 delves into the proposed research methodology, both experimental and theoretical, while Section 4 discusses the results. Finally, Section 5 contains the conclusions.
## 1 Introduction  
### Cell-Based Impedance Assays  
Over 90% of current biological procedures use traditional radiolabeled or fluorescent probes for understanding complex biological processes of tissues in their native environment [1]. These procedures are less sensitive more invasive and provide end-point results that are harmful to cells. The discovery of a novel, label-free technology to monitor cellular response, morphology, and proliferation provides a distinct advantage to read out drug action, cell-cell interaction, and quantification [2]. The impedance-based cellular assay (IBCA) has emerged as a new and alternative method for dynamic, real-time monitoring of cell kinetics, morphology, and behavior based on impedance change over time. It allows the measurement of electronic signals on sensors, developed due to changes in the biological status of cells, and converts them to digital signals for processing and analysis [3]. The major advantages of real-time cell analysis (RTCA) over conventional approaches include the following: fast and easy detection of cell kinetics and quality attachment with the substrate in real-time; an automated measurement of drug candidate effect on therapeutic target,which provides a highly reproducible data set during the drug development process; and broad application with high sensitivity and accuracy of the system that exceeds limitations from end-point assays [4,5].
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
###### Abstract  
Due to the dynamism of topology, sharing of bandwidth and constraint of resources in wireless nodes, the provision of quality of service (QoS) for routing in mobile ad hoc networks (MANETs) presents a great challenge. Security is another crucial aspect of providing QoS since the existence of malicious nodes present all kinds of threats to MANETs. Although a number of mechanisms have been proposed for protecting MANETs, most of the solutions are only effective for a particular kind of attacks or provide security at the cost of sacrificing QoS. In this paper, we propose a trust-based secure QoS routing scheme by combining social and QoS trust. The primary approach of the proposed scheme relies on mitigating nodes that exhibit various packet forwarding misbehavior and on discovering the path that ensures reliable communication through the trust mechanism. The scheme would select the best forwarding node based on packet forwarding behavior as well as capability in terms of QoS parameters, such as residual energy, channel quality, link quality, etc. We will present an adversary model for packet dropping attack against which we evaluate the proposed scheme. Simulation experiment using Network Simulator-2 (NS2) and under various network conditions show that mixing social and QoS trust parameters can greatly improve security and quality of service routing in terms of overhead, packet delivery ratio and energy consumption.  
mobile ad hoc networks; QoS routing; packet-forwarding misbehavior; trust-based scheme +
Footnote †: journal: Future Internet
## 2 Theoretical Background
### Continuity Conditions
#### 2.5.1 Rigid Joint Model  
For the rigid joint model, there is no relative translation and rotation between the two sub-beams. Therefore, the displacements and rotation of the interface are equal which can be mathematically expressed as:  
\[w^{(11)}=w^{(12)},\ \ u^{(11)}-\frac{h^{(1)}}{2}\phi^{(11)}=u^{(12)}+\frac{h^{(2 )}}{2}\phi^{(12)} \tag{9}\]  
Here, \(w^{(ms)}\), \(u^{(ms)}\), and \(\phi^{(ms)}\) with \(m=1\) denote the transverse and the axial displacement and the rotation.  
Furthermore, the relative displacements \((\Delta u^{(1)},\ \Delta w^{(1)})\) and the relative rotation \((\Delta\phi^{(1)})\) can be expressed as:  
\[\Delta u^{(1)}=u^{(12)}-u^{(11)}=0,\ \ \Delta w^{(1)}=w^{(12)}-w^{(11)}=0,\ \ \Delta\phi^{(1)}=\phi^{(12)}-\phi^{(11)}=0 \tag{10}\]