esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Comparison  
### Neutral Layer Shifting  
Figure 13 shows the neutral layer shifting results from simulation experiment and RSM model prediction respectively. The trends of these two are basically consistent. The severe deformation of the thin-wall tube can account for the negative value of RSM model in this figure. Their relativity can be calculated as follow:  
\[r=\frac{\sum R_{1}R_{2}-\frac{\sum R_{1}\sum R_{2}}{m}}{\sqrt{(\sum R_{1}^{2}- \frac{(\sum R_{1})^{2}}{m})(\sum R_{2}^{2}-\frac{(\sum R_{2})^{2}}{m})}}=80.97\% \tag{23}\]  
where \(R_{1}\) and \(R_{2}\) denote sets of the two results, and \(m\) denotes the number of experimental results as before and \(m=47\). In the same bending condition with the above actual experiments, the bimetallic tube bending is also simulated. Figure 14 shows the neutral layer shifting under different bending angles. The results come from RSM model prediction and simulation extraction respectively. Expect the \(15^{\circ}\), under other bending angle, the relative errors of RSM model to simulation are less than 9.82%. It can be indicated that the RSM model works well and the neutral layer shifting calculated from the model is accurate enough. The \(15^{\circ}\) bending angle has not been contained in the experiment design  
Figure 12: Cutaway view of \(90^{\circ}\) bending section.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Tube** & **Bending Section** & **Theoretical** & **Empirical** & **Spring-back** \\
**Number** & **Number** & **Bending Angle/\({}^{o}\)** & **Bending Angle/\({}^{o}\)** & **Angle/\({}^{o}\)** \\ \hline \multirow{8}{*}{T1} & 1 & 90 & 88.6738 & 1.3262 \\  & 2 & 75 & 74.2861 & 0.7138 \\  & 3 & 60 & 59.6963 & 0.3037 \\  & 4 & 45 & 44.7153 & 0.2847 \\  & 5 & 30 & 29.8035 & 0.1965 \\  & 6 & 15 & 14.9187 & 0.0813 \\ \hline \multirow{8}{*}{T2} & 7 & 90 & 89.0270 & 0.9730 \\  & 8 & 75 & 74.3825 & 0.6175 \\ \cline{1-1}  & 9 & 60 & 59.5063 & 0.4937 \\ \cline{1-1}  & 10 & 45 & 44.6647 & 0.3353 \\ \cline{1-1}  & 11 & 30 & 29.7233 & 0.2767 \\ \cline{1-1}  & 12 & 15 & 14.9065 & 0.0935 \\ \hline \end{tabular}
\end{table}
Table 5: Experimental data.  
because this angle is small, and its results may be non-representative. As a consequence, the relative error under 15\({}^{\circ}\) bending is 15.64% and greater than other relative errors.  
Though analysis of variance, in the factors mentioned above, layered thickness ratio has the greatest influence and interlaminar friction coefficient has the second greatest influence on the neutral layer shifting. Other factors affect the results more slightly comparatively. Diameter-thickness ratio, outer diameter, bending angle, and relative bending radius have sequentially reduced impacts on the neutral layer shifting. The response of the neutral layer shifting to layered thickness ratio and the interlaminar friction coefficient is illustrated in Figure 15a. The response of the neutral layer shifting to layered thickness ratio and diameter-thickness ratio is illustrated in Figure 15b.  
Figure 14: Neutral layer shifting under 15–90\({}^{\circ}\) bending.  
Figure 13: Comparison of neutral layer shifting results.
## 3 Results
### Performance of Multi-System PPP with Precise Products from Different Analysis Centers  
#### 3.4.1 Datasets, Statistics and Processing Strategies  
To assess the performance of multi-system (GNSS/RNSS) PPP with the precise products from different analysis centers, the datasets from 29 globally distributed MGEX stations spanning a week from DOY 138 to 144 in 2020 (i.e., 17-23 May 2020) are employed. It is important to notice that we do not perform a continuous PPP processing with the 7-day data in this study. The seven-day data are divided into several sub-sets (one day from GPS time 00:00:00 to 23:59:30 for each sub-set), and the cross-day data are not processed together. Actually, in this study, the 24-h observation files in RINEX format provided by MGEX are processed day-by-day, and the daily PPP solutions of each station are used for analysis (i.e., the long-term performance rather than the short-term performance for PPP). Thus, a total of 203 PPP experiments based on the daily observations are conducted for each system combination case for each precise product. The geographical distribution of the selected stations is shown in Figure 9.  
All the stations can track the GPS, GLONASS, Galileo, BDS-2 and BDS-3 satellites, while only the stations marked in blue can track the QZSS satellites. The performance of PPP is evaluated in terms of position accuracy and convergence time. For each daily (24-h) PPP solution, the position filter at a specific epoch is considered to have converged when the position errors keep within 10 cm for 10 consecutive epochs from this epoch. We calculate the average value of convergence time over all the day-by-day PPP solutions for all the employed stations and days (i.e., over the 203 PPP experiments with single-day observations at 29 stations). As for the fully converged positioning accuracy, the RMS statistics of the epoch-wise static position errors over the last half hour for all the 24-h sessions from all the selected stations and days are taken as the static positioning accuracy, while the epoch-wise kinematic position errors over the last one hour of each 24-h session are used to compute the kinematic positioning accuracy. For comparison, nine different system combination cases, namely GPS-only PPP (G-PPP), GLONASS-only PPP (R-PPP), Galileo-only PPP (E-PPP), BDS-only (BDS-2/BDS-3) PPP (C-PPP), GPS/GLONASS PPP (GR-PPP), GPS/Gailleo PPP (GE-PPP), GPS/BDS PPP (GC-PPP), GPS/QZSS PPP (GJ-PPP), and GPS/GLONASS/Gallieo/BDS/QZSS PPP (GRECJ-PPP), are adopted in the post-processed scenario. In addition to the four single-system PPP and three dual-system PPP (excluding GJ-PPP), GPS/GLONASS/Gailleo/BDS PPP (GREC-PPP) is also employed in the real-time scenario. The detailed processing strategies for PPP are shown in Table 8. The open source program package RTKLIB is used for the data processing in this paper [41], but we have made improvements in many aspects.  
Figure 9: Geographical distribution of 29 selected MGEX stations. The stations marked in both red and blue can track the GPS, GLONASS, Galileo, BDS-2 and BDS-3 satellites, and only the stations marked in blue can track the QZSS satellites.
## 4 Classification of MAC Protocols Based on Channel
### Cooperative Single-Channel MAC Protocols
#### 4.6.1 CoMACAV  
Cooperative MAC was proposed for autonomous vehicles to provide high network throughput. There are three modes of data transmission on which the protocol works. These are direct transmission (DT), cooperative relaying (CR), and multi-hop relaying (MHR). The main objective of this protocol is to increase throughput by selecting the optimal value of relay (R) produced by the SNR concept. The DT (RTS/CTS) and CR modes of transmission are implemented when nodes are present in the same network. In contrast, the MHR mode is implemented when nodes are in different networks. New control packets such as RRTS, RH, RRR, RCR, and RACK are introduced and depicted in Figure 12.  
All neighboring nodes through RTS/CTS know the SNR of both source and destination nodes. Thus, neighboring nodes that are free then broadcast optimal relays. The node with the highest SNR becomes the optimal relay node and sends RRR to both the source and destination. This increases the reliability of transmission. The analysis is performed through the Markov model. The demerit of this protocol is a delay in transmission due to collision.  
Figure 12: Mechanism of packet exchange: (**a**) DT mode, (**b**) CR mode, and (**c**) MHR mode [89].
# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 3 Energy-Efficient Game-Theory-Based Spectrum Decision
### CRSN Settings
There are two types of C-mode request: normal request and urgent request. When the sink receives at least one urgent request, it decides the operation mode of the next frame will be a C mode, and broadcasts this information on the CCC. When the sink receives normal requests, it does not immediately set the next mode to be a C mode, but waits until the number of frames associated with normal requests has exceeded a certain threshold. If there is no request at all, then the next mode is set to be D mode. The conditions in which  
Figure 1: The modules of (a) C mode and (b) D mode. a sensor node requests C mode are explained in the next section. Whenever a C mode is performed, the previous clustering topology is resetted. In the proposed EGSD scheme, we use the following notations:  
* A set of \(N\) sensor nodes acting SUs \(S=\{S_{1},\,S_{2},\,\ldots,\,S_{N}\}\) with \(status(S_{i})\in\{active,\,passive\}\) and \(class(S_{i})\in\{CH,\,CM,\,SN\}\), \(1\leqslant i\leqslant N\). (Players)
* A set of \(M\) primary users \(T=\{T_{1},\,T_{2},\,\ldots,\,T_{M}\}\) with \(status(T_{j})\in\{active,\,passive\}\), \(1\leqslant j\leqslant M\). (Players)
* A set of (\(L\)+1) licensed channels \(A=\{A_{0},\,A_{1},\,A_{2},\,\ldots,\,A_{L}\}\) and its SNR observed by \(S_{i}\)\(SNR_{i}=\{SNR_{1},\,SNR_{2},\,\ldots,\,SNR_{L}\}\). * A set of \(K\) unlicensed channels \(B=\{B_{1},\,B_{2},\,\ldots,\,B_{K}\}\) and its SNR observed by \(S_{i}\)\(SNR^{\prime}_{i}=\{SNR^{\prime}1,\,SNR^{\prime}2,\,\ldots,\,SNR^{\prime}k\}\). * A common control channel \(C_{control}=A_{0}\). * An operating channel of \(S_{i}\) on current frame \(f=(C_{i})_{f}=A_{x}\) or \(B_{y}\) or \(\O\), where \(1\leqslant x\leqslant L\), \(1\leqslant y\leqslant K\), and \(\O\) means empty set. * A backup channel of \(S_{i}\) on current frame \(f=(C^{\prime})_{f}=A_{x}\) or \(B_{y}\) or \(\O\), where \(1\leqslant x\leqslant L\), \(1\leqslant y\leqslant K\), and \((C^{\prime}_{i})_{f}\
eq(C_{i})_{f}\). * \(Status(A_{x})_{i}\in\{available,\,not\,\,available,\,obsolete,\,\,idle,\,busy\}\), \(1\leqslant x\leqslant L\), observed by \(S_{i}\). * \(Status(B_{y})_{i}\in\{clean,\,noisy,\,unknown\}\), \(1\leqslant y\leqslant K\), observed by \(S_{i}\). * CH type \(=type(CH)_{i}\in\{0,\,1,\,2,\,3,\,4\}\), \(class(S_{i})=(CH)\), \(1\leqslant i\leqslant N\).
# A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks
### Research Contributions  
The major impacts of the proposed protocol are as below:  
* Introduces a context-aware method to distinguish the traffic flows with distinct context information to minimize communication overheads.
* Design a machine learning techniques-based routing that considers k-means and SVM approaches for optimal route selection to deliver reliability and robustness towards network malfunction, dynamic topology, and variable mobility in VANET.
* Adopts packet delivery ratio (PDR) and E2E delay as routing metrics which guarantee that the most reliable route is selected during transmission.
# Performance Analysis of Hard-Switching Based Hybrid FSO/RF System over Turbulence Channels
## 2 System Model
### Bit Error Rate  
For average BER calculations, it is assumed that the signal is modulated with OOK scheme and is then transmitted on either of the active FSO or RF link. It is further assumed that both the links operate at the data rate \(R_{0}\). The conditional error probability for RZ-OOK, as a function of instantaneous SNR \(\gamma\) is given by [23]:  
\[P(e|\gamma)=BER_{RZ-OOK}=\frac{1}{2}erfc\bigg{(}\frac{1}{2}\sqrt{\gamma}\bigg{)}, \tag{15}\]  
The average BER for hybrid FSO/RF system, during a non-outage period, can be written as, [19]:  
\[BER_{FSO/RF}=\frac{B_{FSO}(\gamma_{th-FSO})+F^{FSO}(\gamma_{th-FSO})\times B_{ RF}(\gamma_{th-RF})}{1-F_{out}} \tag{16}\]  
where \(B_{FSO}(.)\) is average BER for the time where the FSO link is activated and \(B_{RF}(.)\) presents average BER for the time where RF link is activated. \(F^{FSO}(\gamma_{th-FSO})\) and \(F_{out}\) are given from (14) and (18), respectively. These terms can be defined as [19]:  
\[B_{FSO}(\gamma_{th-FSO})=\int_{\gamma_{th}}^{\infty}P(e|\gamma_{FSO})f_{FSO}( \gamma_{FSO})d(\gamma_{FSO}) \tag{17}\]  
and  
\[B_{RF}(\gamma_{th-RF})=\int_{\gamma_{th}}^{\infty}P(e|\gamma_{RF})f_{RF}( \gamma_{RF})d(\gamma_{RF}) \tag{18}\]  
By substituting (8) and (15) into (17), the following integral for the estimation of the average BER for the FSO link is obtained:  
\[B_{FSO}(\gamma_{th-FSO})=\int_{\gamma_{th-FSO}}^{\infty}\frac{K_{\alpha-\beta }\Big{(}\alpha\beta/\sqrt{\gamma_{FSO}}\Big{)}^{\frac{\alpha+\beta+1}{2}}erfc \bigg{(}\frac{\sqrt{\gamma_{FSO}}}{2}\bigg{)}\gamma_{FSO}^{\frac{\alpha+\beta -3}{4}}}{\Gamma(\alpha)\Gamma(\beta)}d(\gamma_{FSO}) \tag{19}\]  
While by substituting (9) and (15) into (18) the corresponding integral for the average BER of the RF link, is given as:  
\[B_{RF}(\gamma_{th-RF})=\int_{\gamma_{th-RF}}^{\infty}\frac{erfc\bigg{(}\frac{ \sqrt{\gamma_{RF}}}{2}\bigg{)}e^{-\gamma_{RF}/\sqrt{\gamma_{RF}}}}{2\times \overline{\gamma_{RF}}}d(\gamma_{RF}), \tag{20}\]  
Then by substituting (12), (19) and (20) into (16) the average BER of the whole hybrid FSO/RF system can be estimated.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 3 Encryption for Cloud Computing in Manufacturing
### Conclusions  
Section 3 analyzed the limitations and potentials of both classic and homomorphic encryption methodologies for cloud computing in manufacturing. While homomorphic encryption guarantees data security over the whole data life cycle and a resulting exploitation of the advantages of cloud computing, homomorphic encryption requires higher computation time and, thus, energy. This was proven via a literature review, as well as in an experimental validation. However, the latter also showed that the absolute values of operations with homomorphic encryption are in a reasonable, absolute time span that might be applicable in practice use cases. Therefore, the next section considers possible use cases to analyze if homomorphic encryption can be implemented in manufacturing.
## 4 Results and Discussions
### Results of SEA vs. ATPA  
Air-borne noise was omitted from the SEA model due to the absence of dedicated testing focused solely on the air-borne path, such as knowledge about the transmission loss of the panels [20]. In this context, a comparison is drawn between the results of the SEA model and the structure-borne noise analysis conducted using ATPA.  
Figure 10 compares the noise level prediction of the SEA approach with the structure-borne noise contribution calculated using ATPA.  
As shown in Figure 10, the ATPA and SEA methods predict the same trend for noise pressure, although there are some discrepancies between the results. There are various parameters in an SEA analysis that affect the output, such as the complexity of the model's geometry, the material properties, and the boundary conditions used to describe the interactions between the structure and the cavity. If the boundary conditions are not well-defined or are not representative of the actual environment, the predicted sound pressure levels may not match the measured values. Also, the SEA model may not accurately capture the effects of non-uniformities, irregularities, or discontinuities in the structure. In particular, the interior of the aircraft was in green mode, with visible ribs and some elements (like a bulky emergency exit at the rear part of the cabin), which were not present in the model.  
Furthermore, the accuracy of SEA predictions is strongly influenced by the acoustic damping loss factor, which affects the dissipation of energy in the cavity. In this study, the acoustic damping factor was obtained from impact tests on panels, but a more reliable approach would have been to excite the cavity rather than a panel. This is further discussed in Section 4.5.
## 2 Mathematical Model of a Deeply Coupled Navigation System
\right]\) denote the position vectors in ECEF coordinates, \(r=\sqrt{x^{2}+y^{2}+z^{2}}\), which is the distance to the center of the Earth; \(\widetilde{\Omega}^{e}\) is the matrix of the Earth's rotational angular velocity, \(\omega_{e}=7.292115\times 10^{-5}(rad/s)\); \(\widetilde{F}^{e}\) is the matrix of specific force in the ECEF coordinate system; \(C_{b}^{e}\) is the direction cosine matrix between the body coordinate system and the ECEF coordinate system; \(\Lambda\) is the clock error coefficient matrix; \(\omega_{T}=\left[0\;\omega_{f}\right]^{T}\) is the clock error vectors; and \(\omega_{f}\) is the clock drift error vectors. To make sure that the system stability could be analyzed, [PERSON] studied the observability of the integrated system [12]. According to his conclusion, when the system consists of three antennas which form a triangle of sufficient area, observability would be guaranteed under all circumstances. Thus, this paper continues on the premise that the observability of the system is guaranteed by the system hardware.
## 3 Simulation Experiments and Analysis
### Performance Comparison for Different Values of N  
In this section, we investigate the impact of the number of different class samples in the support set, denoted as N, on the network's recognition accuracy under the N-way 5-shot setting. For each value of N, namely N = 3, 4, 5, and 6, multiple training sessions were conducted, and the average values were computed. The simulation results are shown in Figure 7.  
Figure 6: Variation of recognition accuracy with SNR ratio for different K values.  
From Figure 7, it can be observed that as the number of class samples N increases, the performance of the network decreases. When N = 3, the recognition accuracy at 18 dB reaches 91.70%. But when N = 6, the recognition accuracy only reaches 78.36%. With an increase in N, this makes the classification task more complex. This complexity challenges the convergence performance of network training and increases the difficulty of learning for the network model. Additionally, as the differences between classes may become more subtle, the model requires stronger representation learning ability and generalization capability to achieve good performance on new tasks, ultimately leading to a decrease in recognition performance. However, with excessively small values of N, the model only needs to learn a few classes, which may result in overfitting.
## 1 Introduction  
When semiconductors, metals, and other materials have ordered arrays of atoms, they form crystal material. Rutherford backscattering spectrometry (RBS) has been widely used for investigations of solid-state surface layers [1]. The strong influence of the crystal lattice on the trajectories of ions penetrating the solid-state surface layers is known as channeling. A combination of RBS and channeling techniques provides the location of impurities, atoms, and defects distributions in crystals. This information is hardly obtainable from other measurements. In the case of standard RBS, we usually assume the random sample orientation and normal yield is obtained under non- channeling conditions. When the crystal sample is aligned, that is, the direction of the incident beam is oriented with respect to some axis or plane of the crystal (axial or planar channeling), the number of backscattered ions decreases considerably. The procedure to achieve such orientation is called \"beam alignment\". For example, in the case of \({}^{4}\)He+ primary beam in energy ranges from hundreds of keV to several MeV, the sample surface layer of up to 1 \(\upmu\)m can be analyzed. The ions penetrate deeply into the crystal without substantially perturbing the lattice [1; 2; 3; 4].  
In the case of RBS/C, it is performed through gradually tilting the sample and monitoring the registered backscattered ions; this causes a decrease in the RBS energy spectrum yield. The dips in the angular distribution of the backscattering yield are used for crystal alignment. To find such channeling dips, a number of energy spectra of backscattering ions has to be measured while the incident-beam direction with respect to the crystal is changed simultaneously in small angular steps [2; 5].  
The 6 MV Tandetron control software, ARGUS, allows for single or multiple consecutive measurements to be run given in the batch file [6; 7]. The typical channeling measurement, including sample crystal alignment, requires collecting typically hundreds of RBS spectra, which in manual evaluation, is cumbersome and time-consuming. Hence, the process automatization is required in order to increase the efficiency of RBS/C measurement. In fact, because of the different experimental setups, control systems, other software, options, and etc., the ion beam facilities are developing their own tailor-made applications and software tools including RBS/C to support the competences in ion beam analysis (IBA) [8; 9; 10; 11].
## 2 Material and Methods
### Airborne Laser Scanning Processing Chain
#### 2.2.4 Accuracy Assessment of DSM  
Two assessments were accomplished in order to validate the accuracy of estimated DSM. First, the height of a flat surface (i.e., quay, dock) in DSM was compared to the respective one acquired with an onsite survey. Second, the derived aquatic reed heights were assessed on their elevation values in comparison with those measured at each AOI. Distribution type of the height residuals was calculated with the sample skewness and its significance. For a normal distribution, the Mean Error (ME), Standard Deviation (SD), and the Root Mean Square Error (RMSE) were obtained for evaluating the closeness agreement between the observed and the modelled height of reeds (Table 2). The rasterisation of the point clouds was performed using a moving (tilted) plane interpolator (movingPlanes) in OPALS software. Detailed information about this interpolator can be found in [35].
## 2 Architecture of IoMT  
Not all applications and technologies use the same IoMT architecture. This is important to know before studying various aspects of IoMT. Each technology has its own set of guidelines and often claims to be the best [5] (for a particular case). Existing IoMTsystems [6] typically have three main layers as shown in Figure 1: perception, network, and application. These layers include all the stages through which data pass, from the collection of patient information via sensors and wearable devices to the storage, analysis, and visualization stage by the patient and medical staff.  
The perception layer is the foundational layer of the IoMT architecture. This layer ensures the precise sensing of the parameters related to health issues [7]. Different types of sensors, especially those that can be implanted or attached to the body, such as a pacemaker, smart watch, etc., could be used to read and collect data about the patient(s). The collected data are transmitted as raw values, i.e., without any processing, via different communication technologies to the network layer [8]. This second layer forwards the data to the processing units (i.e., cloud) through the IoT gateway [9]. Next, the cloud basically performs the analysis. If some changes are detected for the patient's health data, this would carry important meaning [10]. The changes are then transmitted to the application layer and presented through remote monitoring, such as in a smartphone or a dedicated Access Point (AP), to the physicians for emergency response (such as, quantity, prescription or change of dosage of different medications) and to the patient(s) or for any further actions.