esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles
### Improve CELMD  
Compared to the basic CELMD algorithm, the improved CELMD utilizes the empirical optimal envelope method to compute the envelope curve instead of the sliding average method, the empirical optimal envelope method has been applied by researchers to obtain envelope curves in EMD, achieving favorable results. However, it has not yet been applied to LMD. Furthermore, it employs mirror extension and endpoint correction methods to further reduce the endpoint effect, these methods were often used separately in the past. Additionally, it utilizes the correlation coefficient to filter out the main components [33]. The specific steps are as follows:  
1. First item; add white noise signals \(a_{1}\) with an amplitude ratio coefficient \(\alpha\) to the original signal \(X(t)\), and perform endpoint correction and mirror extension to obtain the processed signal. \[X_{1}(t)=X(t)+\alpha a_{1}\] (3)Second item; utilize the empirical optimal envelope method to obtain the empirical optimal upper envelope curves \(\mathrm{c}_{+\mathrm{n}}(\mathrm{t})\) and lower envelope curves \(\mathrm{c}_{-\mathrm{n}}(\mathrm{t})\) of the signal, and calculate the local mean function \(\mathrm{f}_{1}(\mathrm{t})\) and local envelope function \(\mathrm{g}_{1}(\mathrm{t})\). \[\begin{cases}\mathrm{f}_{1}(\mathrm{t})=\frac{\mathrm{c}_{+\mathrm{n}}( \mathrm{t})+\mathrm{c}_{-\mathrm{n}}(\mathrm{t})}{2}\\ \mathrm{g}_{1}(\mathrm{t})=\frac{\mathrm{c}_{+\mathrm{n}}(\mathrm{t})-\mathrm{ c}_{-\mathrm{n}}(\mathrm{t})}{2}\end{cases}\] (4)
3. Third item; demodulate the signal \(\mathrm{X}_{1}(\mathrm{t})\) to obtain a frequency-modulated signal \(\mathrm{s}_{\mathrm{n}}(\mathrm{t})\), treating it as the original signal, and repeat the second item until the function becomes a pure frequency-modulated signal. \[\mathrm{s}_{\mathrm{n}}(\mathrm{t})=\frac{\mathrm{X}_{1}(\mathrm{t})-\mathrm{ f}_{\mathrm{n}}(\mathrm{t})}{\mathrm{g}_{\mathrm{n}}(\mathrm{t})}\] (5)
4. Fourth item; obtain the first PF (Product Function) component of the signal. \[PF_{1}(t)=g_{1}(t)g_{2}(t)\cdots g_{n}(t)s_{n}(t)\] (6)
5. Fifth item; separate the PF component from the original signal and repeat items 2-4 until all PF components and residual components of the signal are obtained. \[\begin{cases}\mathrm{X}_{11}(\mathrm{t})=\mathrm{X}_{1}(\mathrm{t})-\mathrm{ PF}_{1}(\mathrm{t})\\ \mathrm{X}_{1}(\mathrm{t})=\sum\limits_{\mathrm{i}=1}^{n}\mathrm{PF}_{1}( \mathrm{t})+\mathrm{u}_{1}(\mathrm{t})\end{cases}\] (7)
6. Sixth item; repeat items 1-5 for an even number of rounds. In each round, the added white noise has opposite signs between consecutive rounds. Obtain the corresponding PF components and residual components for each round.
7. Seventh item; average the PF components and residual components obtained from multiple rounds of decomposition. Select the main components based on correlation coefficients and add them together to obtain the final signal.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Cnn
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Year** & **Author** & **The Innovation of the Paper** & **Dataset** & **Evaluation Parameter** & **The Technology of DL** \\ \hline
2017 & [PERSON] et al. [31] & CNN & Created dataset 1 & The overall RSR is 93.7\%. & CNN architecture; Two dimensional time frequency diagram. \\ \hline
2017 & [PERSON] et al. [45] & Modulation recognition based on CNN. & Unspecified & High SNR area is close to 100\%. & AlexNet Model; CNN. \\ \hline
2017 & [PERSON] et al. [40] & Improved AMC method on CNN. & Created dataset 1 & The accuracy is up to 90\%. & CNN architecture \\ \hline
2018 & [PERSON] et al. [42] & A novel sparse-filtering criterion; Unsupervised pre-train. & Created dataset 1 & Accuracy higher than 95\%. & Sparse-Filtering CNN; Unsupervised pretraining. \\ \hline
2018 & [PERSON] et al. [43] & Modulation classification based on CNN. & Created dataset 1 & The classification accuracy reached 97.1\%. & AlexNet; GoogLeNet. \\ \hline
2018 & [PERSON] et al. [44] & VHF radio signal modulation classification based on CNN. & Created dataset 1 & The classification accuracy can reach 99\%. & CNN \\ \hline
2018 & [PERSON] et al. [46] & Time domain features are used to train the CNN classifier. & RadioML2016.10a & The classification accuracy is up to 99\%. & CNN \\ \hline
2018 & [PERSON] et al. [47] & Modulation Recognition Based on Residual Network. & RadioML2018.01a & Up to 94\% accuracy. & Deep residual network \\ \hline
2018 & [PERSON] et al. [35] & CNN & Create spectrum image. & The average recognition rate can reach 95\%. & CNN \\ \hline
2018 & [PERSON] et al. [36] & Combination of time frequency distribution and CNN. & Generating rat spectrum with Matlab. & Classification accuracy up to 100\%. & Blind identification method; CNN architecture. \\ \hline
2018 & [PERSON] et al. [48] & Supervision model based on CNN. & Collected data & Achieve 2\% error rate. & Semi-supervised learning; CNN. \\ \hline
2018 & [PERSON] et al. [49] & CNN; A preprocessed signal representation. & RadioML2016.10a & The accuracy is improved by 8\%. & CNN \\ \hline
2018 & [PERSON] et al. [50] & Improved CNN. & RadioML2016.10a & The accuracy can reach 93\%. & CNN \\ \hline
2019 & [PERSON] et al. [32] & Correction module CM + CNN. & RadioML2016.10a & The accuracy is up to 90\%. & Calibration module cm; CNN architecture. \\ \hline
2019 & [PERSON] et al. [33] & CNN based on Transfer Learning. & Created dataset 1 & The overall RSR is up to 95.5\%. & Image fusion algorithm; CNN of transfer learning. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Radio modulation recognition networks based on CNN.
## 2 State-of-the-Art and Related Work
### Discussion of the State-of-the-Art  
It can be concluded that statistical tolerance analysis of systems in motion considering joint clearance has been steadily improved over the past decades and has already been partly implemented in commercial software tools. However, kinematic joints are thereby mostly presented as ideal and joint clearance is thus still neglected. For the 3D-printing of non-assembly mechanisms including kinematic joints, the consideration of joint clearance is inevitable, as these clearances are comparatively large to ensure the separability of the parts after manufacturing and thus have to be considered for a realistic representation of the motion behaviour [11]. Another significant drawback of FDM is the resulting, comparatively high, geometric deviation, which is strongly influenced by the choice of process parameters. Thus, it has been a constant research topic in the last few years. Especially for non-assembly mechanisms part deviations play a crucial role for their functionality and have to be taken into account in the tolerance analysis. Predicting geometric deviations of printed parts based on process parameters is therefore purposeful as it offers the possibility of considering their influence on the functionality of the mechanism, so the design and the process parameters can be adapted before fabrication. In particular in FDM, it is essential, as there is currently no specific guideline for tolerancing and hence these values have to be estimated [31]. A process-oriented prediction of part deviations and resulting joint clearances overcomes this drawback by taking the influence of the manufacturing process directly into tolerance analysis [32]. ML techniques therefore offer suitable algorithms and have already proven its applicability in the field of AM [29] and tolerance analysis [7].  
In summary, it can be stated that there are already some promising approaches for analysing systems in motion affected by clearance. However, these approaches focus primarily on conventionally manufactured and assembled mechanisms, neglecting the potential but also the inherent challenges of additively manufactured non-assembly mechanisms. For ensuring their functionality and thus fully exploiting the potential of 3D-printing in this field, existing approaches have to be further enhanced with process-specific information, for example, through MBS and empirical predictive models to deal with the main challenges, comparatively large joint clearances and geometric part deviations resulting from the FDM process.
## 2 Experimental Methods
### Design of the Platform  
The platform design was not only based on the heave, roll, and pitch motion but also needed to consider the supporting weight and payload. Moreover, the inverse kinematics and mathematical calculation software Matlab was used to determine the motion trajectory and motor output angle of the multi-axis motion platform before the conceptual design. Based on the results of calculation, the lengths of proper crank linkage mechanisms could be obtained. After the preliminary design was completed, the mechanical performance of the mechanisms was simulated and characterized through analysis software to determine the required optimal dimension and geometry of the crank linkages. The platform designed in this paper contained four parts: a lower plate (motor and reducer), crank mechanism (crank and linkage), upper plate, and upper rotating disk in the middle (see Figure 1). Each motor was combined with reducer to increase the outputs and connected with the crank by  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline Initial height & 0.3 m \\ Roll and pitch angle & \(\pm 20^{\circ}\) \\ Heave stroke & \(\pm 20\) cm \\ Yaw angle & \(\pm 180^{\circ}\) \\ Supporting weight of platform (including tester) & 400 kg \\ \hline \hline \end{tabular}
\end{table}
Table 1: The overall specifications.  
Figure 1: Definition of the platform motion.  
a revolute pair. Both of the cranks and the linkages were connected in the same way by rotating pair. A spherical pair was installed between the upper platform and the linkage. The crank mechanism transmitted input to the upper plate to produce the upper plate motion. The rotating disk above the upper plate could rotate independently by a motor to rotate \(\pm 180^{\circ}\). The schematic illustration is shown in Figure 2.  
The DoF calculation is described by Equation (1). This platform had nine component pairs, three spherical pairs, and six rotation pairs. This platform was contained at the central axis of rotation, so DoF can be calculated by Equation (1),  
\[DoF=6(N-1)-\sum(6-i)C_{i} \tag{1}\]  
where \(N\) indicated the amount of connecting rods, and \(C_{i}\) was the amount of kinematic pairs which had i \(DoF\).
# 5G-IPAKA: An Improved Primary Authentication and Key Agreement Protocol for 5G Networks
###### Abstract  
The 3 rd generation partnership project (_3 GPP_) has been enhancing the security of the 5G AKA (authentication and key agreement) protocol. However, there may still be some shortcomings in the latest version of the 5G AKA protocol. According to the analysis of the latest version of the 5G AKA protocol, this paper points out seven of its shortcomings. To overcome these shortcomings, an improved primary authentication and key agreement protocol for 5G networks is proposed, which is named 5G-IPAKA. Compared with the latest version of the 5G AKA protocol, the main improvements include that the pre-shared key between the user equipment (_UE_) and the home network (_HN_) is replaced with a derivation key as the pre-shared key, the challenge--response mechanism for the serving network (_SN_) is added, the mutual authentication and key confirmation occurs between the _UE_ and the _SN_, and the message authentication code (_MAC_) failure procedure is replaced with a timeout mechanism on the _HN_. Then, the 5G-IPAKA protocol is proven secure in the mixed strand space model for mixed protocols. Further discussion and comparative analysis show that the 5G-IPAKA protocol can overcome the above shortcomings of the latest version of the 5G AKA protocol, and is better than the recently improved 5G AKA protocols. Additionally, the 5G-IPAKA protocol is efficient and backward-compatible.  
Keywords:AKA (authentication and key agreement); 5G AKA; 5G-IPAKA; mixed strand space model; pre-shared key; challenge-response; timeout mechanism +
Footnote †: journal: Information
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 3 Proposed FLDHDT
### Defuzzifier
#### 3.2.3 Handover Decision  
In the handover decision stage, the HOM and TTT values obtained from the preceding stage are used to make the handover decision. In addition to dynamically adjusting the HOM and TTT, only the neighboring BSs that make an angle of less than \(\pm\)90\({}^{\circ}\) with the moving direction of the UE are target BS candidates. This condition ensures that only the BSs in front of the UE can be the target BS candidates to have a more accurate selection  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **In** & \multicolumn{2}{c}{**Out**} \\ \hline
**SINR** & **Speed** & **HOM** & **TTT** \\ \hline bad & fast & C- & C- \\ bad & mid\_V & C & B- \\ bad & slow & C+ & A- \\ mid\_P & fast & B- & C \\ mid\_P & mid\_V & B & B \\ mid\_P & slow & B+ & A \\ good & fast & A- & C+ \\ good & mid\_V & A & B+ \\ good & slow & A+ & A+ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Rule base of inference engine.  
Figure 8: Membership function of linguistic HOM.  
of the target BS within a relatively short time. The pseudocode of the handover decision procedure is presented in Table 4. The UE periodically sends its measurement reports, and all calculations are performed at the serving BS. When the SINR with respect to a certain neighboring BS is greater than the SINR of the serving BS plus HOM for a duration of TTT and the angle between the UE moving direction and the neighboring BS is less than \(\pm 90^{\circ}\), handover from the serving BS to the neighboring BS is performed. If multiple candidate BSs simultaneously arrive at the handover decision, the BS with the largest SINR is selected as the handover target.
# Simulating and Evaluating a Real-World ElasticSearch System Using the RECAP DES Simulator
## 2 Related Work  
Web search engines, such as ES, are multi-layered complex systems. Therefore, system configuration and performance optimisation can consume significant time and resources. Using a live environment for testing, experimentation and performance evaluation are not always feasible due to cost and scale limitations. Simulation frameworks can be used as a better alternative to understand system behaviour and minimise real system testing effort [18].  
There is a relative dearth of literature on simulating web search engines. There are a few research articles in this field. For instance, the Discrete Event System Specification (DEVS) formalism has been used to build search engine simulation models. [PERSON] al. [19] used DEVS to model large-scale web search engines. To validate their approach, they compared web search engines through MPI implementation and process-oriented simulation. The DEVS approach was also used for search engine user behaviour modelling by [PERSON] et al. [20]. Combining timed, coloured Petri Nets, process-oriented simulation and circulating tokens that represented search engine sequences of operations, they were able to measure the computational costs of search engine queries [20]. Both [PERSON] et al. [19] and [PERSON] et al. [20] used DEVS as their approach and data sets sourced indirectly from hyperscale search engines--i.e., Yahoo! and AOL--however, it is unclear to what extent the search engine reflects an actual real-world implementation and supports real-world feasible decisions. Indeed, the perceived utility of their outputs from the data set source is not confirmed. [PERSON] et al. [21] pursued a mathematical modelling approach, developing an adaptive and selective approach for expressing the characteristics of a search engine based on information space constraints. While interesting academically, the data used to test their model were limited and lacked real-world validation.  
As discussed in the previous section, [PERSON] et al. [16] validated the RECAP DES using real-world ES trace data with an SME. They found that RECAP DES could predict system performance at different scales in an efficient, precise and accurate manner. Despite the popularity of ES search engines, the paper by [PERSON] et al. [16] is one of the few works that specifically looks at simulating ES search engines. In this paper, we further validate, extend and adapt the RECAP DES to model and simulate the performance of another real-world SME use case that uses an ES search engine on a public cloud. In this respect, the study differs from previous works in that it focuses on data sets directly sourced from an SME to address a live real-world issue for the case site, the utility of which is confirmed by the case site. Furthermore, it uses a simulation framework built upon CloudSim, one of the most popular and accessible cloud computing simulation frameworks. This decision provides researchers and practitioners with greater scope for future use and extension.
## 2 Methods
### Numerical Conditions
#### 2.1.2 Mesh Development  
The ICEM software (ANSYS 16.0, Canonsburg, PA, USA) was employed to mesh the computational domain. The number of mesh nodes corresponding to the length, width, and height of the computational domain was \(220\times 120\times 100\), respectively, and the total number of elements was 2,640,000. The height of the first layer mesh was 0.001 mm and Y\({}^{+}<1\), which met the calculation requirements. The mesh around the VGs consisted of 50 nodes along the length direction and 40 nodes along the height direction. The mesh distribution can be seen in Figure 1.  
The applied boundary conditions were the velocity inlet and pressure outlet. The upper boundary of the computational domain was set as an Euler wall, the bottom boundary and the VGs were set as adiabatic non-slip walls, and the two sides of the computational domain were set as symmetrical boundaries. The dimensions of the computational domain are shown in Figure 2. A turbulent velocity boundary condition was set at the inlet, where the inlet turbulence velocity type is given as follows:  
\[u=\left\{\begin{array}{cc}U\Big{(}\frac{y}{\delta}\Big{)}^{1/7}&y<\delta\\ U&y\geq\delta\end{array}\right., \tag{4}\]  
where \(u\) is the local velocity, \(\delta\) is the thickness of the boundary layer, \(U\) is the main velocity, and \(y\) is the normal height of the wall.
## 2 Deflecting Cavity Design  
The bunch length can be calculated in the deflecting cavity measurement by the following equation:  
\[\sigma_{t}=\frac{\beta^{2}W}{\omega qV_{def}D}\sqrt{\sigma_{y}{}^{2}-\sigma_{ y}{}_{0}{}^{2}} \tag{1}\]  
and the resolution formula is  
\[\Delta_{t}=\frac{\beta^{2}W\sigma_{y,0}}{\omega qV_{def}D} \tag{2}\]  
where \(\beta\) is the velocity of electron divided by light velocity, \(W\) is the total energy of the electron beam including kinetic energy and rest energy, \(\omega\) is the angle frequency of the microwave, \(V_{def}\) is the effective deflecting voltage, \(D\) is the drift distance, \(\sigma_{y,0}\) and \(\sigma_{y}\) are the vertical RMS bunch size at the screen with the deflecting cavity off and on, respectively.  
To achieve the resolution of less than 200 fs for the 150 keV electron beam, numerical analysis shows that the 1.3 GHz cavity is expected to provide a deflecting voltage of 50 kV while assuming \(\sigma_{y,0}\) is 0.1 mm and \(D\) is about 0.6 m.
## 3 Results And Discussion
### Throughput  
The graph shown in Figure 2 indicates the throughput under different velocities of the mobile node. This analysis uses four kinds of wireless channels with different groups of nodes. The speed of the portable hub (mobile node) increases the effects on the performance of the node and the network-based scenario. In the case of different velocities, the condition of handoff changes and reduces the packet drop and delay effect on the parameter of the throughput. The graph indicates a significant improvement in throughput (i.e., it acquires more than 10 packets/Ms for the velocity of 20 km/h). Furthermore, with an increase in velocity, it increases the throughput compared to the random, RSS-GA, and RSS-CS methods, respectively.  
This improvement is because, in the proposed approach, the use of hybrid optimization helps in improving the static base algorithms, such as RSS. The use of RSS helps in the selection of a static threshold, and this static threshold is optimized via an optimization algorithm. In most of the cases, the optimization approach does not converge and provides a random answer, which further reduces the performance time and the rate of packet drop.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Transmit power & (6, 10, 12, 4) Mw \\ Bandwidth factor & (2, 2, 3, 3, 4) Mbps \\ The bandwidth of weight factor & (3, 3, 4, 4, 4) \\ A signal strength weighting factor & (0.4, 0.23, 0.5, 0.45) \\ The number of mobile nodes & (20, 40, 32, 0.60) \\ Communication rate & 1000 kb/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 2: Throughput of the hybrid model.  
In the proposed approach, the cuckoo search and genetic algorithm are also used. This methodology iteratively improves the performance of the cuckoo search.
# Scalp-Implanted Ultra-Wideband Circularly Polarized MIMO Antenna for Biotelemetry Systems
###### Abstract  
This paper presents an innovative, compact, dual-element, implantable, ultra-wideband, circularly polarized multiple-input multiple-output (MIMO) antenna designed to operate within the 2.45 GHz industrial, scientific, and medical band, and both of its radiating units are circularly polarized antennas with polarization diversity. Specifically, antenna-1 exhibits left-handed circular polarization properties, while antenna-2 demonstrates right-handed circular polarization properties. The slots in the radiating patch and ground plane help the antenna achieve 690 MHz (2.14-2.83 GHz) ultra-wide bandwidth characteristics and circularly polarized characteristics. Additionally, a slit connecting two U-slots on the ground plane allows the antenna to achieve a wide effective circularly polarized axial ratio bandwidth of 400 MHz (2.23-2.63 GHz). The antenna is compact, with dimensions of 0.065 \(\times\) 0.057 \(\times\) 0.0042 \(\lambda_{0}\)\({}^{3}\) (\(\lambda_{0}\) represents the free-space wavelength corresponding to the lowest operating frequency). The proposed antenna system's performance was evaluated with a seven-layer homogeneous human head model, a real human head model, and minced pork. This evaluation revealed that the antenna attained a peak gain of \(-24.1\) dBi and an isolation level of 27.5 dB. Furthermore, the assessment included the antenna's link margin (LM), key MIMO channel characteristics, and Specific Absorption Rate (SAR) metrics. The results indicate that the antenna performs exceptionally well.  
biotelemetry; circular polarization; multiple-input multiple-output; polarization diversity; ultra wideband +
Footnote †: journal:  
00 2024, 24, 7522. [[https://doi.org/10.3390/s24237522](https://doi.org/10.3390/s24237522)]([https://doi.org/10.3390/s24237522](https://doi.org/10.3390/s24237522))