esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Comparison with Results of Previous Works  
This section provides comparisons of accuracy rates between optimal approach BHHO\({}_{TV4}\) in this research and its similar FS approaches introduced in previous studies. Results of BHHO\({}_{TV4}\) are compared with results of SSA in [58], WOA in [59], Grasshopper Optimization Algorithm (GOA) in [60], GSA boosted with evolutionary crossover and mutation operators in [61], GOA with Evolutionary Population Dynamics (EPD) stochastic search strategies in [62], BDA [35], hybrid approach based on Grey Wolf Optimization (GWO) and PSO in [12] and Binary Butterfly Optimization Algorithm (BOA) [63]. As in Table 13, it can be seen that the proposed approach BHHO\({}_{TV4}\) has achieved the best accuracy rates on twelve datasets compared to results presented in previous studies on the same datasets. We can also observe that BHHO\({}_{TV4}\) reached the highest accuracy rates on six datasets. In addition, the F-test results indicate that BHHO\({}_{TV4}\) is ranked as the best in comparison with results of other algorithms used in preceding works.  
In general, the results reflect the impact of the adopted binarization scheme on the performance of HHO in scanning the binary search space for finding the optimal solution (e.g., the ideal or near to the ideal subset of features). It is evident that the utilized time-varying TFs, in particular, TV\({}_{V4}\) can remarkably enhance the exploration and exploitation of the HHO algorithm. A potential key factor behind the superiority of BHHO\({}_{TV4}\) is that changing the shape of TV\({}_{V4}\) transfer function over generations has enabled the HHO  
Figure 5: Convergence curves of BHHO\({}_{TV4}\) versus other competitors on M-of-n, penglungEW, SonarEW, SpectEW, Tic-tac-toe, Vote, WaveformEW, WineEW, and Zoo datasets.  
algorithm to obtain an appropriate balance between exploration and exploitation phases and boosted the HHO algorithm to reach areas containing highly valuable features in the search space. Furthermore, similar to many materialistic algorithms, HHO suffers from the problem of sliding into local optima. The accuracy rates of BHHO\({}_{TV4}\) compared to other algorithms prove its superior capability in preserving the population diversity during the search procedure. Hence, preventing the occurrence of an early convergence problem.
## Problem Description and Contribution  
### Problem Description  
Consider an \(n\)-th dimensional uncertain nonlinear SISO system,  
\[\left\{\begin{array}{c}\dot{x}_{1}(t)=x_{2}(t),\;x_{1}(0)=x_{10}\\ \dot{x}_{2}(t)=x_{3}(t),\;x_{2}(0)=x_{20}\\ \vdots\\ \dot{x}_{n}(t)=f(t,x_{1}(t),x_{2}(t),\ldots,x_{n}(t))+w(t)+u(t),x_{n}(0)=x_{n0} \\ y(t)=x_{1}(t)\end{array}\right. \tag{1}\]  
where \(u(t)\in C(R,R)\) is the control input, \(y(t)\) is the measured output, \(f\in C(R_{tt},R)\) is an unknown function, \(w(t)\in C(R,R)\) is the exogenous disturbance, \(x(t)=(x_{1}(t),x_{2}(t),\ldots,x_{n}(t))\) is the state vector of the system, and \(x(0)=(x_{10},x_{20},\ldots,x_{n0})\) is the initial state. \(L(t)=f+w(t)\) is therefore the \"generalized disturbance\" [1]. By adding the extended state \(x_{n+1}(t)\stackrel{{\text{def}}}{{=}}L(t)=f+w(t)\) to Equation (1), it can be written as,  
\[\left\{\begin{array}{c}\dot{x}_{1}(t)=x_{2}(t),\;x_{1}(0)=x_{10}\\ \dot{x}_{2}(t)=x_{3}(t),\;x_{2}(0)=x_{20}\\ \vdots\\ \dot{x}_{n}(t)=x_{(n+1)}(t)+u(t),x(t)\end{array}\right\}=x_{n0} \tag{2}\] \[\dot{x}_{n+1}(t)=\dot{f}(t,x_{1}(t),x_{2}(t),\ldots,x_{n}(t))+ \dot{w}(t),x_{n+1}(0)=x_{n+1,0}\] \[y(t)=x_{1}(t)\]  
Let \(\Delta(t)=\dot{L}(t)=\frac{dL}{dt}\); it is required to estimate the state vector \(x(t)\) and the generalized disturbance \(x_{n+1}(t)\) of the nonlinear system (Equation (2)) in the presence of the system uncertainties, exogenous disturbances, and measurement noise. To solve the above estimation problem, a conventional LESO is given as per [1]:  
\[\left\{\begin{array}{c}\dot{\hat{x}}_{1}(t)=\hat{x}_{2}(t)+\beta_{1}(y(t)- \hat{x}_{1}(t))\\ \dot{\hat{x}}_{2}(t)=\hat{x}_{3}(t)+\beta_{2}(y(t)-\hat{x}_{1}(t))\\ \vdots\\ \dot{x}_{n}(t)=\hat{x}_{n+1}(t)+u(t)+\beta_{n}(y(t)-\hat{x}_{1}(t))\\ \dot{\hat{xwhere \(\beta_{i}\) is a constant observer gain to be tuned, \(i=1,2,\ldots\ n+1\). The LESO gains \(\beta_{i}\) are selected as:  
\[\beta_{i}=a_{i}\omega_{0}^{i},\ i=1,2,\ldots,n+1 \tag{4}\]  
where \(\omega_{0}\) is the LESO bandwidth, \(\beta_{i},i=1,2,\ldots,n+1\) are selected such that the characteristic polynomial, \(s^{n+1}+\beta_{1}s^{n}+\ldots+\beta_{n}s+\beta_{n+1}=\left(s+\omega_{0} \right)^{n+1}\) is Hurwitz. The binomial coefficients \(a_{i},i=1,2,\ldots,\rho+1\) are defined as [21]:  
\[a_{i}=\frac{(n+1)!}{i!(n+1-i)!},\quad 1\leq i\leq n+1 \tag{5}\]  
However, for perfect estimation of the system states \(x(t)\) and the generalized disturbance \(x_{n+1}(t)\), large LESO bandwidth \(\omega_{o}\) is required. Thus, tolerating noise and increasing H/W complexities. In contrast, reducing \(\omega_{o}\) leads to large estimation errors \(x_{i}(t)-\delta_{i}(t)\), \(1\leq i\leq n+1\). Consequently, to solve the above estimation problem with minimum estimation errors as compared to that of LESO, a nested LESO is proposed to estimates \(x(t)\) and \(x_{n+1}(t)\) without increasing \(\omega_{o}\).
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 2 The Mathematical Model and Characteristics of the B1C/L1C Signal
### Code Tracking Accuracy  
The general expression of the standard deviation of the code-tracking error produced by the thermal noise of the noncoherent code loop phase detector can be expressed as follows [35]:  
\[\begin{array}{c}\sigma_{IDLL}=C\times\sqrt{\frac{B_{L}(1-0.5B_{L}T_{p})- \frac{B_{p}^{f/2}}{J_{B}/2}\,G_{L}(f)\sin^{2}(\pi fdT_{c})df}{(2\pi)^{2}( \text{C}/\text{N}0)!\int_{-\frac{B_{p}^{f/2}}{J_{B}/2}}^{B_{p}/2}G_{L,R}(f) \sin(\pi fdT_{c})df]^{2}}}\cdot\\ \sqrt{1+\frac{1}{T_{p}}\frac{\int_{-\frac{B_{p}^{f/2}}{J_{B}/2}}^{B_{p}/2}G_{L }(f)\cos^{2}(\pi fdT_{c})df}{(\text{C}/\text{N}0)!\int_{-\frac{B_{p}^{f/2}}{ J_{B}/2}}^{B_{p}/2}G_{L,R}(f)\cos(\pi fdT_{c})df]^{2}}}\cdot\\ \end{array} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**NUM** & **Signal Name** & **Modulation** & **Power Distribution** & **Phase Relationship** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{B1C} & BOC (1,1) & \multirow{2}{*}{1:3} & \multirow{2}{*}{orthogonal} \\  & & QMBOC (6,1,4/33) & & \\ \multirow{2}{*}{2} & \multirow{2}{*}{L1C} & BOC (1,1) & & \\  & & TMBOC (6,1,4/33) & & \\ \multirow{2}{*}{2} & \multirow{2}{*}{L1C} & TMBOC (6,1,4/33) & & \\  & & BOC (1,1) & & \\ \multirow{2}{*}{2} & \multirow{2}{*}{L1C} & TMBOC (6,1,4/33) & & \\  & & BOC (1,1) & & \\ \multirow{2}{*}{2} & \multirow{2}{*}{L1C} & TMBOC (6,1,4/33) & & \\  & & BOC (1,1) & & \\ \hline \end{tabular}
\end{table}
Table 1: Design scheme of L1C, B1C.  
where \(C\) is the speed of light, \(B_{L}\) is the noise bandwidth of the code tracking loop, \(B_{f}\) is the filter front-end bandwidth, \(T_{p}\) is the coherent integration time, \(G_{L}(f)\) is the power spectrum density of the local reference signal, \(G_{L,R}(f)\) is the cross power spectral density of the local signal and the received signal, and \(d\) is the correlator interval, \(T_{c}\) is the code period, and C/N0 is the signal carrier-to-noise ratio. Figure 2 shows the simulated signal noncoherent code-tracking error, where \(B_{L}\) is 5 Hz, \(B_{f}\) is 40 MHz, \(T_{p}\) is 0.01 s.  
The tracking accuracy of the QMBOC and TMBOC signal codes as the pilot components overlaps in different C/N0 and correlator intervals. Their code-tracking error is lower than that of the BOC (1,1) signal as a pilot component.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 5 Conclusions  
This paper studies the application of deep learning models in high-precision indoor positioning, and proposes a deep learning model, DECC, for high-precision indoor positioning. Specifically, we design two different convolution modules to extract information from raw CSI features and encoded CSI features, respectively. In addition, in the middle part of the model, we use a self-attention-based dual encoder to explore the channel differences and correlations between different antennas. Experiments on real localization data demonstrate the high accuracy and efficiency of DECC localization in different scenarios.  
We provide a feasible scheme to apply our proposed DECC to the real scenario. First, pre-train our DECC on the dataset to obtain the offline model which stores the updated parameters and converts the offline model file format for the positioning system to the calling format. Secondly, run a system-level simulation of the 5G NR air interface which conforms to the 3 GPP standard to estimate the CSI matrices for the target to be estimated. Thirdly, put the CSI matrices into the pre-train model or the positioning system with the pre-train model to obtain the estimated position. We used a laptop computer to evaluate feasibility from a practical point of view. Specifically, we input 2320 obtained CSI matrices with every 16 into the pre-train model to simulate high-concurrence and high-request scenarios. The results showed that even with the lower performance mobile device with the RTX 3060 GPU and i7-11800H CPU, the total process only took 4.37 s--less than 0.0019 s were taken to locate a unit. It is also worth noting that 2320 obtained CSI matrices came from a room of  
Figure 13: Comparisons of DECC with various classic DL models, in terms of (**a**) time overheads and (**b**) distance errors.  
less than 700 square meters in size. In the real world, such intensive requests are unlikely to occur. So, our feasibility evaluation satisfied two extreme cases: (1) unusually high concurrency requests; (2) lack of high-performance equipment.  
Data curation, X.M.; funding acquisition, W.L.; investigation, Z.Z. and X.W.; methodology, X.M.; project administration, W.L.; software, X.M.; supervision, W.L. and S.Z.; validation, Z.Z. and X.W.; writing--original draft preparation, X.M.; writing--review and editing, W.L. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities, 3072022 TS0605, 3072022 YY0601, 3072022 CF0601, and the State Administration of Foreign Expert Affairs, G2021180021L.  
Not applicable.  
The authors declare no conflict of interest.
# A Novel Robust Control System Design and Its Application to Servo Motor Drive
## 5 Application to the Servo Motor Drive System  
Consider the servo motor positioning system expressed as:  
\[\begin{bmatrix}\dot{\theta}_{m}\\ \dot{\omega}_{m}\end{bmatrix} =\begin{bmatrix}0&1\\ 0&-B_{m}/J_{m}\end{bmatrix}\begin{bmatrix}\theta_{m}\\ \omega_{m}\end{bmatrix}+\begin{bmatrix}0\\ K_{T}/J_{m}\end{bmatrix}u-\begin{bmatrix}0\\ T_{L}/J_{m}\end{bmatrix} \tag{32}\] \[=\mathbf{A}\mathbf{x}+\mathbf{b}u+\mathbf{w}\]  
\[y=\theta_{m}=\begin{bmatrix}1&0\end{bmatrix}\begin{bmatrix}\theta_{m}\\ \omega_{m}\end{bmatrix}=\mathbf{c}\mathbf{x} \tag{33}\]  
where \(J_{m}\) is the motor rotor inertia, \(B_{m}\) is the motor viscous, \(K_{T}\) is the torque constant, \(T_{L}\) is the external load, and \(\theta_{m}\) and \(\omega_{m}\) are the motor angular and motor velocity, respectively. The reference input, \(r=\theta_{md}\), is assumed, and the reference is set as  
\[\mathbf{x}_{ref}=\begin{bmatrix}r\\ 0\end{bmatrix} \tag{34}\]to define the new state, \(\mathbf{z}\), as:  
\[\mathbf{z}=\begin{bmatrix}z_{1}\\ z_{2}\\ z_{3}\end{bmatrix}=\begin{bmatrix}x_{I}\\ r-\theta_{m}\\ -\omega_{m}\end{bmatrix} \tag{35}\]  
where \(z_{1}\) is from the integrator defined to estimate the total perturbations whenever a system is in a steady state.  
\[\dot{z}_{1} =r-\theta_{m}=z_{2} \tag{36}\] \[\dot{z}_{2} =\dot{r}-\dot{\theta}_{m}=-\omega_{m}=z_{3}\] \[\dot{z}_{3} =-\dot{\omega}_{m}=\frac{B_{m}}{J_{m}}\omega_{m}+\frac{T_{L}}{J_{ m}}-\frac{K_{T}}{J_{m}}u=-\frac{B_{m}}{J_{m}}z_{3}+\frac{T_{L}}{J_{m}}-\frac{K_{T}}{J_ {m}}u\]  
Define the linear control law as \(u=-\mathbf{k}_{1}^{\mathsf{T}}\mathbf{z}=-\begin{bmatrix}k_{1}&k_{2}&k_{3} \end{bmatrix}\mathbf{z}\) and one has:  
\[\dot{\mathbf{z}} =\mathbf{A}_{1}z+\mathbf{b}_{1}u_{1}+\mathbf{w}_{1} \tag{37}\] \[=\begin{bmatrix}0&1&0\\ 0&0&1\\ 0&0&-B_{m}/J_{m}\end{bmatrix}\begin{bmatrix}z_{1}\\ z_{2}\\ z_{3}\end{bmatrix}+\begin{bmatrix}0\\ 0\\ -K_{T}/J_{m}\end{bmatrix}[-k_{1}-k_{2}-k_{3}]\begin{bmatrix}z_{1}\\ z_{2}\\ z_{3}\end{bmatrix}+\begin{bmatrix}0\\ 0\\ T_{L}/J_{m}\end{bmatrix}\] \[=\begin{bmatrix}0&1&0\\ 0&0&1\\ k_{1}K_{T}/J_{m}&k_{2}K_{T}/J_{m}&-B_{m}/J_{m}+k_{3}K_{T}/J_{m}\end{bmatrix} \mathbf{z}+\begin{bmatrix}0\\ 0\\ T_{L}/J_{m}\end{bmatrix}\mathbf{=A_{cl}z+\begin{bmatrix}0\\ 0\\ T_{L}/J_{m}\end{bmatrix}\]  
The feedback control \(\mathbf{k}_{1}\) can be used to determine the desired system poles under the integral state feedback control.  
Thus, the switching control part can be designed by assigning the sliding surface as  
\[\sigma(z_{1},z_{2},z_{3},t)=\mathbf{b}_{1}^{-1}(\mathbf{z}-\mathbf{z}_{0})- \mathbf{b}_{1}^{-1}\mathbf{A_{c}}\mathbf{\int z}dt \tag{38}\]  
and incorporated with the control law (31). The vector \(\mathbf{g}\) in (22) is replaced by \(\mathbf{b}_{1}^{-1}\) since the control structure is defined as \(\mathbf{g}^{\mathsf{T}}\mathbf{b}_{1}=1\).
## 2 Materials and Methods
### Raman Spectroscopy Characterization of the Natural Plasmonic Nanostructures  
To determine the effect of SERS on the natural structures, we metallized their surfaces. An apparatus for deposition by sputtering was used to deposit a thin layer of gold (Au) onto the structures. The opal, 10 \(\times\) 14.5 mm in size, a sample of a few centimetres cut out from the feather eyespot and diatoms were processed for 30 s, using a current of 50 mA at a voltage of 230 Volts (50 Hz) and a pressure of 100 mbar, to deposit around 10 nm of gold on the samples' surfaces. An Au thickness of 10 nm was chosen to avoid excessive filling of the nanohole structures (this is especially visible in Figure 1C,D). Obviously, the Au thickness was kept constant for all samples to allow a comparison between them. An inVia confocal Raman microscope was used for the optical characterization of the natural plasmonic nanostructures. We set used a wavelength of 633 nm for the laser, a 50\(\times\) objective and an integration time of 10 s, while the power was set to 2.5 mW for each sample. Spectra were recorded by raster scanning the samples' surfaces, collecting at least 100 spectra, so that for each sample, the point-to-point variation could be estimated. The SERS capabilities of the natural structures were probed using Rhodamine-6G, a fluorescent organic molecule purchased from Sigma Aldrich. A drop of 200 \(\upmu\)L of Rhodamine-6G at a concentration of 10\({}^{-5}\) M was deposited on the opal and feather samples. Meanwhile, 5 mg of diatoms were dissolved in 100 \(\upmu\)L of Rhodamine-6G at a concentration of 10\({}^{-5}\) M and placed on a calcium fluoride slide. Each sample was analysed by recording the Raman signal after the samples had been completely dried in air. Because the opal showed the highest amplification effect (as will be seen in the next sections), 200 \(\upmu\)L of Rhodamine-6G at concentrations of 10\({}^{-7}\) M and 10\({}^{-12}\) M were deposited on these samples and measurements were recorded. To demonstrate the amplification effect of the mentioned natural structures on Raman spectroscopy, we compared the response of the signal coming from Rhodamine-6G deposited on a flat silicon substrate, covered with gold.  
The SERS spectra shown in this work represent the average spectrum of ten spectra recorded at different positions on the samples. Before the comparison between the SERS spectra, a background subtraction was carried out using the spectra recorded on the same samples without R6G (under the same experimental conditions).
## 1 Introduction
These parameter settings must be adjusted in accordance with the material properties. The WAAM of copper-based alloys presents various challenges, such as porosity, delamination, oxidation, humping, etc. Copper (Cu) has medium mechanical strength, exceptional ductility, outstanding electrical and thermal conductivity, and robust corrosion resistance, rendering it an indispensable material for numerous industrial applications [6; 10]. Pro  
Figure 1: Schematic representation of ALC variation in CMT-based WAAM. its alloy with WAM is challenging due to its high thermal conductivity, reflectivity, and oxide formation on the surface [7]. High thermal conductivity leads to faster heat dissipation during the deposition, causing undesirable residual stress and eventually distorting or delaminating the deposited layer [7]. The formation and accumulation of oxides on pure copper during the WAM process leads to reduced wettability and diminished flowability of the molten metal [7]. The reflectivity of copper material is another challenge, and it requires high heat input, causing spatter, porosity, and microcracks [7; 16]. Porosity is a common issue in WAM for some materials such as aluminum and copper. Specifically, achieving adequate penetration with the substrate is another challenge for copper and its alloys due to its high thermal conductivity [8]. [PERSON] et al. [6] investigated the mechanical strength of copper and discovered that WAM produced superior results compared to other AM techniques. Furthermore, the thermal conductivity of copper processed with CMT-based WAM was comparable to that obtained through the TIG-based WAM process, with enhanced grain structure observed [6; 17]. In another study by [PERSON] et al. [7], copper deposition using WAM resulted in hardness comparable to other studies and improved tensile strength. There is limited research on Cu1897 deposition using WAM, but porosities, penetration depth, and its area have not been explored, highlighting a significant research gap. The current research study addresses these issues by varying the PC and ALC in the CMT+P mode.
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 2 Background on Multi-Site VNF Communications
### Description of the Reference Scenario  
We begin our discussion on models to support multi-site communications among VNFs using the reference scenario schematized in Figure 1. In this scenario, two network services (\(\text{NS}_{1}\) and \(\text{NS}_{2}\)) are to be deployed over a set of separated NFV infrastructures, maintained at sites \(A\), \(B\) and \(C\). The top part of the picture reflects the allocation of VNFs to each site and the VNF forwarding graph for each network service, i.e., the specification of the network connectivity among VNFs to build a network service. VNFs of a specific network service can be assigned to different sites as instructed by the tenant of the NFV ecosystem requesting their deployment, or as a consequence of service-level agreements established by NFV infrastructure providers. In this example, sites \(A,B\), and \(C\) must deploy the mechanisms needed to support the point-to-point link between \(\text{VNF}_{\text{1b}}\) and \(\text{VNF}_{\text{1c}}\), as well as the multi-point link that interconnects \(\text{VNF}_{\text{2b}}\), \(\text{VNF}_{\text{2c}}\), and \(\text{VNF}_{\text{2d}}\).  
The reference scenario assumes that each site maintains an NFVI under the control of a VIM. The NFV infrastructures of sites \(A\) and \(C\) belong to a single orchestration domain, where an NFVO coordinates the lifecycle management of the VNFs deployed at both sites, including the allocation of NFVI computing, network, and storage resources to them through the VIMs. In our reference scenario, we assume that the NFVI of site \(B\) belongs to a separate orchestration domain. Hence, the deployment of VNFs at this site is handled by another MANO platform. For simplicity, our discussion does not cover inter-domain orchestration aspects. Although specific control mechanisms are needed to support the deployment of a multi-site network service across distinct orchestration domains, this section focuses on identifying the advantages and the limitations of the different approaches to support data-plane communications among remote VNFs.
# The Effect of Adsorbed Volatile Organic Compounds on an Ultrathin Water Film Measurement
## 3 Results and Discussion
### Proper Cleaning Procedure Prior to the Dropwise Condensation Experiment
The decrease of contact angle after surface cleaning could have been an indication of removing some adsorbed VOCs from the surface. The variations in the contact angles were more significant (\(\sim\) 44\({}^{\circ}\)) in the plasma-cleaned samples. To understand the mechanism of increased hydrophobicity in the plasma-cleaned samples, we analyzed the variation in surface chemistry using XPS. A broad-range energy scan of the cleaned samples showed the presence of gold (Au 4f), carbon (C 1s), and oxygen (O 1s) on the samples' surface, see Figure 3a. High-resolution XPS spectra of C 1s and O 1s indicated that the atomic parentage of C 1s and O 1s increased significantly on the plasma-cleaned surfaces during the one-month exposure time, see Figure 3b. For sample 2, the ratio of C/Au, which is the indicator of adsorbed VOCs, increased by 240% during the exposure time of one month. The result showed the amount of non-polar C-C (or C-H), which is responsible for hydrophobicity, was the dominant part (76%) of the adsorbed hydrocarbons. The high-resolution XPS spectra of sample 4 (seen in Figure 3b) revealed that the adsorbed VOCs on the surface did not contain a polar C=O bond that causes hydrophilicity on the surface. This could explain the larger contact angle (76\({}^{\circ}\)) of sample 4 cleaned with SCP, as compared with the eventual contact angle (70\({}^{\circ}\) after an 1100 h exposure time) of sample 2. Based on the surface characterizations of samples, we used SCP as the cleaning procedure for the gold samples before the condensation experiment. SCP produced negligible changes to the chemistry of the surface, as compared to the plasma cleaning method. Therefore, the effect of concurrent adsorption of VOCs during the condensation experiment, which can take up to several hours after the cleaning process, was expected to be less than that of the samples treated with the plasma cleaning method.