esa-sceva/satcom-chunk-collection huggingface dataset:
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 3 Location and Velocity Prediction  
### Framework of the Proposed Algorithm  
In this section, we will introduce the Kalman-filter-assisted prediction of nodes' information for the FANET. To start with, the procedure flowchart of our proposed algorithm is presented in Figure 2 and we will elaborate each step in detail in the following text. Our proposed algorithm mainly contains the following three steps, which are  
1. Initialize the simulation parameter settings at time \(t_{0}\), including the total node number, nodes' locations and velocities. Determine the number of cluster \(K(t_{0})\) by using the Silhouette coefficient and group all nodes into clusters. Find the CH for each cluster based on the \(k\)-means++ algorithm.
2. Predict the node location \(\hat{\mathbf{p}}_{n}(t_{i})\) and velocity \(\hat{\mathbf{v}}_{n}(t_{i})\) based on the Kalman filter. Cluster all nodes based on the predicted information via \(k\)-means or \(k\)-means++ algorithm. Update simulation time \(t_{i}\gets t_{i-1}+\Delta t\).
3. Repeat step 2 until the preset simulation time \(t_{end}\) is reached or other convergence conditions are satisfied.  
\begin{table}
\begin{tabular}{c c c} \hline
**Citation** & **Networking Method** & **Clustering Metric** \\ \hline
[9] & border patrol clustering algorithm & relative speed and separation distance \\ \hline
[11] & \(k\)-means & mobility and relative positions \\ \hline
[12] & \(k\)-means & bandwidth balance \\ \hline
[13] & weighted clustering algorithm & node mobility, connectivity degree, total distance to neighbors, and consumed battery power \\ \hline
[14] & group mobility based clustering algorithm & residual energy, relative velocities difference, and connections with neighbor nodes \\ \hline
[15] & hybrid gray wolf optimization method & intra-cluster distance, residual energy, and geographic location \\ \hline
[16] & bio-inspired localization and clustering schemes & cluster building time, number of clusters, cluster lifetime, and energy consumption \\ \hline \end{tabular}
\end{table}
Table 1: Comparison among different references.
## 2 Design of POSS-Containing MQL Oil  
### Poss  
The polyhedral oligomeric silsesquioxane (POSS) molecules are well-defined organosilicon compounds consisting of rigid silica cores (Si-O-Si) surrounded by functional organic groups, as shown in Figure 1a, and have the structure of a newly added organic substituent (R) on the outside and silicon (Si) and oxygen (O) on the inside. Due to their finely tunable physical and chemical properties, POSSs meet the requirements of both science and industry, the most recognizable and best known of this being cubic T8 with either one or eight functional group derivatives.  
The average diameter of a POSS molecule is approximately 0.5 nm, indicating a dense structure, which results in high chemical and thermal stabilities. This compound can be introduced into various materials, depending on the type of the organic substituent (R) outside the main chain. In addition, it may be used as a side chain of organic polymers or blended as an additive, to develop organic and inorganic hybrid materials with different properties. This means that the POSS is highly applicable when used as a nanocomposite rather than when used alone [17; 18; 19; 20]. Further, POSS, an organic and inorganic hybrid nanostructure, can be easily introduced to a functional group through a chemical reaction, allowing their molecular weight to be controlled [21; 22].  
Conventional MQL-specific oils, as shown in Figure 1b, are made of various vegetable ester compounds such as glycerol, methyl-triol, and tetraol with three or four OH functional groups. However, octa-POSS-OH compounds, as shown in Figure 1c, have as many as eight functional groups compared with these vegetable ester compounds, meaning that one octa-POSS-OH is capable of producing eight ester compounds and having excellent lubricant properties, thus being an effective candidate for efficient cutting oil. This study, therefore, aimed to develop an eco-friendly POSS-containing MQL oil by designing three kinds of POSS ester compounds with the structural frame of octa-POSS-OH and adding them to conventional MQL-specific oils.  
Figure 1: Structures of (**a**) POSS, (**b**) vegetable ester compound, and (**c**) octa-POSS-OH.
# A Hybrid STL-Deep Learning Framework for Behavioral-Based Intrusion Detection in IoT Environments
## 2 Related Work
### Hybrid Intrusion Detection Systems  
To overcome the limitations of individual methods, hybrid IDS frameworks have gained traction. These systems combine different techniques, aiming for synergistic benefits [36]. Examples include combining signature and anomaly detection modules [12], integrating different ML/DL classifiers (ensemble methods), or using feature engineering/selection techniques alongside classifiers [13; 16]. [PERSON] et al. [12] used a combination involving CNN-LSTM and GANs for DoS detection. While hybridization often improves overall performance, the specific combination strategy is crucial, and integrating statistical time series decomposition with DL represents a distinct hybrid approach explored in this work.  
As highlighted in a comprehensive survey on hybrid intrusion detection systems [37], recent advancements have focused on combining multiple learning techniques to enhance detection accuracy and system robustness. Several recent works have proposed innovative hybrid and ensemble-based models to address critical issues such as class imbalance, false positive reduction, and adaptability to dynamic network environments. For example, [PERSON] et al. introduced a self-healing hybrid IDS that leverages ensemble machine learning to enhance resilience and fault tolerance [38], while [PERSON] and [PERSON] proposed a hybrid model that integrates parametric and non-parametric classifiers for improved detection performance [39]. These studies reflect the growing trend toward intelligent, adaptive IDS frameworks that are capable of maintaining high performance in complex and evolving threat landscapes.
## 3 Theory and Calculation
### Proposed Solution by CAE ANN
#### 3.3.2. Latin Hypercube Sampling Method  
To create a distribution of parameter values from a multidimensional distribution, the LHS method was utilized, among other statistical techniques. In 1979, [PERSON] and co-workers [32] published a description of the LHS method, which was then expanded upon by [PERSON] and co-workers [33], where a square grid with sample placements is referred to as a Latin square in the context of statistical sampling [34]. The extension of this idea to an indefinite number of dimensions is known as a Latin hypercube, where each sample is the sole one in each axis-aligned hyperplane containing it.  
The range of each variable is split into M equally likely intervals when sampling a function with \((D+E)\) variables (see Equations (2)-(4)). LHS requirements are then met by placing \(M\) sample points; take note that this mandates the number of divisions, \(M\), to be the same for each variable. It is also important to highlight that this sampling strategy avoids the need for additional samples for additional dimensions (parameters)--this independence is one of the strategy's primary benefits. Another benefit is that random samples can be collected one at a time while keeping track of the previous samples. The LHS method requires first choosing the number of sample points to utilize and then keeping track of which row and column each sample point was picked from. Furthermore, LHS makes sure that the collection of random numbers accurately reflects the range of possible values.  
In the present study, normal probability density distributions were assumed for all parameters. The number of parameters (\(D+E\), see Equations (2)-(3)) was equal to 23 and M was equal to 100 due to the low computational costs. The number of samples in the extended database amounts to 2600 (=26 \(\times\) 100); 100 samples were generated around each original sample from the primary database. In short, the 100 simulations represent 100 distinct cases that are comparable to the original one, but with the variability that takes into consideration alternative solutions to the problem.  
It should be noted that a more rigorous scientific approach to the determination of epistemic and aleatory uncertainties, as well as the inclusion of more exact probability density functions for parameters from the database, was not taken into account. Namely, the main idea of the presented paper was to develop a general framework for application where improved results can be expected by extending the database with new designs. Over time, more realistic estimations of both types of uncertainties will also be obtained. Nevertheless, the application of the proposed CAE ANN model in the results section shows the usefulness and robustness of the proposed framework.
## 3 Experimental Results
### Optical Linewidth (Phase Noise)  
Phase noise of OFC sources is becoming a very critical parameter for higher-order QAM modulation formats [38]. Phase noise normally is presented by optical mode linewidth. Figure 4a shows the measured phase noise for the selection of individual  
Figure 3: (**a**) RIN spectra including all the modes (red) and three filtered individual channels at 1546.04 nm (brown), 1550.58 nm (blue), and 1554.46 nm (green) for the laser measured at an injection current of 400 mA and 18 \({}^{\circ}\)C in the frequent range from 10 MHz to 20 GHz; (**b**) calculated average RIN (blue) over the 20 GHz frequency bandwidth from the RIN spectra and the difference (yellow) of average RIN from their average value (\(-133.54\) dB/Hz) for 19 filtered individual modes in the wavelength range from 1543 to 1556 nm.  
Figure 2: (**a**) Optical spectrum and (**b**) light-current characteristics of the single-mode fiber coupled laser measured at 400 mA and a temperature of 18 \({}^{\circ}\)C.  
longitudinal lasing modes in the wavelength range from 1543 to 1556 nm. The laser was driven at 400 mA at a temperature of 18 \({}^{\circ}\)C. The phase noise is obtained by analyzing the frequency noise spectra using an optical auto-correlator. A narrowband optical tunable filter (EXFO XTM-50) is used for filtering the individual longitudinal lasing modes. The minimum measured phase noise is 0.19 MHz at a wavelength of 1552.4 nm, and all values of phase noise are below 1.5 MHz. The phase noise as a function of mode number is fitted to a parabolic curve, as shown in Figure 4b. A technique for estimating the RF linewidth (\(\Delta f_{FR1}\)) from the parabolic curve fitting of phase noise (\(\Delta v_{n}\)) vs. mode number (\(\Delta n\)) for a SSP MLL has been recently studied [10]. Using the deduced explicit expression as shown in [10] Equation (7), parabolic fitting results of a minimum linewidth of \(\Delta v_{min}=240\) kHz at the 13 th mode is extracted for the 28.4 GHz laser with QDot gain material. The first harmonic RF linewidth is then extracted from the parabolic fit giving \(\Delta v_{RF1}=0.631\) kHz.
For instance, if the RSS threshold is high, an MN will turn on its interfaces early for network discovery, which makes handoff smooth but increases the power consumption. On the contrary, if the RSS threshold is set to a low value, the MN turns on its interfaces late. Although the MN can consume less power, it may not have sufficient time to perform network discovery and handoff execution, which increases the handoff dropping rate. The motion-aware network discovery method improves the performance of handoff by exploiting location information offered by Global Position System (GPS) [20], Inertial Navigation System (INS) [21] or location systems to predict the position and motion state of an MN. Unfortunately, the GPS system may not work well in an indoor environment and metropolitan area. Although indoor positioning systems [22; 23; 24; 25; 26; 28] can use an RSS map to help positioning an MN and detect the MN's motion, it needs extraneous hardware and has high implementation cost. This work proposes a motion detection scheme called Momentum of Received Signal Strength (MRSS) to detect an MN's motion without the assistance of any positioning systems. With the MRSS method, an MN can turn on its interfaces to discover candidate wireless access networks only when it is leaving the service area of the current associated AP and terminate unnecessary handoffs when it is stationary, leading to significant reduction in power consumption and improvement in handoff success rate. Although MRSS is sensitive to the MN's motion, it is static and fails to quickly detect the changes in the MN's motion. Thus, a novel motion state dependent MRSS scheme called Dynamic MRSS (DMRSS) algorithm is proposed to address this issue. Extensive simulation experiments were conducted to study the performance of our presented algorithms. The simulation results show that MRSS and DMRSS can substantially improve the motion detection delay and be utilized by a handoff algorithm to reduce unnecessary handoff and save battery power. The rest of this paper is organized as follows. Section 2 overviews related network discovery schemes and motion detection technologies. Section 3 details the proposed Momentum of Received Signal Strength (MRSS) based motion detection method and the proposed MRSS-based handoff algorithm. Section 4 analyses the property of MRSS on various design options and evaluates the performance of MRSS-based handoff algorithm. Finally, Section 5 draws conclusions and discuss future works.
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
## 1 Introduction
Although these blended methods can increase the predictive precision to some extent, their excessive complexity brings the heavy computation intensity for practical application. Aiming to overcome the aforementioned problems, an SVM is introduced to achieve precise SOH estimation in this paper [24; 25; 26]. Different from other data-driven methods, the SVM method is more suitable for nonlinear problems with small samples, and can effectively prevent against local minimization. Moreover, it is not sensitive to dimension of the data and variability. In [24], a SVM classification and regression model is proposed for different status of batteries, and is declared to achieve desirable results. However, the discharging process is infeasible to extract input vectors for on-board SOH estimation due to stochastic characteristic of the discharge current. In [25], combined with the rain flow theory, the SVM algorithm is applied to predict the SOH under the complex operating conditions. However, some feature vectors are difficult to measure and calculate in practical application. Therefore, it is imperative to select suitable feature vectors to achieve real-time estimation. In addition, SVM, as a data-driven method, needs large quantity of data to build and train the model, which brings pressure of storage amount for the BMS. From this point, the SVM based SOH estimation algorithm should be carefully designed considering fewer data while achieving high accuracy. This is the main research focus of this paper. In this paper, the extraction of characteristics variables for the battery SOH estimation is introduced in detail, where partial charging voltage curve is analyzed and discussed for different aging status [27]. Then, the SVM model with the radial basis function (RBF) as the kernel function is established for the SOH estimation based on the partial charging voltage data. Experiment validation proves the feasibility of the proposed algorithm. The remainder of this paper is organized as follows. In Section 2, the battery charging process is detailed and analyzed, based on which the related characteristic variables are extracted in depth for the following study. Section 3 introduces the SVM algorithm comprehensively as well as its application for the SOH estimation. Based on the proposed method, a series of results regarding SOH estimation are shown and analyzed in Section 4, and key conclusions are summarized in Section 5.
## Appendix A CG-RBC Membrane Model
Appendix B Prediction of Discocyte, Echinocyte I, Echinocyte II, Echinocyte III and Sphero-Echinocyte RBC Morphologies  
The CG-RBC membrane model-predicted discocyte, echinocyte I, echinocyte II, echinocyte III and sphero-echinocyte morphologies were achieved according to the method presented in [PERSON] et al. (2016), and the key model parameters are summarised in \[E_{\textit{Stretching}}=\sum_{j\;\epsilon\;\text{ln}\_{N_{S}}}\left[\frac{k_ {B}\;T\;l_{\textit{max}}}{4\;p}\;\frac{3x_{j}^{3}-2x_{j}^{3}}{1-x_{j}}+\frac{ k_{p}}{(m-1)\;l_{j}^{m-1}}\right], \tag{2}\]4 The analogous equilibrium discocyte, echinocyte I, echinocyte II, echinocyte III and sphero-echinocyte shapes are presented in \[E_{\textit{Stretching}}=\sum_{j\;\epsilon\;\text{ln}\_{N_{S}}}\left[\frac{k_ {B}\;T\;l_{\textit{max}}}{4\;p}\;\frac{3x_{j}^{3}-2x_{j}^{3}}{1-x_{j}}+\frac{ k_{p}}{(m-1)\;l_{j}^{m-1}}\right], \tag{2}\]5 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Value** & **Source** \\ \hline \(A_{0}\) & Reference RBC membrane surface area & 140.0 μm2 & [62; 76; 93] \\ \hline \(C_{0,Dissocyte}\) & Reference total-membrane-curvature for discocyte & \(1.85\times 10^{-3}\ A_{0}\) m\({}^{2}\) & [76] \\ \hline \(C_{0,Echinocyte I}\) & Reference total-membrane-curvature for echinocyte I & \(2.00\times 10^{-3}\ A_{0}\) m\({}^{2}\) & [76] \\ \hline \(C_{0,Echinocyte II}\) & Reference total-membrane-curvature for echinocyte II & \(5.50\times 10^{-3}\ A_{0}\) m\({}^{2}\) & [76] \\ \hline \(C_{0,Echinocyte III}\) & Reference total-membrane-curvature for echinocyte III & \(6.00\times 10^{-3}\ A_{0}\) m\({}^{2}\) & [76] \\ \hline \(C_{0,Sphero-Echinocyte}\) & Reference total-membrane-curvature for sphero-echinocyte & \(9.00\times 10^{-3}\ A_{0}\) m\({}^{2}\) & [76] \\ \hline \(D_{0}\) & Monolayer thickness of the lipid-bilayer & 2.0 nm & [75; 76] \\ \hline \(N_{S}\) & Number of links in CG-RBC membrane model & 7680 & [76] \\ \hline \(N_{V}\) & Number of vertices in CG-RBC membrane model & 2562 & [76] \\ \hline \(N_{t}\) & Number of triangles in CG-RBC membrane model & 5,120 & [76] \\ \hline \(V_{0}\) & Reference RBC volume & 93.48 μm3 & [62; 76; 93] \\ \hline \(k_{A}\) & Constraint coefficient for total membrane surface area & \(1.0\times 10^{-3}\) Nm\({}^{-1}\) & [76] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The key parameters values used to model the discocyte, echinocyte I, echinocyte II, echinocyte III and sphero-echinocyte RBC morphologies.
# Modified Periosteal Inhibition (MPI) Technique for Extraction Sockets: _A Case Series Report_
## 3 Discussion
[35] described the use of the cortical membrane in combination with a porcine-derived xenograft (OsteoBiol GenOs\({}^{\circledR}\), Tecnos\({}^{\circledR}\), Giaveno, Italy) for ARP. After 6 months of healing, the authors observed a reduction of the ridge width from 9.8 \(\pm\) 1.2 mm to 8.0 \(\pm\) 1.1 mm in test sites (treated with the ARP technique). On the contrary, the control group (spontaneous healing) showed a horizontal reduction of the alveolar ridge from 9.9 \(\pm\) 1.0 mm to 6.2 \(\pm\) 1.3 mm. In both cases, the reductions were statistically significant (\(p<0.05\)), although it was greater on the control side. The technique described by theauthors differed from MPI. Indeed, [PERSON] et al. [35] positioned the membrane so as to have it cover the socket, while in MPI the membrane was placed between the buccal periosteum and the outer surface of the alveolar ridge. Furthermore, unlike [PERSON] et al. [35], in MPI no bone substitutes were inserted in the sockets, leaving the blood clot to form the matrix for new bone formation. In the MPI surgical protocol, the extraction sockets were filled with resorbable collagen sponges. It could be assumed that the material could affect the healing process and, consequentially, the results. Collagen sponges act as a provisional extracellular matrix that promotes the early stages of socket healing [36]. However, they have not been effective in preventing alveolar ridge shrinkage after dental extraction [37]. Recently, [PERSON] et al. [38] compared the dimensional changes of alveolar sockets filled with collagen sponges and porcine cortico-cancellous bone (MP3, OsteoBiot(r), Tecnoss, Coazze, Italy) in the molar and premolar areas. After 3 months, the width of the alveolar ridge decreased by 2.9 (0.9) mm and 3.9 (1.4) mm, respectively. The difference was statistically significant between the two groups (\(p<0.0001\)). These data confirmed that the collagen sponges were not effective for ARP, showing similar results to spontaneous healing [39]. Most importantly, the use of a porcine xenograft alone within the socket did not prevent the horizontal contraction of the alveolar ridge during the healing period. The results of this study were in line with those reported by [PERSON] et al. [39]. The major limitation of this study is the absence of a control group. However, analyzing the recent data in the literature [2], it is possible to observe in the molar area an average horizontal resorption of the crest equals 3.61 mm (95% CI: 3.24-3.98) after dental extraction. These data are essentially in line with the systematic review of [PERSON] et al. [4], according to which the horizontal resorption of the ridge was 3.79 \(\pm\) 0.23 mm after 6 months of healing. Considering the data of the present study, MPI has shown promising results. Further clinical trials will be needed in the future to compare the effectiveness of MPI with both spontaneous socket healing and other ARP procedures.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 3 MIMO Detection: A System Model
### DL-based MIMO Detection  
In DL-based MIMO detection, the MIMO detector calculates the estimated transmitted signal \(\hat{\mathbf{s}}\) by feeding the network with the information of the received signal \(\mathbf{r}\) and channel \(\mathbf{G}\). Now, the estimated transmitted signal \(\hat{\mathbf{s}}\) is measured by the nonlinear mapping function \(m(\mathbf{G},\mathbf{r};\theta)\) through the following equation:  
\[\hat{\mathbf{s}}=m(\mathbf{G},\mathbf{r};\theta), \tag{18}\]  
where \(m(\cdot)\) denotes the inference rule that is used to detect the hidden symbols based on \(\mathbf{r}\) and \(\mathbf{G}\), and \(\theta\) includes all parameters related to the DNN. The DNN aims to determine the ideal value of \(\theta\) that reduces the associated expenses by developing a cost objective function that quantifies the difference between the actual and estimated symbols. The goal is to reduce the mean squared error between \(\hat{\mathbf{s}}\) and \(\mathbf{s}\) as follows:  
\[\hat{\theta}=\arg\min_{\theta}f(\hat{\mathbf{s}},S), \tag{19}\]  
where \(f(\cdot)\) represents the cost function [9] and \(S\) is the transmitted symbol set. Here we discuss our previously proposed work [3] used as our benchmark models.