esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimal Calibration of the TLIMD
### \(H_{2}\) Norm Optimization
\begin{array}{l}\psi(\phi)=b_{3}\phi^{6}+b_{2}\phi^{4}+b_{1}\phi^{2}+b_{0}\\ \Gamma(\phi)=a_{4}(i\phi)^{4}+a_{3}(i\phi)^{3}+a_{2}(i\phi)^{2}+a_{1}(i\phi)+a_{0 }\\ \\ \mathbf{G}=\left[\begin{array}{cccc}b_{3}&b_{2}&b_{1}&b_{0}\\ -a_{4}&a_{2}&-a_{0}&0\\ 0&-a_{3}&a_{1}&0\\ 0&a_{4}&-a_{2}&a_{0}\end{array}\right],\ \mathbf{F}=\left[\begin{array}{cccc}a_{3}&-a_{ 1}&0&0\\ -a_{4}&a_{2}&-a_{0}&0\\ 0&-a_{3}&a_{1}&0\\ 0&a_{4}&-a_{2}&a_{0}\end{array}\right]\\ b_{0}=\overline{\beta}^{4}(\mu_{2}-1)^{2},\ b_{1}=2\overline{\beta}^{2}(\mu_{ 2}-1)\left(\mu_{1}-2\overline{\zeta}_{2}^{~{}2}+2\mu_{2}\overline{\zeta}_{2}^ {~{}2}\right),\ b_{2}=\mu_{1}^{~{}2},\ b_{3}=0\\ a_{0}=-\overline{\beta}^{2},\ a_{1}=-2\overline{\beta}\zeta_{2},\ a_{2}=- \mu_{1}-\overline{\beta}^{2}-\overline{\beta}^{2}\mu_{1},\ a_{3}=-2\overline{ \beta}\zeta_{2}(\mu_{1}+1),\ a_{4}=-\mu_{1}\end{array}  \tag{26}\]  
Then, the optimal frequency ratio \(\overline{\beta}\) and the damping ratio \(\overline{\zeta}_{2}\) can be determined by the following equations:  
\[\frac{\partial I_{H}}{\partial\overline{\beta}}=0,\ \frac{\partial I_{H}}{ \partial\overline{\zeta}_{2}}=0 \tag{27}\]  
with the solutions given by  
\[ \begin{array}{l}\overline{\beta}_{opt}=\frac{1}{\mu\mu_{1}+1}\sqrt{\frac{\mu _{1}(\mu_{1}\mu_{2}\mu^{2}+\mu_{1}\mu+2)}{2(1-\mu\mu_{2})}}\\ \overline{\zeta}_{2,opt}=\frac{\mu_{1}}{2}\sqrt{\frac{\mu(\mu_{1}\mu_{2}\mu^{ 2}+3\mu_{1}\mu+4)}{2(\mu\mu_{1}+1)(\mu_{1}\mu_{2}\mu^{2}+\mu_{1}\mu+2)}}\end{array}  \tag{28}\]  
Note that the proposed solution (28) for the TLIMD optimal parameters is held as long as the condition \(\alpha_{1}<\frac{1}{\mu}-\frac{1}{2}\mu_{3}(\alpha_{2}-1),\ (\alpha_{1}\leq\alpha_{2})\) is fulfilled [22]. The performance index under the optimal tuning condition can be calculated, given as  
\[I_{H,opt}=\frac{\mu_{1}(1-\mu\mu_{2})\big{(}\mu^{4}\mu_{1}^{2}\mu_{2}^{2}+4\mu ^{3}\mu_{1}^{2}\mu_{2}+3\mu^{2}\mu_{1}^{2}+6\mu^{2}\mu_{1}\mu_{2}+10\mu\mu_{1 }+8\big{)}}{4\sqrt{2}(\mu\mu_{1}+1)^{2}\overline{\beta}_{opt}(\mu_{1}\mu_{2} \mu^{2}+\mu_{1}\mu+2)\overline{\zeta}_{2,opt}} \tag{29}\]  
Equation (18) connections the parameters defined in the approximate TLIMD-SDOF system and the one defined in the primary analogue TLIMD-SDOF system. Thus, the optimal parameters given by (28) can be transferred to  
\[\beta_{opt}=\overline{\beta}_{opt}coe,\ \zeta_{2,opt}=\overline{\zeta}_{2,opt}coe \tag{30}\]  
which provides more intuitive formulae for the TLIMD optimal design. Furthermore, it should be emphasized that the proposed formulae for the TLIMD optimal design (given in (30)) explicitly indicate the contribution from the structural inherent damping \(\zeta_{1}\), since \(coe\) is a function of the structural damping, cf. (16).
## 1 Introduction  
The term heavy metal refers to any metal chemical element that has a relatively high density and is toxic or poisonous in even very low concentrations. Examples of heavy metals or some metalloids include mercury (Hg), cadmium (Cd), arsenic (As), chromium (Cr), thallium (TI), and lead (Pb), among others [1; 2; 3; 4; 5]. Heavy metals are generally found as natural components of the earth's crust, in the form of minerals, salts, or other compounds. Heavy metals are dangerous because they tend to bioaccumulate in different crops. To a small degree, they can be incorporated into living organisms (plants and animals) via food and can do so through water and air as means of translocation and depending on their mobility in such media [4; 6; 7; 8].  
Heavy metals, so-called trace elements, can serve as micronutrients for crops, since they are required in small quantities and are necessary for organisms to complete their life cycle [9; 10; 11; 12; 13; 14; 15; 16; 17]. Therefore, proper establishment of health guidelines is often difficult. The content of main elements and trace elements in plants is governed both by the geochemical characteristics of the soil where it grows, as well as by the ability of plants to accumulate elements selectively.  
It is important to consider that heavy metal pollution in Mexico is a problem that has been increasing mainly due to anthropic activities. Mining is known to be one of the main causes of environmental pollution by heavy metals, mainly due to the inadequate management of its waste called \"mining tailings\", which causes pollution problems in different states [18; 19; 20]. As a result of these increases in metal concentrations in soils due to inappropriate practices, increased bioavailability of metals for multiple crops has been causing damage, phytotoxicity and thereby causing a latent risk to human health. The main polluting metals in Mexico, considering their toxicity and abundance are mercury (Hg), arsenic (As), lead (Pb), nickel (Ni), and copper (Cu) [6; 14; 21; 22; 23].  
In this field, numerous chemical studies have been conducted on plants, dedicated to the determination of compounds, heavy metal content, the mechanisms of their effects, as well as the investigation of external and internal factors that influence the qualitative and quantitative content of trace elements. Most analytical methods used for the determination of metals in natural samples require the destruction of their matrix [3; 24; 25; 26; 27]. The energy dispersion X-ray fluorescence (EDXRF) method presents a technique that enables the identification of chemical elements present in multiple samples in an effective, simple and non-destructive manner for multi-elemental analysis [14; 28; 29; 30; 31].  
In this sense, the objective of this study is to determine the level of environmental risk of heavy metals in the plants _Croton diocius_ and _Phorandendron villosum_--and their aqueous, ethanolic, hydroethanolic, and oily extracts--to determine the feasibility of its intake since they are used in several areas of Mexico to support the treatment of multiple diseases.
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 4 Channel Model
### Pointing Misalignment  
In the context of the G2T communication system, which operates in an environment filled with mechanical vibrations, both from train movements and external disturbances, misalignment and pointing errors may occur. A gaussian beam propagates over distance z from the BS to a circular detection aperture with radius a, and the instantaneous radial displacement is between the beam centroid and the detector center r. Approximating the collected power fraction at the receiver as [36]:  
\[h_{\mathrm{g}}\ \approx\ A_{0}\exp\left(-\frac{2r^{2}}{w_{\mathrm{z_{eq}}}^{2}}\right) \tag{8}\]  
where A\({}_{0}\)= \(\left[\mathrm{erf}(\mathrm{v})\right]^{2}\) is the fraction of the collected power at r = 0, v =\(\sqrt{\pi/2}\frac{\mathrm{a}}{w_{\mathrm{z}}}\) is the aperture radius to beamwidth ratio, and \(w_{\mathrm{z_{eq}}}\) is the equivalent beamwidth \(w_{\mathrm{z_{eq}}}^{2}=w_{\mathrm{z}}^{2}\frac{\sqrt{\mathrm{erf}(\mathrm{v} )}}{2w\ \exp(-\mathrm{v}^{2})}\). The beamwidth w\({}_{\mathrm{z}}\) at distance z is given as:  
\[w_{\mathrm{z}}\ \approx\ w_{\mathrm{o}}\sqrt{1+\left(\frac{\lambda z}{\pi w_{ \mathrm{o}}^{2}}\right)^{2}} \tag{9}\]  
where w\({}_{\mathrm{o}}\) is the beam waist at z = 0.  
A Rayleigh distribution is used to model the radial displacement \(r\) at the receiver [36]:  
\[f_{\mathrm{r}}(\mathrm{r})=\frac{\mathrm{r}}{\sigma_{\mathrm{s}}^{2}}\exp \left(-\frac{\mathrm{r}^{2}}{2\sigma_{\mathrm{s}}^{2}}\right)\text{, }\mathrm{r}\ >\ 0 \tag{10}\]  
where \(\sigma_{\mathrm{s}}^{2}\) is the jitter variance at the receiver.
## 2 Related Work
### Smart Home Devices  
Smart home systems represent the most dominant and exponentially growing segment of IoT (Internet of Things) devices penetrating home environments. Smart TVs, smart household devices, sensors and other devices provide convenience for the user but also introduce security and privacy concerns that are complex to manage [36]. In the United States, 69% of households have at least one smart device at home, and 12% have more than one device, meaning that there will only be a small number of remote workers where the remote (home) location does not include any of these smart devices.  
The available technology for smart home settings and home automation [37] includes a number of solutions for user interfaces (central point to control the system) and options for transmission (wired or wireless). Almost all of them use the same communication interface, with a Wi-Fi router present in most homes, and only a few of them use other options, such as direct access to telecommunication networks.  
Although the smart home is a very different environment, the overall nature of security threats is related to threats in other domains. Confidentiality, authentication and integrity threats are also present in the home environment [38]. Most smart devices are designed to be low cost in order to achieve high market penetration, and, therefore, security or privacy concerns do not necessarily have a high priority in the device manufacturing process. The average home user has a widely varying degree of technical knowledge and also a varying degree of security awareness of potential risks. As such, the installation and configuration of these smart devices are often in the hands of mostly untrained users.  
A number of smart home device manufacturers assume that they can delegate security to the smart home's underlying architecture, such as the Wi-Fi router firewall [39]. This approach can result in exploitation by malicious third parties, which can include eavesdropping, impersonation, software exploitation and other attack vectors. One recommendation of manufacturers is proper and tailored installation and configuration. The other recommendation for the whole lifecycle of devices is regular updating of the device software.  
Voice activation is an additional feature recently added to some smart home devices. The benefit is the convenience of control and comfort of usage the risks are new attack vectors and access to sensitive user data [40]. The mitigation of these newly emerged risks is ongoing, but one minor step is changing the initial voice activation code or phrase immediately after installation.  
We investigate the topics of smart home device security settings, smartphone usage and other details for both student groups in our survey results section (see Section 4.2).
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
###### Abstract  
Combining multiple devices for localization has important applications in the military field. This paper exploits the land-based short-wave platforms and satellites for fusion localization. The ionospheric reflection height error and satellite position errors have a great impact on the short-wave localization and satellite localization accuracy, respectively. In this paper, an iterative constrained weighted least squares (ICWLS) algorithm is proposed for these two kinds of errors. The algorithm converts the nonconvex equation constraints to linear constraints using the results of the previous iteration, thus ensuring convergence to the globally optimal solution. Simulation results show that the localization accuracy of the algorithm can reach the corresponding Constrained Cramer-Rao Lower Bound (CCRLB). Finally, the localization results of the two methods are fused using Kalman filtering. Simulations show that the fused localization accuracy is improved compared to the single-means localization.  
fusion result; short-wave localization; satellite localization; Kalman filtering +
Footnote †: journal: Sensors  
0000-0001-5599-2276-1000][PERSON]  
0000-0001-5599-2276-1000][PERSON]  
0000-0001-5599-2276-1000][PERSON]
## 3 Results
### Molecular Autopsy  
The genetic test, as a fundamental investigation for the post-mortem assessment of HCM, was reported in several studies [26; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. The related data obtained from the articles are summarized in Table 2. Genes codifying sarcomeric protein were the most frequently involved, with a prevalence for the mutation of MYBPC3 (Myosin-binding protein C) [26; 39; 40; 41; 42; 43; 44; 45; 47; 48; 49; 50; 52], in which 12 pathogenic or likely pathogenic variants were reported (c.221 delG; c.3491-2A>T; p.Gln96 Per; p.Phe295 SerfsTer5; p.Gly278 GluftsTer22; c.649A>G; c.150C>A; c.2003G>A; c.1504C>T; c.442G>A; p.Arg726 Cys; c.2441_2443 del). Then, mutations were observed at MYH7 (Myosin heavy chain 7) [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50] with nine variants classified as pathogenic or likely pathogenic (c.1325G>A; R249Q; c.5120T>C; (c.3134G>T; Thr446 Pro; Phe468 Leu; p.Arg652 Lys; c.2105T>A; c.G2348T). Other pathogenic and likely pathogenic variants were found at MYH6 (Myosin heavy chain 6; c4193G>A) [43], ACTN2 (Actinin \(\alpha\)2; c.355G>A) [51], TNNT2 (Troponin T type 2; c.517_519 de) [50], TPM1 (Tropomyosin 1\(\alpha\), c.656A>T) [53], and TTN (Titin; c.48163C>T) [43]. Two studies analyzed the phenotype-genotype correlation. [PERSON] et al. [47] reported HCM-related rare variants in cases with a disarray >5%: the pathogenic/likely pathogenic variants p.Arg470 Gln of MYBPC3 observed in one case and pThr1046 Met of MYBPC3 found in two cases showed, respectively, 8.5%, 12.5%, and 15.2% of disarray, while variants of uncertain significance were reported in five cases. However, the authors concluded that it was impossible to genetically determine the clinical appearance and outcomes of HCM considering the large number of factors (i.e., genetic and environmental) that can affect the morphological, histological, and clinical phenotype. On the other hand, [PERSON] et al. [26] described two predicted protein-truncating variants (PTVs) in MYBPC3 (p.Gly278 GluftsTer22 and p.Phe295 SerfsTer5) associated with high heart-weight-to-BMI ratios and death under age 40, supporting the PTV correlation with a more severe HCM phenotype. Finally, in some cases, variants that were likely pathogenic or pathogenic were found in the absence of morphological cardiac abnormalities indicating HCM [38; 48; 50; 52; 53].
# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 2 Description of the Experiments and Data Acquisition  
The data used in this paper is open-source vibration data published by the NASA prognostics centre for experiments on bearings [17]. This dataset has been utilised by many researchers to validate their proposed techniques. A detailed comparison of the summary of findings published in literature with the proposed method are presented in the Table 1. [PERSON] et al. [18] and [PERSON] et al. [19] have developed a weak signature detection method using wavelet-transform. [PERSON] [20] has taken a different approach to utilise hidden Markov model to filter the damage sensitive coefficients and used dynamic principle component analysis to decompose the signal. These algorithms can detect defects and no prior knowledge to failures are required, [PERSON] et al. [18] have implemented a de-noising process using wavelet transform. They managed to detect the defect pattern on day 27 which is a week earlier than the final run to failure inspection day. The trend forecast developed by [PERSON] et al. [19] can be easily affected by noise and false alarm are quite possible as the empirical method retrieved from the 10 th IMF (Intrinsic Mode Function) from EMD resulting in a coarse result.  
The test setup consists of four Rexnord ZA-2115 double row bearings installed on a shaft as shown in Figure 1. The AC motor coupled to the shaft was set to rotate at a constant speed of 2000 RPM via rub belts. A spring mechanism was used to apply a radial load of 2721.55 kg on the shaft and bearing to accelerate the deterioration process.  
Four pairs of PCB 353B33 accelerometers were installed to capture the \(x\)- and \(y\)-axes vibration response of the four bearings. The sensor displacement is presented in Figure 1. All data set consists of individual files of a one second duration of vibration signal measurements with 20,480 data points sampled at 20 kHz. The measurement interval is set to every ten minutes. The experiments are conducted until the failures occurred after exceeding the designed lifetime of the bearing that is more than 100 million revolutions.
## 3 Methodology
### Prediction Using GP
#### 3.2.3 Mars  
The MARS algorithm is a non-parametric algorithm developed by [81] to estimate the complex nonlinear correlation between model inputs and output. This estimating process is achieved by automatically building a series of linear piecewise regression models through the use of basis functions, to fit the given data pair.  
In the general, the MARS model is of the form precisely considered in Equation (14):  
\[\hat{f}(z)=\beta_{0}+\sum_{k=1}^{N}\beta_{k}\lambda_{k}(z) \tag{14}\]  
where \(\hat{f}(z)\) signifies the estimated output parameter value, \(\beta_{0}\) is constant, \(\lambda_{k}(z)\) is the \(kth\) basis function, \(\beta_{k}\) signifies the \(k\)th basis function's coefficient and \(z\) signifies the input variable. The basis function act as a hinge function to split the data into separate sections, which can be modelled individually. Each basis function can be precisely considered in Equation (15) as:  
\[\lambda_{k}(z)=\prod_{i=1}^{I_{k}}\left[s_{ik}\cdot\left(z_{v(i,k)}-h_{ik} \right)\right]_{+} \tag{15}\]  
where \(I_{k}\) is the quantity of splits that formed \(\lambda_{k}(z)\), \(s_{ik}\) is the selected sign with value \(\pm\), \(v(i,k)\) labels the predictor variable and \(h_{ik}\) is the knot value on the corresponding input variables.  
The MARS algorithm adopts two main steps namely: the forward selection process and the backward deletion process; to develop its model. In the forward selection process, the model is initially constructed with a constant basis function. New pairs of basis functions are thereafter iteratively included in the model to reduce the training residual sum-of-squares error; to improve the model. However, as many basis functions are added in the forward process; the model built becomes overfit and cannot generalize well with unseen data. The backward deletion process is then introduced to remove all redundant basis functions. It employs the generalized cross-validation (GCV) Equation (16) to evaluate the performance of individually created models as it eliminates the unwanted basis functions.  
The individually created model with the least value of GCV is then chosen as the optimal MARS model.  
\[\text{GCV}(Q)=\frac{\frac{1}{H}\sum\limits_{j=1}^{H}\left(y_{j}-\hat{f}_{Q}(z_{j} )\right)^{2}}{\left(1-\frac{C(Q)}{H}\right)^{2}} \tag{16}\]  
where \(y_{j}\) and \(\hat{f}_{Q}(z_{j})\) denotes the actual output and predicted values of the training samples, and \(H\) represents the total number of training samples. As shown in Equation (17), \(C(Q)\) is a penalty for model complexity that is proportional to the model's number of basis functions.  
\[C(Q)=(Q+1)+pQ \tag{17}\]  
where \(p\) is the penalty cost for the optimization of every single basis function which works as a smoothing variable. The details of MARS as well as the selection of the \(p\) are in [76].
## 2 Methodology
### Simulation Setup  
The simulation involved analyzing pointing accuracy, particularly to achieve a pointing accuracy below 100 \(\upmu\)rad. Data exchange between the ground and the ship occurred  
Figure 8: ENU and Pointer reference frames.  
Figure 9: Azimuth and elevation function definition.  
through both a ship-satellite and a satellite-ground connection. During the satellite's orbital period, there are instances when a specific satellite is no longer within the ATP system's visible field, requiring the system to re-engage with another satellite to continue the information exchange operation. The most critical phase is when the ATP system initiates the pointing process, necessitating a rapid satellite targeting phase to reduce information lag. Once the satellite is targeted, the ATP system will be capable of tracking its path across the celestial sphere and facilitating information exchange.  
For the simulation, the ship's position and a portion of the orbital trajectory of the satellite were chosen (Figure 10).  
Since the objective was to evaluate the satellite's pointing phase, the chosen time for the simulation was 10 s, with a simulation time step of 0.001 s.  
The azimuth and the elevation axes in the ATP system are concurrent in a single point and it is enough to consider only the angular displacement, while the translational one is negligible.
## 2 Materials and Methods
### Data Processing  
Analysis of variance (ANOVA) was performed using the IBM SPSS Statistics 25 (SPSS Inc., Chicago, IL, USA) software, principal component analysis (PCA), and PLSR analysis were performed using the Unscrambler X software (CAMO Software AS, Oslo, Norway), and all variables were standardized (1/standard deviation). The comparison and visualization of the MF (%) values of different types of sensory characteristics were performed using the \"vioplot\" function in the \"ggplot2\" version 3.3.5. Data are expressed as mean \(\pm\) standard deviation (SD).
## 6 Results and Discussion  
### S-Parameters  
The simulated and measured results with all four radiating elements of the MIMO antenna are shown in Figure 8. The measurement has been performed by using the Agilent Vector Network Analyzer N5247A under an anechoic chamber measuring 10 ft (length) \(\times\) 12 ft (breadth) \(\times\) 9 ft (height). As seen in Figure 10a, there is a shift in the measured operating frequency compared to the simulated results. The measured result shows that Antenna-1 radiates between 22.8 and 31.5 GHz, Antenna-2 between 23.8 and 31.5 GHz, Antenna-3 between 22.4 and 31.1 GHz, and Antenna-4 between 22.0 and 30.5 GHz. The shift in both the S\({}_{11}\) and the radiating frequency may be attributed to the large size of the SMA connectors compared to the small size of the antenna. Also, due to the minute size of the antenna, any small deviation in the dimensions or the alignment during physical antenna fabrication impacts the output characteristics. But, in spite of the shift in frequency in the measured results, the required frequency band from 24.25 to 29.5 GHz is completely covered.  
Figure 10: _Cont._  
Figure 9: Fabricated prototype of the proposed antenna. (**a**) Feed-plane, (**b**) ground plane.  
elements is partially due to the orthogonal placement of the antenna elements with respect to each other, which not only helps to reduce the overall dimensions but also to achieve low mutual coupling. In addition, as shown in Figure 11, the protruding stubs on the edge of the ground plane also induce a current with a direction opposite to that of the monopole semi-circle, which further reduces the value of mutual coupling [36].  
The simulated and measured gain vs. the frequency plot is shown in Figure 10d.