esa-sceva/satcom-chunk-collection huggingface dataset:
# SMCSPO-Based Robust Control of AUV in Underwater Environments including Disturbances
## 3 Controller and Observer Design
### Stability Analysis  
The stability analysis of the SPO and SMC is presented in this section. As shown in (23), the perturbation is the summation of all the nonlinearities of the vehicle system. Each perturbation term is assumed to be upper-bounded by the known continuous functions of the state as follows [7]:  
\[\mathbf{\Gamma}(\mathbf{\theta},\mathbf{t})\mathbf{=}\mathbf{\Lambda}(\mathbf{\theta})\mathbf{+} \mathbf{\Pi}(\mathbf{t})>|\mathbf{\Psi}(\mathbf{t})|, \tag{39}\]  
where \(\mathbf{\Lambda}>|\Delta\mathbf{A}|\) and \(\mathbf{\Pi}>|\mathbf{D}|\) are the assumed upper bounds of uncertain nonlinear terms of the vehicle system. In the sliding observer, the conditions for the existence of the sliding are  
\[ \begin{array}{ll}\widetilde{\mathbf{X}}_{2}\mathbf{\leq}\mathbf{k_{1}}\mathbf{+} \mathbf{\alpha_{1}}\widetilde{\mathbf{X}}_{1}\mathbf{,}\ \mathbf{(}\mathbf{if}\ \widetilde{\mathbf{X}}_{1}>\mathbf{0}\mathbf{),}\\ \widetilde{\mathbf{X}}_{2}\mathbf{\geq}\mathbf{-}\mathbf{k_{1}}\mathbf{+}\mathbf{\alpha _{1}}\widetilde{\mathbf{X}}_{1}\mathbf{,}\ \mathbf{(}\mathbf{if}\ \widetilde{\mathbf{X}}_{1}<\mathbf{0}\mathbf{).}\end{array}  \tag{40}\]  
When the sliding mode takes place, the resulting error dynamics take the following form:  
\[\ddot{\widetilde{\mathbf{X}}}_{2}\mathbf{=}\mathbf{-}\mathbf{\Psi}\mathbf{-}\left( \frac{\mathbf{k}_{2}}{\mathbf{k_{1}}}\right)\widetilde{\mathbf{X}}_{2}. \tag{41}\]  
The stability of the sliding observer, \(\widetilde{\mathbf{X}}_{2}\dot{\widetilde{\mathbf{X}}}_{2}<0\), can be guaranteed by setting the following conditions: \(\mathbf{k_{2}}\mathbf{>}\mathbf{\Gamma}\) and \(\left|\widetilde{\mathbf{X}}_{2}\right|\mathbf{\leq}\mathbf{k_{1}}\). If \(\widetilde{\mathbf{X}}_{2}\mathbf{<}0\), then the stability is ensured and established by (42). \[\dot{\widetilde{\mathbf{X}}}_{2}\mathbf{=}\mathbf{-}\mathbf{\Psi}\mathbf{-}\left(\frac{ \mathbf{k}_{2}}{\mathbf{k_{1}}}\right)\widetilde{\mathbf{X}}_{2}\mathbf{\geq}\mathbf{-}\mathbf{ \Psi}\mathbf{+}\mathbf{k_{2}}\mathbf{>}0 \tag{42}\]  
As with the previous method, if \(\widetilde{\mathbf{X}}_{2}>0\), then the stability is established by (43). \[\dot{\widetilde{\mathbf{X}}}_{2}\mathbf{=}\mathbf{-}\mathbf{\Psi}\mathbf{-}\left(\frac {\mathbf{k}_{2}}{\mathbf{k_{1}}}\right)\widetilde{\mathbf{X}}_{2}\mathbf{\leq}\mathbf{-}\bm {\Psi}\mathbf{-}\mathbf{k_{2}}<0, \tag{43}\]  
where (42) and (43) ensure that \(\widetilde{\mathbf{X}}_{2}\dot{\widetilde{\mathbf{X}}}_{2}<0\) by assuming \(\mathbf{k_{2}}\mathbf{>}\mathbf{\Gamma}\) and \(\left|\widetilde{\mathbf{X}}_{2}\right|\mathbf{\leq}\mathbf{k_{1}}\). Therefore, if the gains (\(k_{1}\) and \(k_{2}\)) are sufficiently large, then the stability of the SPO can be guaranteed.
## 3 Near Field Models of Reflectarray and Transmitarray Antennas
### Phase-Only Synthesis at 28 GHz
Then, the layout is used in a direct layout optimization (DLO) at multiple frequencies until specifications are met. where \(d_{i}\) is the distance from the feed phase center to the \(i\)-th unit cell, and \(\rho\) is a direct reflection coefficient (either \(\rho_{xx}\) or \(\rho_{yy}\), depending on the polarization). Thus, by combining (35) and (36), we can obtain the phase-shift introduced by each reflectarray element to focus the near field at a point \(\vec{r}=(x,y,z)\):  
\[\angle\rho=k_{0}\big{(}d_{i}+|\vec{r}-\vec{r}_{i}^{\prime}|\big{)}=k_{0}\bigg{(} d_{i}+\sqrt{(x-x_{i})^{2}+(y-y_{i})^{2}+z^{2}}\bigg{)}. \tag{37}\]  
Figure 8a shows the focused near field for linear polarization \(X\) when the phases in (37) are implemented. The magnitude of the field is concentrated in the middle of the coverage zone, but it presents an important drop towards the edges of the coverage zone. Indeed, the ripple in this case is 48 dB for polarization \(X\) and 38 dB for polarization \(Y\). After the POS is completed, a phase-shift distribution is obtained such that the ripple is considerably reduced at 28 GHz. A layout is obtained from this phase-shift distribution by following the steps described above and in [23] and using the MoM-LP described in [35]. When this layout is simulated, the near field shown in Figure 8b is obtained. As can be seen, the near field presents a more uniform distribution in the coverage area when compared to that of the starting point of the POS shown in Figure 8a. Now, the ripple for polarizations X and Y is 2.66 dB and 2.48 dB, respectively. Although it does not comply with the specifications of a maximum ripple of 2 dB, it is a net improvement over the starting point. However, we are also interested in the performance in the 5G NR n257 band (26.5-29.5 GHz). To that end, Table 3 gathers the results at seven equispaced frequencies, showing the maximum achieved ripple as well as the percentage of the coverage area that complies with a 1 dB, 2 dB, or 3 dB ripple. As can be seen, the best results are obtained at the frequency of design, 28 GHz. For frequencies 500 MHz above and below 28 GHz, the results deteriorate slightly. However, as the frequency moves further away from the design one, the results quickly worsen, showing the narrow bandwidth behaviour of the reflectarray antenna and the need for a wideband optimization. Figure 8: Normalized magnitude of the electric field (**a**) for the starting point of the POS and (**b**) the result of the POS simulated with a MoM-LP at 28 GHz. Coverage zone is represented by a solid red line and transition zone by a dashed red line. Near field is plotted in the NFCS.
## 3 Use Case: Watson Analytics as AaaS to Identify HF Patients Analyzing Only the ECG Signal  
### Purpose of the Use Case  
The goal of this use case is to report the experience of using a well-known AaaS system, IBM Watson Analytics, for medical information technology research purposes. The chosen task is: identifying the presence of Heart Failure (HF) by the simple analysis of the electrocardiographic signal (ECG). The research trends for analysis and decision support systems in HF are typically performed using two different approaches. One seeks to obtain predictive models that are as exhaustive as possible by including in the analysis a big amount of parameters of different nature (blood tests, biometrical values, etiology, comorbidities etc.) [17; 18; 19]. The other approach, through the application of tele-care systems, tries to obtain predictive models by restricting the inputs to easily measurable parameters detectable in tele-monitoring environments [20; 21; 22].  
This study belongs to the second line of research and has the purpose of determining whether the analysis of the Heart Rate Variability (HRV) on an ECG signal is enough to determine the presence or absence of HF, and if the result with a signal acquired for 24 h (Long Term) is comparable to an acquisition of five minutes (Short Term). The HRV technique will be described later in the article.  
The Prediction Strength (PS) obtained using cloud Watson Analytics system will be evaluated as well. The obtained results will be assessed in comparison with the existing literature, where the same goal is pursued by analyzing similar signals with different modalities (analytics using machine learning, non-cloud techniques) [23; 24].
# Central Non-Linear Model-Based Predictive Vehicle Dynamics Control
## 3 Central Predictive Control  
The steps of prediction and subsequent optimization characterize the central predictive control based on the non-linear model-based predictive control algorithm [22]. Within this contribution, the integrated model-based predictive control presented in [5] is extended and elaborated with respect to influencing the self-steering behavior. In this context, the control of roll behavior features the highest priority. Influencing the self-steering behavior and reducing pitching movements are subordinate control objectives.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Vehicle Body Mass & 1820 & kg \\ Track Width & 1.538 & m \\ Wheelbase & 2.75 & m \\ Tires & 235/55R18 & - \\ Distance of the Center of Gravity to the Front Axle & 1.343 & m \\ Distance of the Center of Gravity to the Rear Axle & 1.407 & m \\ Height of the Center of Gravity & 0.682 & m \\ Height of the Center of Pitching & 0.3257 & m \\ Height of the Center of Rolling & 0.2826 & m \\ Moment of Inertia about the Lateral Axis & 2654 & kg m\({}^{2}\) \\ Moment of Inertia about the Longitudinal Axis & 760 & kg m\({}^{2}\) \\ Moment of Inertia about the Vertical Axis & 2774 & kg m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle Parameters.  
Figure 1: Simulation Framework, © 2021 IEEE. Reprinted, with permission, from [18].
# Radar-Assisted Multiple Base Station Cooperative mmWave Beam Tracking
## 2 System Model
### Radar Measurement Model  
The radar echo measurement model is associated with the calibration process in the EKF. The RSU continuously calibrates the EKF predictions of the kinematic parameters by exploiting the radar echoes of each time slot. The radar measurement models for linear and curvilinear mobility scenarios are similar, differing only in speed, as will be noted below. Specifically, the Doppler frequency \(\hat{\mu}_{k,n}\) and the time-delay \(\hat{\tau}_{k,n}\) of the \(k\)-th vehicle can be estimated via the conventional matched-filtering [16], i.e., the transmitted signal with delay and Doppler shift is used to perform correlation calculations with the received radar echo signal. The estimated delay and Doppler frequency are acquired by maximizing correlation expression, which can be expressed as  
\[\big{\{}\hat{\tau}_{k,n},\hat{\mu}_{k,n}\big{\}}=\arg\max_{\tau,\mu}\bigg{|} \int_{0}^{\Delta T}\mathbf{r}_{k,n}(t)\mathbf{s}_{k,n}^{*}(t-\tau)e^{-j2\pi\mu t }\mathrm{d}t\bigg{|}^{2} \tag{10}\]  
By exploiting the estimated time-delay \(\hat{\tau  
where G denotes matched-filtering gain. We normalize (11) to acquire the measurement model of the angle \(\theta_{n}\) and reflection coefficient \(\beta_{n}\) as follows,  
\[\bar{\mathbf{r}}_{k,n}=\kappa\beta_{k,n}\mathbf{v}(\theta_{k,n})\mathbf{u}^{H}( \theta_{k,n})\mathbf{u}\left(\hat{\delta}_{k,n|n-1}\right)+\mathbf{w}_{\theta} \tag{12}\]  
where \(\mathbf{w}_{\theta}\) denotes the measurement noise normalized by the transmit power \(p_{k,n}\) and the matched-filtering gain G, with zero mean and variance of \(\sigma_{1}^{2}\). Accordingly, the distance \(d_{n}\) and velocity \(v_{n}\) also have measurement models, which yields  
\[\tau_{k,n}=\frac{2d_{k,n}}{c}+w_{\tau} \tag{13}\]  
\[\mu_{k,n}=\frac{2v_{k,n}cos\delta_{k,n}c}{c}+w_{\mu} \tag{14}\]  
It should be noted that for curvilinear mobility scenario, \(v_{k,n}=\sqrt{v_{x_{n}}^{2}+v_{y_{n}}^{2}}\), i.e.,  
\[\mu_{k,n}=\frac{2\sqrt{v_{x_{n}}^{2}+v_{y_{n}}^{2}}cos(\theta_{k,n})f_{c}}{c} +w_{\mu} \tag{15}\]  
where c and \(f_{c}\) denote the speed of light and the carrier frequency, respectively. \(w_{\tau}\) and \(w_{\mu}\) denote the Gaussian noise which has a mean of zero and a variance of \(\sigma_{2}^{2}\) and \(\sigma_{3}^{2}\), respectively. It should be noted that \(\sigma_{1}^{2}\), \(\sigma_{2}^{2}\), and \(\sigma_{3}^{2}\) are related to the signal-to-noise ratio (SNR) at the RSU [21], which is derived as [16]  
\[\sigma_{1}^{2}=\frac{\sigma^{2}}{Gp_{k,n}},\sigma_{1}^{2}=\frac{a_{1}^{2} \sigma^{2}}{G\kappa^{2}|\hat{p}_{k,n}|^{2}\left|\mathbf{u}^{H}(\theta_{k,n}) \mathbf{v}(\hat{\delta}_{k,n|n-1})\right|^{2}p_{k,n}},i=2,3 \tag{16}\]  
where \(a_{1}\), \(a_{2}\), \(a_{3}\) are constants related to system configuration.
## 5 Discussion
### Performance of Existing System  
In this section, we evaluate the performance of the proposed segmentation method against existing state-of-the-art systems for liver and tumor segmentation. Many existing methods have improved medical image segmentation by applying innovations like residual connection, attention mechanism, and nested skip pathways like UNet [22], Res-UNet [46], UNet++ [25], UNetAttn [47], RA-UNet [37], etc. Such improvements have helped the accuracy and robustness of segmentation, making them more reliable for challenging medical imaging tasks. For the performance assessment, we compared quantitative metrics such as the Dice coefficient, Volume Overlap Error (VOE), and Relative Volume Difference (RVD), which are essential for evaluating the segmentation accuracy.  
Table 8 provides empirical evidence supporting the effectiveness of the proposed method, demonstrating its superior performance in liver and tumor segmentation compared to existing state-of-the-art systems.  
Several methods were compared for liver segmentation on the 3 DIRCADb dataset; UNet [22] achieves a Dice coefficient of 0.871 with some variability, while Res-UNet [46] improves upon this with a score of 0.932. UNet++ [25] and UNetAttn [47] further enhance  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Liver (Dice)** & **Tumor (Dice)** \\ \hline Single Attention & 92.7 & 70.1 \\ Dual Attention & 96.22 & 74.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison single vs. dual attention.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Liver**} & \multicolumn{3}{c}{**Tumor**} \\ \cline{2-7}  & **Dice** & **VOE** & **RVD** & **Dice** & **VOE** & **RVD** \\ \hline UNet [22] & 0.871 \(\pm\) 0.011 & 0.195 \(\pm\) 0.022 & 0.084 \(\pm\) 0.023 & 0.53 \(\pm\) 0.11 & 0.66 \(\pm\) 0.11 & \(-\)0.09 \(\pm\) 0.3  
the performance, achieving Dice coefficients of 0.943 and 0.9552, respectively. RA-UNet [37] achieves a high Dice coefficient of 0.9619, [PERSON] et al. [31] also report a score of 0.9522, and [PERSON] et al. [39] achieve a Dice coefficient of 0.9613. X-Net [48] demonstrates strong performance with a Dice coefficient of 0.967, indicating high accuracy and consistency. However, the proposed method in this study outperforms all others, achieving a Dice coefficient of 0.9703 \(\pm\) 0.012, further solidifying its superior performance in liver segmentation.  
For tumor segmentation, the performance varies significantly across methods. The lowest performing method (UNet) achieves a Dice coefficient of 0.53 \(\pm\) 0.11, while the highest performing method (RA-UNet) attains 0.7047 \(\pm\) 0.113. The proposed method achieves a Dice coefficient of 0.754 \(\pm\) 0.07, demonstrating superior performance to other approaches.
### Healing and Repairing Process
Nanoengineered biomaterials that target the regenerative potential of the bone tissue and promote the osteoblastic activity can be added to the implanted scaffold in order to stimulate and accelerate local healing. Dental pulp stem cells (DPSCs) offer the change of using an autologous source in order to trigger and influence the regenerative process. The studies performed on culture cells with the association of human platelet lysate (PL) replacing the animal serum were a success. Human platelet lysate is biocompatible, generating no risk of viral transmission or contamination [22]. The dental pulp stem cells have osteogenic and angiogenic potential, with multiple differentiation possibilities. The study conducted by [PERSON] et al. [22] analysed in vitro the effects that the added human platelet lysate had upon the dental pulp stem cells. The results showed a higher rate of the cell viability and proliferation when PL in concentration 1% was used, in comparison to the normal protocol that uses foetal bovine serum (FBS). The medical procedures contribute to the solution of recreating a proper environment in order to regain the morpho-functional characteristics, to offer optimal conditions for the integration and the local remodelling process with the help of material substitutes. The term of biomedical devices refers to the replacement of the missing tissues using tested materials that can successfully fulfil all the desiderates of the specific area, maintaining a good interaction with the surrounding tissues [23], as shown in Table 1.  
The osteosynthesis method in the maxillofacial field desires to regain the functionality and morphology of the bones, creating an optimal environment for the osteogenic process with the help of different materials that are biocompatible. The present review aims to evaluate the use of titanium alloys in maxillofacial osteosynthesis under various forms, the advantages and disadvantages that accompany their use and their long-term implications in relation with the surrounding tissues. Taking into consideration the important properties that titanium possesses, the existing studies still debate if the titanium alloys materials could be left or should be removed after a period of time due to possible alterations in the surrounding tissue and body fluids. \begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Description** \\ \hline
**Elasticity Module** & The amount of force required to break the material \\
**Shear Stress** & The amount of force required to break the material in a sliding type vector \\
**Tensile Strength** & The resistance of breaking under various tensions \\
**Yield Strength** & The amount of force required to deform the material \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of metals used in maxillofacial reconstructions.
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 4 Network Architecture
### VLC Downlink
\tag{2}\]  
Here, \(A_{t}\) is the physical area of the photodetector within the receiver, \(d_{(i,p)}\) is the distance between the LED \(i\) and the photodetector \(p\), \(T_{s}(\Psi)\) is the gain of the optical filter, \(\Psi_{c}\) denotes the width of the field of vision of the photodetector, and \(m_{1}\) is the Lambertian emission order and is given by the semi-angle half-power LED as follows:  
\[m_{1}=\frac{-ln(2)}{ln(cos\theta_{\frac{1}{2}})}. \tag{3}\]  
Furthermore, the gain of the optical concentrator \(g(\Psi)\) is given by the following expression:  
\[g(\Psi)= \begin{cases}\frac{n^{2}}{sen^{2}\Psi_{c}},&0\leq\Psi\leq\Psi_{c}\\ 0&\Psi>\Psi_{c}\end{cases} , \tag{4}\]  
where \(n\) represents the refractive index. Based on the three-dimensional coordinate system described in Figure 4, it is possible to find an expression for the irradiation angle as  
\[cos(\phi)=\frac{z_{i}-z_{p}}{[(z_{i}-z_{p})^{2}+(x_{i}-x_{p})^{2}+(y_{i}-y_{p} )^{2}]^{1/2}} \tag{5}\]  
The model also assumes a control system located on each of the safety helmets, which uses an accelerometer to always keep the transceiver parallel to the roof of the mine, thereby \(cos(\phi)=cos(\Psi)\). Therefore, the channel response of the system could be written as follows. \[Hlos_{(i,p)}=\frac{T_{s}(\Psi)g(\Psi)A_{t}(m_{1}+1)(z_{i}-z_{p})^{m_{1}+1}}{2 \pi[(z_{i}-z_{p})^{2}+(x_{i}-x_{p})^{2}+(y_{i}-y_{p})^{2}]^{\frac{m_{1}+3}{2}}} \tag{6}\]  
Finally, the power received by the photodetector is given by:  
\[Pr_{(i,p)}=\frac{Pt_{i}T_{s}(\Psi)g(\Psi)A_{t}(m_{1}+1)(z_{i}-z_{p})^{m_{1}+1 }}{2\pi[(z_{i}-z_{p})^{2}+(x_{i}-x_{p})^{2}+(y_{i}-y_{p})^{2}]^{\frac{m_{1}+3} {2}}} \tag{7}\]
## 2 Preliminary and Related Works  
### Industrial Anomaly Detection  
Industrial anomaly detection is vital in ensuring industrial processes' reliability, safety, and efficiency. In this subsection, we review recent research efforts in industrial anomaly detection, including product quality inspection, system state monitoring, equipment failure detection, and other industrial applications [15; 16; 17; 18; 19; 20; 21; 22; 23].  
[PERSON] et al. [16] introduce the masked swin transformer Unet (MSTUnet) as a solution to address limitations observed in traditional convolutional neural network (CNN)-based anomaly detection methods. MSTUnet combines the swin transformer and Unet networks, demonstrating superior performance in anomaly detection and localization on industrial datasets. [PERSON] et al. [17] propose a graph-based approach to anomaly detection, leveraging graph theory and set coverage principles. This method achieves results comparable to deep autoencoders without the need for extensive parameter tuning. [PERSON] et al. [18] tackle the challenges posed by explosive time-series data in the industrial internet of things (IIoT). They introduce the AMBi-GAN, an integrated deep generative model that combines generative adversarial networks with bidirectional long short-term memory and attention mechanisms. This model shows promise in improving anomaly detection accuracy in multidimensional industrial time-series data. [PERSON] et al. [19] implement one-class classifiers for anomaly detection in industrial systems. Their approach, which includes a clustering step, achieves successful results with AUC values exceeding 90%. [PERSON] et al. [20] focus on log analysis for anomaly detection in large-scale distributed systems. Their approach, based on a combination of WGAN and encoder CNN, shows potential for online anomaly detection, contributing to data-centric AI in industrial production.  
In recent years, the proliferation of unsupervised anomaly detection techniques has sparked interest in leveraging normal images to detect anomalies in industrial processes, even when labelled anomaly data are scarce. In many industrial scenarios, the probability of faults occurring is low, making it challenging to collect a sufficient number of fault samples. In highly specialized applications, such as visual fault detection in railway freight cars, which involve a multitude of complex vehicle components and hundreds of fault types, it requires experienced domain experts to identify fault locations in images and provide precise annotations. Consequently, the cost of manual annotation is prohibitively high. In such applications, due to the scarcity of labelled fault samples, the use of unsupervised methods becomes necessary to achieve the detection of anomalies or faults.