esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles of Doppler OCT for Otology
As another approach to enhance the sensitivity for measuring phase changes, the Fourier frequency analysis approach is applied to Equations (5) and (7). Fourier transformed Equation (5) becomes:  
\[ \begin{array}{l}p(z,f)=FT[\Delta\Phi(z,t)]\\ \\ =\frac{(2\pi)^{3}}{\lambda}\delta zf_{0}\Delta t[\delta(f-f_{0})+\delta(f+f_{0 })]=|p(f)|\exp[j\theta(f)]\\ \end{array} , \tag{8}\]where \(f\) is the frequency variable and \(|p(f)|\) and \(\theta(f)\) are the magnitude and the phase of the vibration at the specific frequency, respectively. Therefore, the magnitude of the vibration source at a specific frequency (\(f_{0}\)) is expressed as:  
\[\delta z=\frac{|p(f_{0})|\lambda_{0}}{\left(2\pi\right)^{3}\!f_{0}\Delta t}. \tag{9}\]  
By applying a Fourier transform to Equation (7), the obtained phase noise of the system was able to be assumed as the Gaussian noise and phase data were chosen by the applied specific frequency (\(f\)) of the input sound, which enhances SNR for the signal of interest. Therefore, the signal of the region-of-interest was amplified without effect on Gaussian noise, which enables to measure the phase changes at a subnanometer scale [60; 61; 62]. In this section, we briefly introduced principles of phase-resolved Doppler OCT methods to measure the phase difference and magnitude of vibration in otology. There are various methods of Doppler-related OCT techniques, such as phase-resolved color Doppler, phase-resolved Doppler variance, intensity-based Doppler variance, and optical coherence vibrometry. The phase-resolved color Doppler technique was the first introduced method of Doppler OCT. However, phase-resolved color Doppler is based on time-domain OCT, which has a comparable low scanning speed that is comparably slow. Therefore, other methods of Doppler OCT have been widely studied to compensate for these limitations. Phase-resolved Doppler variance, which applied an autocorrelation algorithm for complex data of each position, has been introduced to measure the phase variance of the sample. In addition, intensity-based Doppler variance was presented, which increases the time interval between adjacent A-lines, to enhance the sensitivity. However, variance-based Doppler methods have been primarily applied to measure the flow. Therefore, we focused on the vibration-related phase and magnitude differences, which are appropriate for otology applications.
# Hamming Distance Optimized Underwater Acoustic OTFS-IM Systems
## 5 Conclusions  
This paper proposes an OTFS-IM underwater acoustic communication system based on Hamming distance optimization, which introduces index modulation into the DD domain. The performance of the OTFS-IM underwater acoustic communication system is further improved by the proposed Hamming distance optimization model to eliminate the redundancy present in the indexed combinatorial sequence. The proposed underwater acoustic communication system and Hamming distance optimized index mapping model were verified by varying the size of the sub-grid groups and the number of activated sub-grids in them and by using different detection methods at the receiver side. The results show that the proposed Hamming distance optimization model optimizes the BER performance of the OTFS-IM underwater acoustic communication system and further enhances the robustness of the OTFS-IM underwater acoustic communication system. Combined with the fact that the OTFS-IM underwater acoustic communication system has the ability to control DD domain sub-grid selection activation more flexibly than conventional OTFS systems, this feature enables OTFS-IM systems to achieve a better compromise between transmission reliability and SE in time-varying channels. Therefore, it can provide a more flexible and reliable solution for underwater acoustic communication systems.  
Conceptualization, X.G.; methodology, X.G. and B.W.; software, Y.Z.; validation, X.G., B.W. and Y.Z.; formal analysis, Z.H.; investigation, Z.F.; resources, Y.Z.; data curation, X.G.; writing--original draft preparation, X.G.; writing--review and editing, B.W., Y.Z. and Z.F.; visualization, X.G.; supervision, X.G.; project administration, Y.Z.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 52071164 and in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX23_3879.  
Not applicable.  
The data presented in this paper are available after contacting the corresponding author.  
The authors would like to thank the anonymous reviewers for their careful reading and valuable comments.  
The authors declare no conflict of interest.
## 4 Outlook  
The global grid assignment for which we need to identify the absolute position of a laser point in the grid, presents a considerable challenge during the course of reconstruction. In contrast to ex vivo experiments where a highly controlled setting can ensure a sufficient visibility of grid edges and corners, in vivo recordings suffer from the limited space in the pharynx. Shadows from superior structures and distortion of the laser points on the extreme curvature of the epiglottis and the lateral pharynx walls affect the laser grid especially in the peripheral areas (see Figure 17).  
Figure 16: Evaluation of total duration of the laser point detection algorithm compared to its previous implementation by [20] subdivided in the initial grid detection and the tracking algorithm.  
Figure 17: Uncontrollable conditions for global grid assignment in in vivo recordings due to shadows or distortions of the grid points on pharynx tissue.  
The current standard approach to face this challenge requires an experienced operator thoroughly watching the complete footage in order to identify significant edges and corners which is very time consuming and still prone to errors. Due to the geometry of the experimental set-up, mistakes in the global assignment of the grid produce a dramatic effect on the results. A mistaken offset of only one column in the assignment results in a shift of the entire reconstructed structure of about 6.4 mm along the vertical axis.  
In order to compensate for this shortcoming, we suggest deviating from the conventional, regular grid as shown in Figure 18 (on the left) and using a spatially irregular laser grid instead. By the use of diffractive optical elements and liquid crystal-based spatial light modulators, it is possible to imprint any desired pattern on the spatial distribution of the laser projection. A centered cross hair in the regular grid as depicted in Figure 18 (middle) would facilitate the global grid assignment in the absence of the outer edges and corners. However, the cross hair itself cannot be relied on for the 3D reconstruction. Another conceivable approach might be a random pattern of laser points as in Figure 18 (on the right), where each subset of points displays a unique spacing towards its neighbors. This could however be impaired by a distortion of the relative distances due to the curvature of the vocal folds.  
Nevertheless, we strongly believe that the use of an irregular laser projection will allow for further automation of the detection algorithms and significantly reduce its proneness to errors. This improvement will greatly facilitate the analysis of the preclinical trials, which we have planned to determine the added value by the 3D-laserendoscopy for future patients.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## Preliminaries
### Markovian Model
#### 3.2.2 Full Order Dynamic Output-Feedback Controller for MJLS
Considering \(y(k)=x(k)\) in Equation (1), the closed-loop system is given by  
\[\mathcal{G}_{k}: \begin{cases}\hat{x}(k+1)=(A_{i}+B_{i}K_{i})\ x(k)+E_{i}w(k), \\ z(k)=(C_{zi}+D_{z_{i}}K_{i})\ x(k)+E_{zi}w(k)\end{cases}  \tag{10}\]  
Considering a generalized Bernoulli distribution for MJLS (1), the state-feedback control law (9) that assures an upper bound for the \(\mathcal{H}_{\infty}\) norm and the MSS of the closed-loop MJLS (10) can be obtained by solving the LMI conditions given in [20], reproduced as follows. **Lemma 3**. : _There exists a state-feedback controller (9), such that \(\|\mathcal{G}_{k}\|_{\infty}^{2}<\gamma\) if and only if there exist symmetric matrices \(Z_{i}\) and \(X\), and a matrix \(Y_{I}\) with compatible dimensions satisfying the LMIs_  
\[ \begin{bmatrix}Z_{i}&\bullet&\bullet&\bullet\\ 0&\gamma I&\bullet&\bullet\\ A_{i}X+B_{i}Y_{I}&E_{i}&X&\bullet\\ C_{zi}X+D_{zi}Y_{I}&E_{zi}&0&I\end{bmatrix} >0,\forall i\in\mathbf{K},\forall l \in\mathbb{L}, \tag{11}\]  
\[Z_{p}-X<0, \tag{12}\]  
_where \(Z_{p}=\sum_{j=1}^{\sigma}p_{j}Z_{j}\). If a feasible solution is found, the partially mode-dependent state-feedback control gains are given by \(K_{I}=Y_{I}X^{-1}\)._  
System (1) associated with the filter-observer (\(z_{f}(k)=x(k)\)) given in Equation (4) and the state-feedback control law (9) with \(x(k)=z_{f}(k)\) can be rewritten as  
\[\mathcal{G}: \begin{cases}\hat{x}(k+1)=\hat{A}_{o;i}\hat{x}(k)+\hat{B}_{o;i}w(k )\\ z(k)=\hat{C}_{o;i}\hat{x}(k)+\hat{D}_{o;i}w(k)^{\prime}\end{cases}  \tag{13}\]with \(\hat{x}=[x(k)^{\prime}\ x_{f}(k)^{\prime}]^{\prime}\in\mathbb{R}^{2n_{x}}\), and the following matrices  
\[\hat{A}_{oi} = \begin{bmatrix}A_{i}+B_{i}K_{i}D_{fi}C_{yi}&B_{i}K_{i}C_{fi}\\ B_{fi}C_{yi}&A_{fi}\end{bmatrix} ,\ \hat{B}_{oi}= \begin{bmatrix}E_{i}+B_{i}K_{i}D_{fi}E_{yi}\\ B_{fi}E_{yi}\end{bmatrix} , \tag{14}\] \[\hat{C}_{oi} = \begin{bmatrix}C_{zi}+D_{zi}K_{i}D_{fi}C_{yi}&D_{zi}K_{i}C_{fi} \end{bmatrix} ,\ \hat{D}_{oi}= \begin{bmatrix}E_{zi}+D_{zi}K_{i}D_{fi}E_{yi} \end{bmatrix} .\]  
When the matrices of system (13) are known, the \(\mathcal{H}_{\infty}\) norm can be computed using the conditions of Lemma 1. However, it is important to emphasize that Lemmas 2 and 3 provide optimal solutions for partially mode-dependent filter and static state-feedback controller when considering a generalized Bernoulli distribution (transition probability matrix with identical rows) for each problem solved independently. When the solutions are combined, only a suboptimal solution in terms of the \(\mathcal{H}_{\infty}\) performance is obtained. Actually, the mode-independent output-feedback is an open problem in the MJLS literature (only suboptimal solutions are available so far).
# Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System
## 4 Emerging Technologies
### Reality
#### 4.4.4 Mixed Reality (MR)  
VR recreates real life in a digital world, while AR enriches the real world with virtual displays. Combining these two distinctive perceptions unveils the idea of Mixed Reality (MR) [65], which is an amalgam of real and virtual worlds. MR is an immersive technology that merges the physical and the real world to provide a new visualized environment where physical and digital items and objects coexist and interact with each other in real-time.  
MR not only overlays virtual objects in the real-world environment, but also embraces virtual objects into the real world. There are two kinds of MR technologies: MR that begins with the real world, where virtual objects are overlaid on the virtual world (real to virtual), and MR that begins with the virtual world and the virtual world content is anchored to real world devices (virtual to real) [66].  
MR has already been used in various applications across fields, including the medical sector. MR combines smart glasses with surgical processes so as to provide a better level of healthcare. Specifically, headsets based on MR have the ability to share information between doctors. This means that it is not necessary for many doctors to be in close proximity to the patients. In some diseases such as contagious disease, the distance between doctors and patients enhances doctor safety and reduces the use of personal protective equipment [67]. Furthermore, MR gives surgeons the ability to perform an operation on a patient from a distance, while it improves the skills of the nursing staff that are able to perform many nosocomial operations virtually without touching the patients. Figure 7 illustrates the virtualization technologies.  
Table 3 presents some popular healthcare applications of each technology separately and the corresponding 5G network services of appliance in order to meet the specifications of efficient operation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Healthcare Applications** & **Network Services** \\ \hline Big Data Analytics & \(\bullet\) & Medical diagnosis & eMBB \\ Cloud Computing & \(\bullet\) & Real-time health status monitoring & eMBB \\  & \(\bullet\) & Illness or disorder prediction & eMBB \\ Artificial Intelligence & \(\bullet\) & Drug discovery & mMTC \\ \hline \hline \end{tabular}
\end{table}
Table 3: Healthcare application domains and network services of emerging technologies.
## 3 Related Works  
### Methods to Solve Trajectory Optimization in MPC  
When the system is linear, the constraints are affine and the cost functions are quadratic, the associated MPC optimization problem is a quadratic program. Several convex optimization algorithms have been proposed [23; 24] that are fast, robust, and possess global convergence guarantees. In contrast to the first-order approaches mentioned above, Newton-type methods are able to deal with general constraints and have faster rates of convergence [25]. This leads to a considerable reduction in computation time and makes them better suited to handling real-time nonlinear problems.  
The proximal averaged Newton-type method for optimal control (PANOC) is applied, considering obstacle avoidance [26]. Compared with SQP and IP, PANOC does not require the solution of linear systems or quadratic programming problems at every iteration, and it involves only very simple operations such as vector additions and scalar and inner  
Figure 1: Proposed human–robot collaboration system design.  
products. Additionally, PANOC is able to converge globally to a point that satisfies the first-order optimal conditions of the problem from any initial guess. As an extension, refs. [27; 28] combines the proximal averaged Newton-type method for optimal control (PANOC) with the penalty and augmented Lagrangian methods to compute approximate stationary points of non-convex problems.  
Although MPC is widely used in practice, it is known that suboptimality and infeasibility caused by the numerical optimization method can compromise the stability and positive invariance (satisfaction of constraints) properties of the MPC-controlled system. There have been a few works, such as [29], that attempt to remedy this issue, but these are limited to linear dynamical systems, convex formulations, and specific numerical optimization methods. To the best of the authors' knowledge, no results exist on the problem of NMPC stability under inexact numerical optimization. In this work, we focus on the issue of collision avoidance and demonstrate that the combination of nonlinear MPC, PANOC, and a CBF-based framework leads to a significant improvement in safety in terms of obstacle avoidance.
###### Abstract  
Biogas produced by anaerobic digestion contains different types of contaminants, and it is preferable to eliminate those contaminants before biogas' energetic valorization or upgrading to biomethane as they are harmful to human health and detrimental to combustion engines. This study presents the biogas cleanup system optimized by an Italian full-scale anaerobic digester treating food waste (FW) and represented by micro-oxygenation, chemical scrubber, cooling, and activated carbon sections. The cleaned biogas is upgraded to biomethane using a membrane-based upgrading unit and injected into the natural gas network for transport sector use. H\({}_{2}\)S and volatile organic compound (VOC) concentration in raw biogas was reduced from an annual average value of 1207 ppm\({}_{v}\) and 895 mg/Nm\({}^{3}\), respectively, to below 0.1 mg/Nm\({}^{3}\) in the final biomethane. In the summer, the H\({}_{2}\)S average content in raw biogas was 833 ppm\({}_{v}\) due to a greater presence of low-sulfur-containing vegetables in FW, while in the winter it was an average of 1581 ppm\({}_{v}\) due to a larger portion of protein-containing FW. On the other hand, raw biogas VOC content in the winter was an average of 1149 mg/Nm\({}^{3}\), with respect to 661 mg/Nm\({}^{3}\) in the summer, due to the greater consumption of citrus fruits containing high amount of terpene compounds. The concentration of other trace contaminants, such as HCI, NH\({}_{3}\), and siloxanes, was lowered from 17, 36, and 0.6 mg/Nm\({}^{3}\) in raw biogas, respectively, to below 0.1 mg/Nm\({}^{3}\) in the final biomethane. All the considerations and evaluations underlying the technological and plant engineering choices together with the individuation of the best operating conditions are discussed.  
biogas contaminants; biogas pre-treatment; biomethane; food waste anaerobic digestion; full-scale anaerobic digester +
Footnote †: journal: Applied Sciences
## 3 Experiment Two: Verification of the Localisation Thresholds
### Materials and Methods
#### 3.1.2 Test Stimuli  
The stimuli used for the experiment were the same sources used in Experiment One. The test stimuli were presented to subjects in the following conditions: (1) main layer only; (2) height layer only; (3) vertically oriented phantom image with 0 dB interchannel level difference (ICLD) and; (4) vertically oriented phantom image with the localisation threshold applied to the height layer. The ICTDs applied to the height layer for the phantom image conditions were 0 and 1 ms. The 10 ms condition was not tested for the following reasons. Firstly, as there was no significant difference between the localisation thresholds obtained for 1 and 10 ms it was deemed unnecessary to test both conditions. Furthermore, as discussed earlier, 10 ms represents a condition whereby the path difference between the direct sound arriving the main and height layers respectively is around 3.4 m, which is fairly large in practice. It was  
Figure 7: Localisation thresholds for combined sources.  
therefore decided that the 1-ms condition would be more representative of a practical configuration, with the resultant path difference being only around 0.34 m.  
Although not necessarily integral to the verification test, the 0-dB ICLD and height layer only conditions were included in the experiment in order to reduce any expectation biases. During preliminary tests, in which only the main layer only and localisation threshold conditions were considered, subjects reported that hearing all stimuli originate from the same position in a localisation test was confusing. Furthermore, some were led to believe that the stimuli could not all be coming from the same location and this forced them to provide different answers to what was actually being perceived. The height layer only and 0-dB conditions were therefore included in order to introduce stimuli that were in a position away from the main layer only condition. This was found to prevent the issue.  
All stimuli were presented at 70 dB LAeq at the listening position when presented from the main layer only. The increase in amplitude when the stimuli were presented as vertically arranged quadraphonic phantom images was dependent on the localisation threshold applied to the height layer. In the case of the 0-ms condition, the height layer was attenuated by 9.5 dB with respect to the main layer, whilst for 1 ms the attenuation was 7 dB, which was based on the results from Experiment One. In total, there were 30 stimuli, being the main and height layer-only conditions (10), the localisation threshold conditions (10--five sources, two ICTDs) and the 0-dB ICLD conditions (10--five sources, two ICTDs).
# A Low-Cost Secure IoT Mechanism for Monitoring and Controlling Polygeneration Microgrids
## 3 The Proposed System Architecture
### The IoT WSANCS Architecture
#### 3.1.1 Range and Operation Modes  
The maximum distance to transmit data between two XBees was around 60 feet (18.3 m), taking into consideration concrete walls. The XBee's radio modules require 3.3 V with a power-down current < 10 \(\mu\)A, a transmit (TX) current of 45 mA, and a receive (RX) current of 50 mA. A consumption of less than 50 \(\mu\)A is expected in sleep mode operation; in this mode, for 1 min, the micro-controller keeps receiving information from the current or voltage sensors. Then, when the XBee wakes up, the acquired power consumption is sent to the sink node.
## 5 Results
### Exploratory Analysis
#### 5.1.3 Time Series Decomposition  
Time series decomposition analysis allows for the identification of seasonal patterns, trends, variability, and cycles present in the data. This information greatly aids in a deep understanding of the data to be used. The decomposition of the time series of the variable _kWh_ into its components of trend, seasonality, cycle, and randomness is shown in Figure 6. In the resulting plots, a clear seasonal effect and recurrent patterns can be observed in the time series corresponding to the variable _kWh_. Similarly, there is an upward trend, likely due to increased electricity consumption in the summer season, which has implications for the residual component as values begin to deviate from zero during periods of increased consumption, indicating a change in behavior or patterns.