esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations Results  
To check the theoretical performance of controllers tuned on the basis of the given relationships, simulation studies were carried out using the Matlab/Simulink 2023b software. First of all, it was demonstrated to what extent the proposed controller tuning method is capable of achieving the desired transfer function of a closed-loop system when a PI controller or a PID controller is used. In addition, the performance obtained was compared with the control quality of systems with conventional controllers that were tuned based on other known rules, including those formulated also using the direct synthesis method, to control the same plants and that were aimed at achieving the same objectives. Simulation studies also included cases where the transfer function of the model differs from that of the plant.
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
### Motivation and Contributions  
The research towards the performance analysis of cooperative relaying in NOMA D2D-based systems in multi-cell scenarios is still under consideration; especially, the behaviour of D2D devices is not often considered. In the literature [18; 26; 29], PPP is commonly used to analyze the behaviour of D2D networks. However, PPP is inferior to the Gaussian-Poisson process (GPP) when clustering the behaviour of D2D networks is under consideration. GPP is a class of cluster point processes which have a simple structure as compared to PPP and also provide a better modelling in a cooperative environment [29]. In this paper, a quality-of-service-based cooperative NOMA D2D network (Q-CNOMA) in a multi-cell interference-aware environment is proposed. The transmitters in the proposed system model, i.e., the D2D group transmitters, are distributed randomly over R\({}^{2}\) following GPP, and the D2D receivers are randomly clustered around the transmitter.  
Another important aspect of NOMA D2D communication networks is power allocation among users. The NOMA in a D2D system assigns the power to users depending on their channel conditions. This ordering of the user is not optimal for group D2D users as the D2D receivers are almost always in close proximity, which leads to the same channel conditions resulting in similar power allocation coefficients. This approach limits the benefits of the NOMA in D2D communication. In contrast to ordering on the basis of channel condition, few authors [16; 30] considered ordering the user according to their QoS. The proposed Q-CNOMA system uses user's desired rate as the QoS measure to assign the power allocation coefficients.  
The limitations and gaps in the aforementioned discussion prompted us to evaluate and explore the performance of cooperative relaying in NOMA-aided group D2D networks. In this work, we propose and investigate group D2D communication with a Q-CNOMA network and try to overcome the above-mentioned limitations and gaps. To the best of our knowledge, this is the first time that Q-NOMA has been analyzed in a cooperative group D2D relaying in a multi-cell scenario.  
The primary contributions of this work can be summarized as follows.  
* We propose a multi-cell cooperative relaying in Q-NOMA-enabled D2D communications. To completely analyze the device clustering and spatial separation, GPP is used to model the D2D transmitter topology, and the D2D receivers are randomly distributed around it.
* The power allocation coefficients are assigned depending upon the QoS of users.
* Furthermore, closed-form expressions of interference and the outage probability at the D2D receiver are derived. The interference approximation for cooperative Q-NOMA-aided D2D relaying is used to derive the closed-form expression of outage probability.
* Analytical and simulation results are established to validate the outage probability results and to demonstrate the dominance of Q-NOMA over conventional OMA-assisted D2D systems.
## 2 Materials and Methods
### Methodologies
#### 2.3.2 Estimation of NEP  
The assessment of carbon sources/sinks of vegetation is mainly measured by the net carbon budget. Net ecosystem productivity (NEP) represents the net CO\({}_{2}\) exchange between terrestrial ecosystems and the atmosphere [11]. Without considering the influence of other natural and anthropogenic factors, vegetation NEP can be expressed as the differ ence between vegetation net primary productivity (NPP) and carbon emissions from the heterotrophic respiration of soil microorganisms with the following equation.  
\[NEP=NPP-R_{H} \tag{1}\]  
[PERSON] et al. studied the relationship between soil carbon emissions and environmental factors. They established a regression model of soil microbial respiration with temperature and precipitation to estimate the distribution of soil microbial heterotrophic respiration. This empirical model has now achieved more satisfactory results in applying a carbon uptake estimation in different vegetation and climate zones, such as the Qinghai-Tibet Plateau, Southwest Arid Zone, Northeast Forest Zone, and Xuzhou city, and Jiangsu Province. Its calculation equation is as follows [31].  
\[R_{H}=0.22\times(Exp(0.0913T)+Ln(0.3145R+1))\times 30\times 0.465 \tag{2}\]  
where \(R_{H}\) is the monthly soil microbial heterotrophic respiration value (g C), \(T\) is the monthly mean temperature (\({}^{\circ}\)C), and \(R\) is the monthly precipitation (mm).
# Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?
## 2 Materials and Methods
### Database and Selected Participants  
The database was created with the help of an IOS app named ASICA. It included the recordings of 54 patients and 49 healthy speakers, all of whom were native Spanish language speakers. In this database we defined as patient any speaker with a clinical history associated to the presence of HN, which means that some patients did produce hypernasal speech when they were recorded, whereas others were non-nasal due to previous successful treatments. The patients were from Spain (N = 36), Chile (N = 16) and Ecuador (N = 2) and they were recruited from diverse clinical facilities and parents' associations:  
* Hospital Materno infantli de Malaga, Spain (N = 12);
* ASAFiLAP, the Andalusian Cleft Palate Association, Spain (N = 9);
* 22q.11 Andalusian Association, Spain (N = 10);
* Fuensocial CAIT Fuengirola, Spain (N = 1);
* Clinica Medica Fuengirola, Spain (N = 3);
* Independent Speech Therapist, Spain (N = 1);
* Hospital Gantz, Santiago de Chile (N = 16);
* Independent Speech Therapist, Ecuador (N = 2).  
The control speakers were recruited through social media and with the help of the patients' associations. For the purpose of this study, we selected from the database those patients that matched these criteria:  
1. Age and sex: female adult 18-42 years old or child 5-15 years old.
2. Mean fundamental frequency: above 180 Hz.
3. Data completion: the patient produced, at least, 90% of the utterances in the task.
4. Audio quality: loud masking noise was observed in fewer than a 10% of the utterances.
5. Confirmed HN. In the case of the patients, HN was confirmed perceptually by three trained speech therapists in at least five utterances.  
The application of these criteria resulted in a total of 39 patients (1 from Ecuador, 15 from Chile and 23 from Spain). The control group was selected so that it included the same number of speakers as the experimental group, with the two groups matched on age (for children) and age and sex (for adults). All the control speakers were from Spain except one that was from Ecuador.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Monitoring
#### 2.5.2 Magnetic Imaging  
Magnetic Resonance Imaging (MRI) is a medical imaging technique that was developed in the 1980s. MRI can be used for image acquisition from biological tissue in a non-invasive manner. It can provide very fine and precise images with high spatial resolution. A very strong magnetic field is provided by a huge magnet in combination with radiofrequency (RF) electromagnetic waves and magnetic field gradients. MR imaging is widely used in neurosurgery as it provides a contrast between the healthy and tumor tissue very well. In the last decade, fMRI or functional MRI has been used to study brain function with promising results. MRI has been used recently for in vivo tracking of and localization of nanorobots [120]. Some advantages of MRI is having excellent tissue contrast, it can be used for 3D localization of nanorobots, it is biocompatible and non-invasive, and it can be used for both imaging and localization as well as micromanipulation of nanorobots via the magnetic field. Some disadvantages of MRI systems is the high cost, it is time consuming and real-time image acquisition can be challenging, and magnetic materials can cause artifacts and degrade the quality of the images causing a lower signal-to-noise ratio.  
Magnetic Particle Imaging or MPI is an emerging method that uses a magnetic field. This method was introduced by [PERSON] et al. in 2005 [121]. Currently, commercial MPI systems are being used to conduct research on animals such as rats. The main challenge for MPI systems is the size and how they can be manufactured in larger sizes to be able to use them on humans; however, they have the potential of localizing and manipulating nanorobots.
## 3 Case Study
### OSP in Seamless Condition
#### 3.3.4 Evaluation of OSP Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Inertia** & **Acceleration** & **Maximum** & **Safety** & **Producers’** \\
**Name** & **Weight** & **Coefficients** & **Velocity** & **Threshold** & **Number** \\ \hline PSO & \(w=0.9\) & 2 & 20 & - & - \\ IPSO & \(w_{\text{max}}=0.9\), & 2 & 20 & - & - \\  & \(w_{\text{min}}=0.4\) & 2 & 20 & - & - \\ SSA & - & - & - & 0.6 & 0.8 \\ ISSAMS & - & - & - & 0.6 & 0.8 \\ AHA & - & - & - & - & - \\ IAHA & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter settings for various intelligent algorithms. Figure 14: Convergence curves of fitness for different intelligent algorithms: (**a**) convergence curve of maximum fitness value, (**b**) convergence curve of average fitness value, and (**c**) convergence curve of minimum fitness value. improvement; this is because the improved algorithms do not change the principle of the algorithm itself but only initialize the respective algorithms to initialize the populations more adequately or introduce a mathematical model describing the stochastic motion in the middle of the algorithms, which inevitably increases the algorithms' time in terms of the principle but tend to bring about a higher degree of accuracy; IAHA is not inferior to ISSAMS in terms of time cost, and IAHA takes the least time among the three improved intelligent optimization algorithms, only 101.43 s, which also shows the superiority of IAHA. Therefore, IAHA is the most advantageous algorithm. In summary, combining the evaluation indexes of the intelligent algorithm placement results, the MAC matrix diagram, and the convergence stability of the algorithm and other factors, the most suitable intelligent algorithm for the OSP of the model in this paper is IAHA, which will be used for the relevant computational analyses in a later paper. In the past, the number of sensors was selected based on observing the curve of MAC-MAX with the number of sensors and combining the economic conditions and other factors, which may not be the optimal solution. In order to have a clear judgement criterion for the determination of the number of sensors, combined with the principle of the preliminary selection of the number of sensors in the previous section, the interval of the number of sensors is selected to be [15; 25], and then the IAHA is used to calculate the fitness value, MAC-MAX, and S in this interval, and the curve and bar graph are plotted to determine the ONS, where each calculation is run 10 times to take the average value; the calculation results are shown in Figure 15. According to Figure 15, the number of sensors corresponding to the minimum MAC-MAX and fitness value is 20, and the number of sensors preferred by the S criterion is also 20. Therefore, 20 sensors can be selected to achieve the optimal layout effect, which indicates that it is feasible to achieve the ONS by the judgement criterion. The number of sensors selected by this judgement criterion is also consistent with the number of sensors initially selected in Section 3.3.2.
## 4 Results
### Community Mapping
#### 4.1.2 Exercise 2-Satellite Map  
Following the completion of the first phase of the participatory mapping, the participants were then requested to once again draw on paper the five different elements that are important in disaster risk management (namely, (1) tsunami risk areas; (2) flood risk areas; (3) critical social and cultural infrastructure; (4) safe areas, evacuation centers and evacuation routes; (5) contaminated areas). However, this time they were provided with an A0 size printed satellite map of Chanaral (one for each group).  
Figure 10 show the tsunami risk perception of Groups 1 and 2. Both groups highlight how the coastal areas are at risk, though Group 1 indicates that the risk extends to the airport, while Group 2 highlights that the tsunami can ascend through the river.  
Figure 11 shows the flood risk perception. Each of the groups chose to focus on a different aspect of flooding, with Group 1 describing the floods coming from the \"quebradas\" (ravines) in the nearby mountains, and Group 2 those brought by the main river. One of the reasons for this difference can be found in the conceptual maps (Figure 8), where Group 1 concentrated on describing the events in the area where they lived (going into great effort to draw all the different flow paths), while some in Group 2  
Figure 10: Tsunami risk perception in Chanaral: (**a**) Group 1, (**b**) Group 2.  
Figure 9: The map of Chanaral (reality) vs. Sketch map (perception): (**a**) Group 1, (**b**) Group 2.  
lived in a different side of city, and thus suffered more from the river flooding (they nevertheless also indicated flooding from the ravines, though these are difficult to appreciate in Figure 11b). Combining the results of the two maps appears to provide an accurate overall description of the flooding from both the rivers and ravines.  
Figure 12 indicates the critical evacuation infrastructure in the town. Again, Group 1 concentrated on depicting the most important facilities in the area where they lived. However, both groups where generally in good agreement regarding town-wide important locations (highlighting high areas to evacuate to--such as the lighthouse and the far side of the airport--the hospital, and schools).
## 1 Introduction
### Main Findings and Significance  
We implemented our crypto processor in Verilog (HDL) programming language using the Vivado IDE tool. We show the implementation results over \(GF(2^{233})\) as a trend after the post-place-and-route level on Virtex-6 and Virtex-7 FPGA devices. The trend has been shown for operating frequencies of 100, 50, and 10 MHz, showing that decreasing clock frequency increases the computation time or latency. Moreover, the higher frequencies consume more power. Our crypto processor outperforms in hardware resources and consumes power on modern (28 nm) Virtex-7 FPGA devices. On identical FPGA, it utilizes 391 slices, achieves a maximum 161 MHz frequency, takes 4.45 ms latency, and consumes 77 mW when computing one ECC PM operation. Results and comparisons indicate that our crypto processor design benefits applications that demand low area and less power for cryptographic computations.  
The structure of this manuscript is as follows: The mathematical background is presented in Section 2. We outline our proposed crypto processor architecture in Section 3. Section 4 provides implementation results and comparisons. Finally, we conclude in Section 5 to wrap up the paper.
# Signal Detection for GSTFIM Systems with Carrier Frequency Offset +
## 3 Proposed MMSE Detectors for GSTFIM Systems
In general, the proposed MMSE detector is only suitable for the situation of Gaussian noise. Under non-Gaussian noise, the calculation of the mean and variance of \(\mathbf{X}_{l}\) will be inaccurate, resulting in the inaccurate calculation of the subsequent posterior probability. In that case, we could substitute the MMSE criterion in Step 4 by the maximum correntropy criterion [26] to mitigate the inaccuracies. The correntropy between \(\mathbf{X}_{l}\), \(\mathbf{\tilde{X}}_{l}\) with joint distribution \(F_{XY}\big{(}\mathbf{X}_{l},\mathbf{\tilde{X}}_{l}\big{)}\) is defined as  
\[V\big{(}\mathbf{X}_{l},\mathbf{\tilde{X}}_{l}\big{)}=\mathrm{E}\big{[}\kappa \big{(}\mathbf{X}_{l},\mathbf{\tilde{X}}_{l}\big{)}\big{]}=\int\kappa\big{(} \mathbf{X}_{l},\mathbf{\tilde{X}}_{l}\big{)}dF_{XY}\big{(}\mathbf{X}_{l}, \mathbf{\tilde{X}}_{l}\big{)}, \tag{19}\]  
where \(\mathrm{E}\) is the expectation operator and \(\kappa(\cdot,\ \cdot)\) denotes a shift-invariant Mercer kernel, and  
\[\kappa\big{(}\mathbf{X}_{l},\mathbf{\tilde{X}}_{l}\big{)}=\mathrm{G}_{\sigma} (e)=\exp\biggl{(}\frac{-e^{2}}{2\sigma^{2}}\biggr{)}, \tag{20}\]  
where \(e=\big{(}\mathbf{X}_{l}-\mathbf{\tilde{X}}_{l}\big{)}\) and \(\sigma\) represents the kernel bandwidth. Then, the correntropy can be estimated by  
\[\bar{V}\big{(}\mathbf{X}_{l},\mathbf{\tilde{X}}_{l}\big{)}=\frac{1}{N}\sum_{i= 1}^{N}\mathrm{G}_{\sigma}(e(i)), \tag{21}\]  
where \(e(i)=\mathbf{X}_{i}-\mathbf{\tilde{X}}_{i}\).
# Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System
## 2 Thermal Model of a Single PV Module
### Thermal Balance Equations  
A PV module contains a number of layers from the front to the back side as follows: glass (\(g\)), EVA (\(e^{\prime}\)), silicon PV cell, another EVA layer (\(e^{\prime\prime}\)), and backsheet (\(b\)) [13]. The batteries were mounted on the back side of the PV module by an aluminum-bar structure so as to leave an airgap (d \(\approx\) 2 cm) between the batteries and the PV backsheet, as depicted in Figure 3.  
The thermal balance for each layer can be expressed taking into account all the thermal power exchanges such as convective (_conv_), radiative (_rad_), and conductive (_cond_) powers \(Q\). The incident thermal power on the surface of the PV module is transmitted to the different layers (\(G_{1}\), \(G_{2}\), \(G_{3}\), and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer’s Material** & \(s\) **[mm]** & \(k\) **[W/(m-K)]** & \(\rho\) & \(\tau\) & \(\alpha\) \\ \hline Glass & 4.0 & 1.8 & 0.1 & 0.88 & 0.02 \\ Ethylene Vinyl Acetate (EVA) & 0.4 & 0.35 & - & 0.97 & 0.03 \\ Silicon PV cell & 0.4 & 150 & - & - & 1 \\ Backsheet & 0.3 & 0.3 & - & - & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thickness (\(s\)), thermal conductivity (\(k\)), and optical coefficients (\(\rho\), \(\tau\), \(\alpha\)) of the PV module layers.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & \(\epsilon\) \\ \hline PV module front surface (glass) (\(f\)) & 0.91 \\ PV module back surface (backsheet) (\(b\)) & 0.85 \\ Sky & 0.91 \\ Ground & 0.94 \\ Polished aluminum plates & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Emissivity coefficients.  
\(G_{4}\)) on the basis of absorption and transmission coefficients. The summary of these thermal balances can be expressed as:  
\[\left\{\begin{array}{l}Q_{conv,f}+Q_{rad,f}-Q_{cond,g}+G_{1}=0\\ Q_{cond,g}-Q_{cond,^{\prime}}+G_{2}=0\\ Q_{cond,e^{\prime}}-Q_{cond,e^{\prime\prime}}+G_{3}-P_{pv}=0\\ Q_{cond,e^{\prime\prime}}-Q_{cond,b}+G_{4}=0\\ -Q_{comp,b}+Q_{rad,b}+Q_{cond,b}=0\end{array}\right.,\text{ being }\left\{\begin{array}{l}G_{1}=\alpha_{f} \cdot G_{n}\cdot A\\ G_{2}=\tau_{f}\cdot\epsilon_{EVA}\cdot G_{n}\cdot A\\ G_{3}=\tau_{f}\cdot\tau_{EVA}\cdot(\alpha_{c}K+\alpha_{EVA}(1-K))G_{n}\cdot A \\ G_{4}=\tau_{f}\cdot\tau_{EVA}\cdot\tau_{EVA}\cdot\alpha_{b}(1-K)G_{n}\end{array}\right. \tag{3}\]  
where \(K\) represents the ratio between the area of silicon of the PV cells and the total PV module area.  
In addition to the previous thermal balances (Equation (3)), in the case of the PV module with energy storage, the thickness of the batteries (_batt_) can be taken into account as:  
\[Q_{conv,batt\text{-}b}+Q_{rad,batt\text{-}g}+Q_{cond,al+batt}+Q_{conv,batt\text {-}g}+Q_{rad,batt\text{-}g}=0 \tag{4}\]  
where \(Q_{cond,al+batt}\) is the conductive heat transfer between the battery layer and aluminum sheet.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 4 Simulation and Results
### Formation Moving Using Decentralized Virtual Linkage  
In this section, the nine agents are required to align in a line formation with a uniform distance of 0.1 m and move around a circle with a radius of 1 m in 100 s. To perform such tasks, the trajectory of the virtual linkage is specified:  
\[r_{d}=\left(-\frac{9\pi}{500}\cos(\frac{\pi}{50}t),\frac{9\pi}{500}\sin(\frac {\pi}{50}t)\right) \tag{13}\]  
Figure 6: Predefined virtual linkage used in these two simulations.  
Figure 7: Information-exchange topologies.  
Meanwhile, the attitude of the virtual linkage can be expressed as the angle from the \(x\) direction of the virtual link1 to the world coordination frame \(x\) direction and is also specified as a function of time  
\[q_{d}=\frac{\pi}{50}t+\frac{\pi}{2} \tag{14}\]  
Using the virtual linkage approach, the nine agents are able to maintain a line formation with a uniform distance of 0.1 m by specifying:  
\[\Theta_{d}=\theta_{1}=0^{\circ} \tag{15}\]  
\[\lambda_{d}=[\lambda_{1x},\lambda_{1y},\lambda_{2x},\lambda_{2y}]=[0.4,1,0.4,1] \tag{16}\]  
Figure 8 shows the snapshots during the simulation for 100 s.  
Figure 9a,b show the reference state and desired trajectory of the virtual linkage defined in Equations (13)-(16). The individual elements of each \(\xi_{i}\) are plotted in Figure 9c-h respectively. As can be seen from the figures, the nine random initialized \(\xi_{i}(i=1,2,\dots,9)\) finally converge into reference state defined in Equations (13)-(16). Recall the implication of virtual linkage states \(\xi_{i}\) illustrated in Section 3.2.1 in which the convergence of \(\xi_{i}\) indicates that the nine agents finally forms a specified virtual linkage and moves in desired formation shapes along the specified path. Therefore, the simulation results indicate the effectiveness of moving in formation using the virtual linkage approach.  
Figure 8: Snapshot of the formation moving using virtual linkage.