esa-sceva/satcom-chunk-collection huggingface dataset:
# Inducing Optimality in Prescribed Performance Control for Uncertain Euler-Lagrange Systems
## 5 Simulation Results
### Case A: Pendulum
The initial conditions for these simulations are included in Table 1, along with a comparison between the costs of the initial admissible PPC policy, the final one, and an optimal control policy obtained through a successive approximation strategy similar to [30]. The response of the system under the new optimal policy was simulated for 3 s for the same initial conditions and is illustrated in Figure 4, along with the control effort requested by it. While it is clear that this policy results in significantly less control effort and thus a decrease in cost, note that this comes at the expense of sacrificing prescribed performance, as it is dictated by the parameters of the adopted performance function. This becomes evident in Figure 4, where it can be seen that for the initial conditions \(x_{0}^{1}\), the tracking error \(\sigma_{1}(t)\) in our method stays within the region defined by the performance function, while the tracking error \(\sigma_{2}(t)\) escapes the region, indicating the other policy's trade-off of prescribed performance for optimality. Figure 2: Comparison between the actual values and their estimates for \(f(q,\dot{q})\) (**top**) and \(g(q)\) (**bottom**) for 300 random samples within \(\Omega_{q}\times\Omega_{\dot{q}}\). Regarding our method, it can be easily observed that the cost was successfully decreased, owing mainly to the reduction in the required control effort and despite the fact that we confined the system evolution within the set where the approximation capabilities of the polynomial structure held. Finally, Figure 5 shows the evolution of the transformed error. Notice that even though all initial conditions lie within \(\Omega_{z}\), if we obtain the weights \(w_{L}\) through the solution of the unconstrained least squares problem (17), the trajectories of \(e\) escape the set \(\Omega_{z}\), and \(\tau^{*}\) grows unbounded. However, by imposing a set of inequalities for \(w_{L}\) as described in Section 4.2, the transformed error evolves strictly within \(\Omega_{z}\), where the polynomial approximation holds, and thus optimality along with prescribed performance specifications are met. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Condition** & & & **Final Policy’s Cost** \\
**[**\(q(0)\)**,**\(\rho(0)\)**,**\(e(0)\)**]** & **Initial Policy’s Cost** & **Final Policy’s Cost** & **(Without Prescribed Performance)** \\ \hline \([0.73,0.148,0.45]\) & 22.60 & 16.79 & 0.47 \\ \([0.2,0.165,0.25]\) & 45.54 & 34.83 & 1.01 \\ \([0.56,0.174,-0.37]\) & 3.21 & 1.59 & 0.05 \\ \([0.37,0.173,0.37]\) & 6.63 & 1.4 & 0.21 \\ \([0.43,0.11,-0.13]\) & 3.90 & 1.80 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the initial PPC policy and the final optimal one. Figure 3: The state response of the calculated optimal PPC scheme (**top**). Comparison between the control effort \(\tau-G(q_{d})\) requested by the initial and the optimal PPC policies (**bottom**).
###### Abstract  
Electroencephalography (EEG) signals greatly suffer from gradient artefacts (GAs) due to the time-varying field gradients in the magnetic resonance (MR) scanner during the simultaneous acquisition of EEG and functional magnetic resonance imaging (fMRI) data. The GAs are the principal contributors of artefacts while recording EEG inside an MR scanner, and most of them come from the interaction of the EEG cap and the subject's head. Many researchers have been using a spherical phantom to characterize the GA in EEG data in combined EEG-fMRI studies. In this study, we investigated how the phantom shape could affect the characterization of the GA. EEG data were recorded with a spherical phantom, a head-shaped phantom, and six human subjects, individually, during the execution of customized and standard echo-planar imaging (EPI) sequences. The spatial potential maps of the root-mean-square (RMS) voltage of the GA over EEG channels for the trials with a head-shaped phantom closely mimicked those related to the human head rather than those obtained for the spherical phantom. This was confirmed by measuring the average similarity index (0.85/0.68). Moreover, a paired \(t\)-test showed that the head-shaped phantom's and the spherical phantom's data were significantly different (\(p<0.005\)) from the subjects' data, whereas the difference between the head-shaped phantom's and the spherical phantom's data was not significant (\(p=0.07\)). The results of this study strongly suggest that a head-shaped phantom should be used for GA characterization studies in concurrent EEG-fMRI.  
Keywords:artefact correction; head-shaped phantom; spherical phantom; gradient artefact; simultaneous EEG-fMRI +
Footnote †: journal: Applied Sciences
# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 4 Systematic Mapping Study Results
### Most Relevant Articles  
To present the readers with a selection of the good-quality articles presented in this survey paper, we further narrowed down and short-listed 19 journal and conference articles. In particular, only articles published from 2018 to 2020 in Q1/Q2 level ([[https://www.scimagojr.com/journalrank.php](https://www.scimagojr.com/journalrank.php)]([https://www.scimagojr.com/journalrank.php](https://www.scimagojr.com/journalrank.php))) journals and A/B ranked ([[http://www.conferenceranks.com](http://www.conferenceranks.com)]([http://www.conferenceranks.com](http://www.conferenceranks.com))) conferences were identified as relevant, and these are summarized in Table 9.  
Table 9 depicts pivotal aspects that were examined in the reviewed articles, including publication year and type, techniques, approaches, models/algorithms, evaluation metrics, and the sources and size of the datasets used to conduct the experiments. It can be seen that it is almost impossible to directly compare the articles in terms of performance due to the variety of algorithms/models and datasets applied to conduct the sentiment analysis task. However, it is interesting to note that the performance of sentiment analysis systems has generally improved over the years, achieving an accuracy of up to 98.29% thanks to the recent advancements of deep learning models and NLP representation techniques.
## 4 Numerical Simulation and Discussion
### Numerical Results
#### 4.3.1 Thermal and Seepage Field before Impoundment  
The simulated thermal and seepage fields are shown in Figure 14. The calculation result fits the previous study on the hydrogeological context which has been discussed in Section 2. As it has been pointed out, the upward water brings heat from limestone and causing abnormally high ground temperature near the dam site. Simulated temperature distribution near the dam site is extracted and compared to the measured values. As shown in Figure 15, the calculated multi-field distribution of the stationary state is consistent with the hydrogeological exploration results in Figure 3. It has to be noted that the underlying limestone stratum with higher permeability is the key, which plays as a seepage channel and makes the upward seepage flow happen.  
Figure 14: Thermal and seepage distribution before impoundment: (**a**) B–B1 section in Figure 13; (**b**) C–C1 section in Figure 13.
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 5 Simulations and Experimental Results
### Experimental Results  
As illustrated in Figure 11a,b, the proposed techniques were implemented on the dSPACE 1104 platform with a sampling frequency of 10 kHz. The grid was simulated in real-time within the dSPACE, and the grid voltage was fed to the proposed system under review. The performance variables were monitored using a digital scope and the dSPACE Digital to Analog converters D/A with 16-bit resolution. Two test cases were executed. In Case (1), a step change in the DC link voltage was used to test the system shown in Figure 12. Stepped voltage for the DC link ranged from 400 V to 600 V. The proposed controller's performance parameters for the DC link voltage response were as follows: settling time (\(t_{s}\)), rise time (\(t_{r}\)), and maximum percentage overshoot (3.35%). The DC link voltage proposed controller had a swift response consistent with the simulation results.  
Figure 10: One source current cycle and its spectrum when Load 2 was applied for the three control techniques: (**a,d**) conventional method, (**b,e**) Fuzzy type 1, and (**c,f**) the proposed method.  
Figure 9: Step change from Load 1 to Load 2 and back to Load 1 for the three control techniques: (**a**) DC link voltage, (**b**) source current, (**c**) one cycle for the source voltage and current with conventional method, (**d**) one cycle for the source voltage and current with Fuzzy type 1 method, and (**e**) one cycle for the source voltage and current with the proposed method.  
Figure 11: The proposed system’s experimental setup: (**a**) hardware implementation, and (**b**) Simulink model for controlling the DS1104 board.  
Figure 12: Step change in the dc link voltage from 400 to 600 V.  
In Case (2), the system was tested when the ac load was stepped from 0% to 100% and then returned to 0% from the full load value. Figure 13 shows the source current and DC link voltage. Figure 14 depicts the source voltage and current during step changes in the AC load side. The source voltage and current are shown in Figure 14a during 100% AC load, Figure 14b during switching from 0 to 100% AC load at t = 0.767 s, and Figure 14c during switching from 100 to 0% AC load at t = 2.417 s. Figure 15 shows that the displacement angle between the source voltage and current was less than 8\({}^{\circ}\), resulting in a displacement factor of about 0.990 when applying a 100% AC load. Figure 16 depicts the source current spectrum when applying a 100% AC load. The THD was 3.11%, which made the power factor 0.972. Case (2) outcomes will adhere to the simulation outcomes presented in Table 4.  
Figure 14: The source voltage and current during step changes in the ac load side: **(a)** during 100% load, **(b)** from 0 to 100% load, and **(c)** from 100 to 0% load. The black line is the voltage and the red line is the current.  
Figure 13: The source current and the DC link voltage during a step change in the ac loads from 0% to 100%.
# Advancing Digital Twin-Based Collision Avoidance:
## 4 Functional Safety for Wireless Systems
### Modeling Outage Frequency for Wireless Systems
#### 4.3.2 SGM for \(PFH_{D}\)  
A common use of a safety functions is the direct transmission of data between local machines without routing it via the internet. For packet loss on the access link, the SGM model has proven to be accurate [22]. Using it, we can calculate \(p\) and \(q\) from measurements for the SGM, given \(m_{k}\) represents the amount of observed loss events of length \(k\).  
Figure 9: Reachable performance level with 1% packet loss for various packet transmission rates \(f\) and times to dangerous with outage \(T\), assuming a uniform distribution. This model disregards the bursty behavior of packet loss and is only recommended for nonbursty loss of data.  
\[p=\frac{\sum_{k=1}^{n-1}m_{k}}{m_{0}}\]  
\[q=1-\frac{\sum_{k=2}^{n-1}m_{k}\cdot(k-1)}{\sum_{k=1}^{n-1}m_{k}\cdot k}\]  
For our data set from Section 3, this results in \(p=0.044,q=0.895\), which can also be written as  
\[\text{SGM}=\begin{bmatrix}0.956&0.044\\ 0.895&0.105\end{bmatrix}\]  
Furthermore, with \(p_{k}=(1-q)^{k-1}\cdot q\), given \(k=T\cdot f\), we receive the loss distribution in Table 5. We then substitute \(p\) and \(k\) in Equation (6) with \(1-q\) and \(T\cdot f\) and normalize it to events/hour.  
\[PFH_{D}=(1-q)^{(T\cdot f-1)}\cdot q\cdot f\cdot 3600\,\text{s} \tag{7}\]  
In Figure 10, we see the effect of the packet transmission frequency \(f\) and the critical outage duration \(T\) on the reachable performance level if we apply the SGM to our measurements. We see that given our own case, where we consider the limit for outages at \(T=1\,\text{s}\), we need \(T=10\,\text{Hz}\) for performance level A and \(T=12\,\text{Hz}\) for D.
## 2 Sequential Modelling Strategy
### Contact Dynamics
#### 2.3.2 Normal and Friction Contact Force  
Given the description of the contact detection algorithm, it is essential to describe the methodology required to compute the normal and tangential components of the contact response force. Any impact of the net with the space debris wall must first be seen as \(N\)-spherical net node collisions with an infinite plane. Each net node has a radius \(R_{i}\) (computed as half the thread diameter).  
The normal force is computed using Hertzian theory [21], in which an impact between two bodies is modelled as the compression of a (non-linear) spring.  
The relationship between the normal force and contact algorithm parameters is as follows:  
\[F_{n}=k_{n}\ \delta^{n}+d_{n}\ \delta\ \delta^{n}. \tag{6}\]  
where \(k_{n}\) and \(d_{n}\) are the stiffness and damping coefficients, \(\delta\) and \(\delta\) are the penetration depth and rate of change, and \(n\) is a material-dependent constant usually chosen as 1.5 [15; 23]. The methodology used to compute these parameters can be found summarised in [10], where \(d_{n}\) is obtained using the Hunt-Crossley model [21].  
Given the normal force, the friction force \(\mathbf{F}_{t}\) can be directly found using [PERSON]'s model [15]:  
\[F_{t}=\begin{cases}\left[\mu_{k}+\frac{2(\mu_{s}-\mu_{k})}{1+\left(\frac{\psi _{t}}{\psi_{t,0}}\right)^{2}}\right]F_{n},&\text{for $V_{t}\geq V_{t,0}$}\\ \\ \left(\frac{V_{t}}{V_{t,0}}\right)\ \left[\mu_{k}+\frac{2(\mu_{s}-\mu_{k})}{1+ \left(\frac{V_{t}}{V_{t,0}}\right)^{2}}\right]F_{n},&\text{for $V_{t}<V_{t,0}$} \end{cases} \tag{7}\]  
\[\mathbf{F}_{t}=-F_{t}\hat{\mathbf{t}}, \tag{8}\]  
Figure 3: Level 1 to 2 contact bounding boxes of a generic simulation scenario for both net (red dots represent the bullets) and target systems.  
where \(\hat{\mathbf{i}}\) is the tangential unit vector, \(\mu_{s}\) and \(\mu_{k}\) are the static and kinetic friction coefficients, \(V_{t,0}\) is the transition velocity from static to dynamic friction and \(V_{t}\) is the tangential velocity (obtained using the methodology described in [15]).
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 4 Challenges in Indoor Localization Systems
### Dynamic Environments  
Dynamic environments, such as busy hospitals, factories, or military zones, introduce unpredictable changes that can affect localization accuracy. BLE and ZigBee systems in industrial settings must cope with the constant movement of people, machinery, and goods, which can disrupt signal propagation [60]. Adaptive algorithms and sensor fusion techniques help maintain localization reliability despite these changes [36].
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 1 Introduction
### Contributions and Organization of the Paper  
The proposed hybrid algorithm aims to navigate the most acceptable flight route in a complex environment without colliding with environmental elements by combining the PRM [26] and ABC [27] algorithms. Besides this, optimization of the different environments is considered to achieve the best possible path. PRM and ABC's hybridization and optimization have not yet been tested to the best of our knowledge. However, a hybrid of PRM and ABC provides a better convergence rate and accuracy than other algorithms. In the proposed HPP, the PRM roadmap is used as an input by the ABC algorithm, where a swarm of bees (particles) coordinates with different environmental constraints to obtain the shortest and safest path.  
The key contributions of our study are as follows:  
* We incorporate a UAV as a communication network facilitator for efficient data gathering from WSNs in emergency environments. The UAV uses directional antennas and comprises two wireless transceivers operating concurrently for timely and energy-efficient UAV to cluster head (CH) communication;
* The proposed model uses an energy-efficient clustering technique which plays a significant role in reducing delay and energy of UWSNs;
* The proposed hybrid path planning algorithm uses the PRM to obtain the shortest and collision-free roadmap, followed by the modified ABC, to dynamically find the position coordinates if some threats or obstacles arise during the flight time;
* The proposed HPP algorithm enhances the wake-up schedule of CHs in the target WSN with the UAV's path to minimize the energy consumption of the CHs, ensuring that data are collected efficiently and fairly from the ground sensors;
* The UAV path is optimized to avoid static and dynamic obstacles, prohibited areas, and different environmental constraints (such as wind speed, atmospheric irregularities, and wind pressure). The UAV's height and velocity are optimized to minimize the UAV flight time and the sensors' energy consumption. The turning angle of UAV is also considered to obtain a smoother path;
* Our performance study based on MATLAB simulations shows that the proposed hybrid path planning algorithm outperforms the conventional algorithms in terms of flight time, energy usage, accuracy, and safety. The convergence time of the proposed algorithm is also compared with some other popular path planning algorithms.  
Figure 1: Example of UAV path planning in a UWSN.  
The remainder of this paper is organized as follows. The existing UAV path planning techniques are reviewed in the following section. In Section 3, two typical UAV path planning algorithms are investigated, and a network model is presented. In Section 4, the proposed hybrid path planning algorithm is presented with different optimization constraints. In Section 5, the proposed algorithm's performance is evaluated via computer simulation and compared with the PRM and ABC algorithms. Finally, the conclusions and future works are presented in Section 6.
# IoT Services for Monitoring Food Supply Chains
## 3 Methodology and Implementation
### Data Collection
collected from these locations is directly integrated into the platform's visualisation and alerting system. This integration enables continuous monitoring of product conditions during transportation and storage. Data from the selected locations was used to validate the system's ability to detect deviations, ensure cold chain compliance with clients' requirements, and provide services. Figure 4: GP Outdoor LoRa Gateway. For instance, in the context of agricultural operations, when a sensor node detects low humidity levels in a crop field, the system can automatically trigger an electric irrigation valve to activate, ensuring timely watering of the field [30]. This helps users quickly identify the geographic distribution of sensors. The accompanying table displays numerical values for average humidity and air temperature and, as secondary information, shows the sensor's battery status. Temperature readings range from \(-1\)\({}^{\circ}\)C to 22.86 \({}^{\circ}\)C, while humidity varies between 0% and 100%, with intermediate values such as 92.29% and 99%. While the table allows for numerical comparison, it lacks an intuitive visual representation of trends. Enhancing visualisation with heatmaps, colour-coded indicators, or interactive charts can improve data interpretation. Implementing time-series graphs or dynamic tooltips on the map would further enrich the user experience, making it easier to analyse variations over time. In conclusion, the data gathered serves as a fundamental building block for our operations, as it can be accessed and analysed in real time through the platform. This immediacy, combined with the high reliability of the data, plays a crucial role in effectively supervising the perishable products being monitored. It not only aids in ensuring that these products are kept in optimal conditions but also supports informed decision-making and facilitates in-depth analysis of the information collected.
## 3 Results and Discussion
### Effect of pH on Adsorption  
The effect of pH on the adsorption of ME and BPA (40 mg L\({}^{-1}\)) is presented in Figure 5. All the tested phenols have similar values of p\(K_{\rm a}\) (9.8 \(\pm\) 0.4, Table S2). It was found that \(q_{\rm e}\) was almost independent from the pH within the value range from 3 to p\(K_{\rm a}\), but significantly decreased when pH \(>\) p\(K_{\rm a}\). The phenomena were explained by the change of adsorption force. When pH \(<\) p\(K_{\rm a}\), the \(\pi\)-\(\pi\) stacking between phenols and graphene was the main force and pH was not the major parameter influencing the \(\pi\)-\(\pi\) interaction. When pH \(>\) p\(K_{\rm a}\), phenols dissociate and form negatively charged anions. Meanwhile, we monitored the zeta potentials of the adsorbents in aqueous dispersion as a function of pH (Figure S2), and found that all the samples had negative zeta potentials at pH values ranging from 5 to 11. That means that the surface of samples had a negative charge. Therefore, the electrostatic repulsion force was the main force at the range of pH \(>\) p\(K_{\rm a}\) and against the \(\pi\)-\(\pi\) interaction to influence the adsorption for phenols.  
\begin{table}
\begin{tabular}{c c c c} \hline
**adsorbent** & \
\begin{tabular}{c} **Phenolic Initial** \\ **Concentration (mg L\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Adsorption Capacity** \\ **(mg g\({}^{-1}\))** \\ \end{tabular} & **Reference** \\ \hline GO/RGO & 40 (PE) & 25/45 & Our work \\ GO/RGO & 40 (BPA) & 91/355 & Our work \\ surfactant-modified zeolite & 100 (BPA) & 60 & [31] \\ \(\beta\)-Cyclodextrin hydroxypropyl & 99.54 (BPA) & 8 & [32] \\ methylcellulose hydrogels & 300 (BPA) & 125 & [33] \\ CTAB-modified graphite & 1000 (PE) & 90 & [34] \\ Thermal modified activated carbon & 103 (PE) & 14 & [35] \\ biochar & 50 (PE) & 28 & [36] \\ \hline \end{tabular}
\end{table}
Table 1: Adsorption capacity of phenol (PE) and bisphenol A (BPA) in the different systems.  
Figure 5: Variations of adsorption capacity of (**a**) ME and (**b**) BPA on (1) GO and (2) RGO as a function of pH.