esa-sceva/satcom-chunk-collection huggingface dataset:
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 5 Experimental Results and Discussions
### Signal Detection and Classification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RF** & **SVM** & **KNN** & **LR** & **DT** & **Extra Trees** \\ \hline \multirow{3}{*}{**N**} & **Precision** & 96.11\% & 95.26\% & 96.64\% & 94.73\% & 95.75\% & 96.17\% \\  & **Recall** & 98.27\% & 98.38\% & 96.68\% & 98.27\% & 94.73\% & 98.63\% \\  & **Fl-score** & 97.19\% & 96.79\% & 96.66\% & 96.47\% & 95.23\% & 97.38\% \\ \hline \multirow{3}{*}{**ABN**} & **Precision** & 83.33\% & 82.06\% & 72.98\% & 79.87\% & 60.61\% & 85.96\% \\  & **Recall** & 70.31\% & 60.30\% & 72.73\% & 55.60\% & 65.85\% & 68.13\% \\ \cline{1-1}  & **Fl-score** & 76.27\% & 69.51\% & 72.86\% & 65.56\% & 63.12\% & 77.03\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance report obtained from the six classification techniques for normal and abnormal ECG signals. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Feature** & **Definition** \\ \hline
1 & RR0/avgRR & The current R-R interval divided by the average of the last 32 beats \\
2 & RR+1/RR0 & The next R-R interval divided by the current R-R interval \\
3 & RR-1/RR0 & The previous R-R interval divided by the current R-R interval \\
4 & RR+1/avgRR & The next R-R interval divided by the average of the last 32 beats \\
5 & hbf\_3 & The coefficients of fitting Hermite basis functions with polynomials degree = 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The top 5 informative features used to classify the ECG signals. Figure 7: (**a**) Accuracy of Extra Trees classifiers with a varying number of the top 10 mutual information ranked features; and (**b**) SMS message by Twilio sent to the healthcare provider to alert of any abnormal heartbeats. We deploy the classification module on a Raspberry Pi 3 B+ board acting as an edge device. MQTT is the underlying communication protocol between our mobile application and the edge device. The mobile application is designed to publish the ECG data in patches every second. This is another system design decision that can be changed to alternative options with 5 or 10 s intervals. Therefore, the integration of the MQTT protocol provided a pipeline for our mobile application to publish the ECG signals collected by the ECG patch in 0.12 s. The edge device continuously receives ECG data until an abnormal heartbeat is detected; the system simultaneously sends the signal to different services to alert caregivers and/or healthcare providers. Figure 7b shows a screenshot of an SMS message sent to alert the healthcare provider of irregular heartbeats as part of the notification service provided by the proposed platform. The average processing time for the ECG signal detection is 0.29 s. If an abnormal heart condition is detected, a message is sent out immediately to caregivers in a range of 0.57 to 0.77 s, which is quick enough for healthcare providers to take necessary actions.
# Variable Responses to a Marine Heat Wave in Five Fringing Reefs of Southern Taiwan
## 3 Results  
### Temperature  
Based on a trailing MMM of \(\sim\)29 \({}^{\circ}\)C, NOAA's satellite data-based estimates calculated 16 DHWs for Southern Taiwan's reefs in 2020 (Figure 2 & Table 2). This value agrees well with DHW calculations made from our at-depth _in situ_ temperature data (Figure 3A-E and Figure S1) for the four shallow reefs (DHWs ranging from 16-20); however, the seawater was significantly cooler at 7 m (ILO), and this site experienced only 2 DHWs in 2020 (Table 2). Although ILO was not characterized by the lowest MMM (Table 2), it was the coolest site overall (annual mean = 25.9 \({}^{\circ}\)C), and the temperature never surpassed the empirically derived local coral bleaching threshold of 31.5 \({}^{\circ}\)C determined previously [32; 33]. The MMM of the OL, 29.3 \({}^{\circ}\)C, was significantly higher than that of the other reefs; as such, although the DHWs of the four shallow, fringing reefs were similar (Table 2), the actual degree of thermal dosing at OL was far higher due to the regular inundation with warm effluent water from the nearby nuclear power plant [36; 37]. Corals of the OL were exposed to temperatures \(>\)30 \({}^{\circ}\)C for \(\sim\)78 days across a \(\sim\)six-month period in 2020, and the temperature even reached 35 \({}^{\circ}\)C for several hours (Table 2); there were 72 days in 2020 in which the mean temperature was above 30 \({}^{\circ}\)C. In fact, all shallow reefs were immersed in seawater \(>\)30 \({}^{\circ}\)C for at least 25 days over the June-October 2020 period; this degree of thermal dosing was clearly sufficient to induce mass coral bleaching at all but the deeper reef.
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 5 Numerical Experiments
### Experiment Settings
#### 5.1.2 Performance Metrics  
The hypervolume (HV) and the inverse generation distance (IGD) are metrics that comprehensively measure convergence and diversity, making them appropriate for comparing the algorithms in this study. It is important to note that the objective values of each comparison algorithm are normalized, as the two objectives have different scales [48].  
The HV metric measures the volume of the objective space that is dominated by the solution set obtained from an optimization algorithm, and the reference point (1, 1) is selected [49]. A larger HV value indicates better overall performance of the algorithm. Let \(P\) represent the Pareto front obtained by the algorithm, and the HV value for \(P\) can be calculated by \(\text{HV}(P)~{}=~{}\delta(\cup_{i~{}=~{}1}^{|P|}v_{i})\), where \(\delta\) is the Lebesgue measure used to calculate volume, \(|P|\) denotes the number of non-dominated solution sets, and \(v_{i}\) represents the hypervolume constituted by the reference point and the \(i\) solution in the solution set.  
IGD metric measures the average distance from the true Pareto frontier to \(P\). A smaller IGD value indicates better overall performance of the algorithm [50]. Since the true Pareto frontier, \(P^{*}\), is unknown in advance for these instances, it is approximated here by all non-dominated solutions obtained from different algorithms, with each independently repeated 10 times per instance. Therefore, the IGD value of \(P\) can be calculated by \(\text{IGD}(P,P^{*})~{}=~{}\sum_{x\in P^{*}}d(x,P)/|P^{*}|\), where \(d(x,P)\) denotes the Euclidean distance from the solution \(x\) to the set of nondominated solutions \(P\).
# A Study on the Stoichiometry of Casein/Chitosan Gel Complexes as a Delivery System for Quercetin
## 2 Materials and Methods
### Methods  
#### 2.2.1 Preparation of Casein/Chitosan Nanocomplexes  
Casein/chitosan complexes were formed by using ionotropic gelation based on the attractive electrostatic interactions between the oppositely charged polyelectrolytes. First, 0.5% w/v of water solution of casein (pH = 7) and 0.5% \(w/v\) solution of chitosan in 3% acetic acid were left to stir during the night to achieve complete solubility. Prior to the mixing step, the pH value of the solution of chitosan was increased to 5 by 5 M NaOH. The complex formation occurred after mixing different volumes of polyelectrolyte solutions, thereby achieving theoretical stoichiometric ratios between their charged groups, COO\({}^{-}\)/NH\({}_{3}\)\({}^{+}\) (namely, 1:1, 2:1, 4:1, and 6:1 in favour of casein). The total amount of the reaction mixture is always constant and equal to 20 mL. Immediately after the mixing step, the pH of the resultant solution was adjusted to pH = 6 and left to stir for 15 min at room temperature and 1500 rpm. After this, the solutions were ultrasonicated in an ice bath at an amplitude of 90% for 20 min using the UP100H Compact Ultrasonic Laboratory Device (Hielscher Ultrasonics GmbH, Teltow, Germany). The same preparation protocol was followed for the quercetin-loaded particles. First, 50 mg of quercetin was dissolved in 3.5 mL of ethanol and added to the chitosan solution before the complex formation step. The obtained structures were precipitated at 14,000 rpm for 15 min and lyophilized for 72 h at 10 Pa pressure and a temperature of \(-\)50 \({}^{\circ}\)C. The final dry mass was sealed in containers and stored at controlled humid conditions (RH 54%) at room temperature for further applications.
## 3 Results
### Temperature Dependence of the Intentionally Hydrated Sample  
The temperature dependence of the infrared spectrum of PYR\({}_{14}\)-DCA + H\({}_{2}\)O is reported in Figure 6. As in the case of PYR\({}_{14}\)-DCA, in PYR\({}_{14}\)-DCA + H\({}_{2}\)O the spectra measured at the highest (300 K) and lowest (140 K) temperature display the same bands, without any splitting. Only in the region between 3200 and 3600 cm\({}^{-1}\) there is an increase of the intensity of the bands centered around 3270 and 3400 cm\({}^{-1}\) at low Ts. On increasing temperature, the bands smoothly recover the shape they had at room temperature, before the thermal cycle. This trend suggests that in PYR\({}_{14}\)-DCA + H\({}_{2}\)O no crystallization takes place as a function of temperature, rather suggesting that a glass transition occurs at very low temperatures.  
To gain a better description of the interaction of the ionic liquid with a large amount of water, the analysis of the same vibrational lines considered for PYR\({}_{14}\)-DCA was repeated for the PYR\({}_{14}\)-DCA + H\({}_{2}\)O sample. The best fit parameters of the investigated five bands are reported in Figure 7. Both bands due to the DCA vibrations show a decrease of both frequency and intensity as the temperature increases, while the width of those bands stay almost constant. Concerning the OH stretching bands, the two absorptions centered at \(\sim\)3400 and 3480 cm\({}^{-1}\) remain practically at the same frequencies in the whole temperature range, while their intensity decreases as \(T\) increases. A different behavior is shown by the band around 3270 cm\({}^{-1}\): the frequency increases as \(T\) increases; its intensity stays almost constant up to 210 K, significantly decreases between 220 and 240 K and stays constant for higher temperatures.  
Figure 6: Temperature dependence of the spectra of PYR\({}_{14}\)-DCA + H\({}_{2}\)O measured on heating from 140 up to 300 K in different frequency regions (700–1550 and 2050–2300 cm\({}^{-1}\) in the left panel and 2800–3700 cm\({}^{-1}\) in the right panel).
## 2 CFD Modelling
### Mesh Creation and Sensitivity Testing  
According to the CFD code utilized in the current study, the mesh motion approach is applied for the transient analysis of the fan. Unstructured mesh domains, which are connected yet have overlapping interface zones, are needed for this technique. A mesh sensitivity study is conducted to check that the number of control cells utilized to mesh the fluid zone does not affect the numerical results. The mesh is initially created using large-sized element lengths equivalent to 42.3 lakh control cells, with the flexibility for localized smoothing for regions with high gradients, such as the border layer, wherever applicable in the zone. Regional mesh refinement is used to iteratively enhance the mesh with a steady decrease in cell element lengths to build finely meshed regions that correspond to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Impeller:** & : 0.6 D \\ Radius at the inlet & : D \\ Radius at the exit & : 30\({}^{\circ}\) \\ Inlet blade angle & : 76\({}^{\circ}\) \\ Number of blades & : 13 \\ \hline
**Diffuser:** & \\ Diameter of the inlet & : 2.3 D \\ Diameter of the exit & : 3 D \\ Blade angle at the inlet & : 23 deg \\ Blade angle at the outlet & : 38 deg \\ Number of diffuser vanes & : 13 \\ \hline
**Volute casing:** & \\ Height of the channel & : 0.45 D \\ Flange width & : 2.25 D \\ \hline Blade thickness & : 0.025 D \\ \hline Impeller and diffuser passage width & : 0.175 D \\ \hline The meridional gap between the diffuser and impeller & : 0.15 D \\ \hline Rated RPM of the fan & : 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric specifications of the hub-less base model.  
Figure 1: Centrifugal fan schematic drawing.  
99.9 lakh cells. It can be demonstrated that there is almost convergence for the mesh density trials for the 68.9 and 99.9 lakh control volume cells. As a result, a mesh size of 68.9 lakh control volume cells was chosen for further numerical analysis [17]. This ensures that the memory storage and computation time required for the analysis are kept to a minimum. The distribution of the total control volume cells from the aforementioned investigation among the flow domains is shown in Table 2.
# Distinction Between Interturn Short-Circuit Faults and Unbalanced Load in Transformers
## 3 Materials and Methods  
In this work, experiments were conducted using a purpose-built platform designed for fault detection in a three-phase transformer bank, which consisted of three single-phase dry-type transformers rated at 120 VA, 240/24 V, and 60 Hz. This setup offered flexibility to intervene individually in each phase, inject faults in a controlled manner, and observe the electrical and thermal behaviors for each transformer [19]. One of these transformers was modified to intentionally induce incipient interturn short-circuit faults with magnitudes of 2 and 4 short-circuited turns, as well as a 4-turn imbalance in the supply. Four transformer conditions were evaluated to analyze the differences between a supply imbalance and an incipient interturn short circuit: a healthy transformer (no fault), a transformer with a 2-turn short circuit, a transformer with a 4-turn short circuit, and a healthy transformer with a 4-turn imbalance.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Standardization and Technology of NB-IoT
#### 2.3.4. NB-IoT Applications in Industries  
The application of NB-IIoT spans many of the industrial sectors with varying degrees of deployments [20]. This usually depends on the criticality and level of confidence in NB-IoT technology. However, its application has been forecast to increase in the foreseeable future especially with the increase in the manufacturing of NB-IoT modules by various vendors [2]. NB-IoT is deployed for different purposes in industries, as shown in Figure 13. In the oil and gas industries, NB-IoT deployment includes refining, distribution, and monitoring of products. Applications are also found in food processing, agriculture, and water industries.
## 2 Related Work
### Dynamic Interaction-Based Complexity Evaluation Methods  
Dynamic interaction-based complexity quantification methods capture the nonlinear interaction characteristics in dynamic environments by analyzing the spatiotemporal relationships between the primary vehicle and surrounding vehicles. These methods quantify local interaction complexity, often utilizing techniques like information entropy and gravitational models to describe vehicle interactions. This provides a more direct and flexible quantification approach, effectively addressing the limitations of the above methods in handling dynamic interactions and interpretability.  
For instance, [PERSON] et al. [38] quantified the drivable space of the primary vehicle using information entropy to indirectly measure decision-making complexity at intersections. [PERSON] et al. [39] evaluated the impact of dynamic interactions by measuring the gravitational influence of the primary vehicle on other vehicles. [PERSON] et al. [40] modeled dynamic traffic vehicles as charged particles, quantifying the complex interactions between the primary vehicle and surrounding vehicles through the combination and superposition of potential fields. Similar studies can be found in [30; 41].  
Although dynamic interaction-based quantification methods have shown effectiveness in local complexity assessment, their outcomes are performance-dependent on the primary vehicle, leading to a lack of objectivity and limiting their suitability as general complexity metrics. Additionally, these methods primarily focus on the local dynamic behaviors around the primary vehicle, failing to comprehensively represent the global interaction relationships within complex road structures, resulting in partial assessments. Moreover, existing complexity evaluation models are largely abstracted from expert knowledge, and this human-perceived complexity may not align with the actual perception of neural networks or autonomous driving systems. Therefore, the consistency of evaluation results with autonomous driving system performance must be verified, which is currently lacking.  
To address these limitations, this paper proposes a complexity evaluation framework that is independent of primary vehicle performance. By introducing multidimensional indicators--such as vehicle count, interaction density, disorder, and interaction risk--the framework comprehensively quantifies the global interaction complexity of intersection scenarios from a system-level perspective.
## 2 Materials and Methods
### Cell Proliferation Assay  
A 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium-bromide (MTT) assay was used to evaluate cell viability of osteoblast-like human MG63 cells attached to untreated and heat-treated Ti64 specimens after 4, 24, and 72 h of incubation. Human MG-63 osteoblast-like cells were purchased from Bioresource Collection and Research Center (product No. BCRC 60279, BCRC, Hsinchu City, Taiwan).  
MG-63 cells were cultured up to \(\approx\)60% confluence in Dulbecco's modified Eagle medium (DMEM) containing 10% heat-inactivated fetal bovine serum (FBS). Cultures of MG-63 cells were maintained at 37 \({}^{\circ}\)C in a humid atmosphere of 5% CO\({}_{2}\). MG-63 cells were trypsinized, counted, and resuspended at the appropriate densities (\(\approx\)10\({}^{5}\) cells/mL) in fresh culture medium. All of the Ti64 disks were autoclaved, rinsed with phosphate-buffered saline (PBS), and transferred to 24-well tissue culture plates. Prior to cell seeding, specimens were equilibrated in culture medium for 10 min. Subsequently, MG-63 cells were drop-seeded onto substrates at a density of 5 \(\times\) 10\({}^{4}\) cells/mL and incubated statically for at least 1 h to allow cell attachment. At specified time intervals of cultivation, namely 4, 24, and 72 h of incubation, an MTT (0.5 mg/mL) solution was added to each well, and cells were further incubated for 3 h at 37 \({}^{\circ}\)C. Supernatants were removed, and 1000 \(\mu\)L of dimethyl sulfoxide (DMSO) was added to each well to dissolve the formazan product. Then, 100 \(\mu\)L of supernatant from each well was transferred to new 96-well cell culture plates. Absorbance at 570 nm was measured using an enzyme-linked immunosorbent assay (ELISA) reader. Five parallel specimens were used to provide an average and standard deviation (sd).
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 5 Cloud Services
### Planning Services  
Mission planning in the cloud represents the highest level of path planning for autonomous inspection. The services extract the locations of the infrastructure, i.e., power towers, lines, bridges, and railways from the Open Street Map, and create a connected graph based on the data. It enables the calculation of the shortest path and the determination of the order in which drones visit inspection targets. Planning can be enhanced by providing no-fly zones, weather data, or geofences to the swarm.
## 7 Applications in SDN-based VANETs
### Traffic Accident Detection  
Based on the velocities and directions of vehicles, the information is gathered from the SDN-based VANETs. Since the accessible information is as indicated by speed and organization of each vehicle, artificial intelligence (AI) computations are utilized to break down the vehicle's conduct and anticipate the mishap that will undoubtedly occur. The Artificial Neural Network (ANN), Support Vector Machine (SVM), and arbitrary backwoods are calculations utilized on the informational index acquired to recognize the typical case from the clumsy case [12].
# TMD Damping for Structures with Uncertain Modal Parameters
## 2 Modelling
### Optimum TMD Parameters for Nominal Modal Properties  
The optimum designs of the TMD natural frequency \(f_{2}\) and the TMD damping ratio \(\zeta_{2}\) are given in the literature for various optimization objectives, e.g., [12]. For this study, \(f_{2}\) and \(\zeta_{2}\) are designed by the well-known closed-form solutions of [PERSON] and [PERSON] [3; 5], which minimize the structural displacement response for harmonic excitation, and their equations are shown as follows:  
\[f_{2}=\frac{f_{1,nom}}{1+\mu}, \tag{8}\]  
\[\zeta_{2}=\sqrt{\frac{3\mu}{8\left(\mu+1\right)^{3}}} \tag{9}\]  
where \(f_{1,nom}\) denotes the nominal eigenfrequency of the target mode, and the mass ratio \(\mu\) is computed based on the nominal modal mass \(m_{1,nom}\), see (6). Based on (8) and (9) the TMD stiffness and viscous damping coefficient become the following:  
\[k_{2}=m_{2}(2\pi f_{2})^{2}, \tag{10}\]  
\[c_{2}=2\zeta_{2}m_{2}(2\pi f_{1,nom}) \tag{11}\]  
where \(f_{1,nom}\) must be used in (11) because of the cubic power in (9) [3; 5].
## 5 Results and Discussion
### Nonlinear Trajectory Interpolation
This behavior can be interpreted since we have chosen the mean vectors, \(\mu_{k}\), to be a linear function of the condition \(y\) as defined in Section 3.3. As a result the approximation of spiral trajectory by a mixture of Gaussians with linearly-varying mean vectors. Let us note that this result not an effect of the reparameterization methodology but of how we chose to model the conditioning. In the case of unconditional data where \(y\) is not taken into consideration, the generated data are distributed uniformly on the spiral which is approximated by a mixture of \"static\" Gaussians (data not shown). We should finally note that in the case of the interpretable generator (right panels of Figure 3A,B) the number of Gaussian modes \(K\) plays an important role in the quality of the generated data. In the case of choosing a higher-than-required number of Gaussian modes to represent a given data set, the extra Gaussians become degenerate (i.e., \(p_{k}\approx 0\)). In effect, the extra Gaussians are discarded from the resulting model. On the contrary, a smaller \(K\) will result in an under-representation of the test data, covering only the main  
Figure 3: Real and generated samples of a 2-dimensional dataset of size \(N=\) 10,000 following a spiral-shaped distribution. Color indicates the value of the condition variable, \(y\in[0,1]\). (**A**) Training data (left) where \(y\) belongs to to the whole interval \([0,1]\) along with generated data using conditional FNN (middle) and conditional GMM (right) with \(K=30\) modes. (**B**) Training data (left) where \(y\) belongs to \([0,0.25]\cup[0.3,0.6]\cup[0.65,1]\). Generated data using conditional FNN (middle) and conditional GMM (right). distribution modes while missing smaller ones. In the spiral data set, this resembles a coarse polygon-shaped approximation of Gaussians (Supplementary Figure S2).